CLASS 382, IMAGE ANALYSIS |
100 | APPLICATIONS: |
This subclass is indented under the class definition. Subject matter wherein the image analysis* is disclosed
as being designed for or utilized in a diverse art device, system,
process, or environment.
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101 | . Mail processing: |
This subclass is indented under subclass 100. Subject matter wherein the image is sensed from materials,
such as letters and packages, handled in a postal system.
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102 | .. ZIP code: |
This subclass is indented under subclass 101. Subject matter wherein the image sensing is limited specifically to finding and reading a series of numbers that indicate the general location where the mail piece is to be sent (e.g., a ZIP code or postal code). | |
103 | . Target tracking or detecting: |
This subclass is indented under subclass 100. Subject matter wherein an object is located, recognized,
or followed (tracked) by an imaging system*.
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104 | . Vehicle or traffic control (e.g., auto, bus, or train): |
This subclass is indented under subclass 100. Subject matter wherein a conveyance that is, or can be,
manned is located, identified, or controlled by image analyzing
techniques.
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105 | .. License plate: |
This subclass is indented under subclass 104. Subject matter including image sensing for specifically finding and reading a series of alphanumerics from a plate on, or affixed to, a conveyance such as an automobile. | |
106 | . Range or distance measuring: |
This subclass is indented under subclass 100. Subject matter wherein the length or magnitude of a path
from a sensor or imaging system* to an object is determined.
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107 | . Motion or velocity measuring: |
This subclass is indented under subclass 100. Subject matter wherein the amount of change in position
or movement of an imaged object is determined.
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108 | . Surface texture or roughness measuring: |
This subclass is indented under subclass 100. Subject matter wherein the examined characteristics of an
imaged object include distribution of color and intensity on a surface
to give an appearance of texture, such as smooth, rough, shiny,
or dull.
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109 | . Seismic or geological sample measuring: |
This subclass is indented under subclass 100. Subject matter wherein the analyzed image is a sample of
earth or rock.
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110 | . Animal, plant, or food inspection: |
This subclass is indented under subclass 100. Subject matter wherein the examined or analyzed image is
an animal, plant, or foodstuff.
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111 | . Textiles or clothing: |
This subclass is indented under subclass 100. Subject matter wherein the image analysis* is designed
for, or used in, the inspection or production of textiles or apparel.
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112 | . Document or print quality inspection (e.g., newspaper, photographs, etc.): |
This subclass is indented under subclass 100. Subject matter wherein the accuracy or correctness of printed
pattern*s on an object under inspection is determined or
the inspection of the accuracy of reproduced pattern*s
is determined.
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113 | . Reading maps, graphs, drawings or schematics: |
This subclass is indented under subclass 100. Subject matter wherein the imaging system* is specifically
designed for reading or processing documents having s:graphical
notations or illustrations.
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114 | . Reading aids for the visually impaired: |
This subclass is indented under subclass 100. Subject matter wherein a particular representation of an
image is generated that can be interpreted by a sense other than
sight.
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115 | . Personnel identification (e.g., biometrics): |
This subclass is indented under subclass 100. Subject matter wherein an image or an image pattern* is
analyzed for the purpose of recognizing an individual or verifying
a person"s identity.
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116 | .. Using a combination of features (e.g., signature and fingerprint): |
This subclass is indented under subclass 115. Subject matter wherein more than one type of distinct identification
process is used to recognize or verify a person"s identity.
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117 | .. Using a characteristic of the eye: |
This subclass is indented under subclass 115. Subject matter wherein a person is identified by analyzing
the person"s eye or characteristics of the eye, including
retinal pattern*s and iris pattern*s.
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118 | .. Using a facial characteristic: |
This subclass is indented under subclass 115. Subject matter wherein a person is identified by analyzing
the person"s face or characteristics thereof, including
distinct features of the face like eyes, nose, mouth, etc. and spacing
of the features, as well as the face as a whole, including facial
curves and thermal energy pattern*s of the face.
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119 | .. Using a signature: |
This subclass is indented under subclass 115. Subject matter wherein a person is identified by analyzing
the pattern* of a signed name.
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120 | ... Sensing pressure together with speed or acceleration: |
This subclass is indented under subclass 119. Subject matter wherein the pressure characteristics of the writing sample are used in combination with either speed (velocity, that is, the first derivative of the values of the pen movement) or acceleration (change in velocity, that is, the second derivative of the values of the pen movement) characteristic to identify the person doing the writing. | |
121 | ... Sensing pressure only: |
This subclass is indented under subclass 119. Subject matter wherein the characteristic of the writing sample used is only pressure or force exerted by the writer with a writing instrument during the writing of the sample. | |
122 | ... Sensing speed or acceleration only: |
This subclass is indented under subclass 119. Subject matter wherein the characteristic of the writing sample used is only speed (velocity, that is, the first derivative of the values of the pen movement) or acceleration (change in velocity, that is, the second derivative of the values of the pen movement) exerted by the writer with a writing instrument during the writing of the sample. | |
123 | ... Sensing geometrical properties: |
This subclass is indented under subclass 119. Subject matter wherein the characteristics of the writing
sample used are static parameters of the writing.
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124 | .. Using a fingerprint: |
This subclass is indented under subclass 115. Subject matter wherein a person is identified by analyzing
the person"s fingerprint.
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125 | ... Extracting minutia such as ridge endings and bifurcations: |
This subclass is indented under subclass 124. Subject matter in which the finest details of a fingerprint
are measured so as to identify a person.
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126 | ... With a guiding mechanism for positioning finger: |
This subclass is indented under subclass 124. Subject matter wherein the system includes specific apparatus to place the finger to be inspected at a specific location with respect to an imaging system*. | |
127 | ... With a prism: |
This subclass is indented under subclass 124. Subject matter wherein a prism is used as part of an imaging
system* so as to acquire an image of the fingerprint for
identification.
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128 | . Biomedical applications: |
This subclass is indented under subclass 100. Subject matter wherein the image analyzing system is designed
specifically for or utilized in the areas of radiation imaging or
microscopic cell analysis for the detection or diagnosis of disease,
or any other image analyzing application substantially related to
medicine, health, or other life sciences not provided for elsewhere.
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129 | .. DNA or RNA pattern* reading: |
This subclass is indented under subclass 128. Subject matter wherein cells or cell objects are analyzed for specific parameters related to DNA, RNA, or chromosome pattern*s. | |
130 | .. Producing difference image (e.g., angiography): |
This subclass is indented under subclass 128. Subject matter wherein related images are processed (e.g., by subtraction) to produce difference images indicative of dissimilarities. | |
131 | .. Tomography (e.g., CAT scanner): |
This subclass is indented under subclass 128. Subject matter wherein a means or process is provided for
generating or processing digitized images of one or more slices
of a nominally solid object, generated by computer tomography (i.e.,
CT), magnetic resonance (i.e., MR) or ultrasonically.
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132 | .. X-ray film analysis (e.g., radiography): |
This subclass is indented under subclass 128. Subject matter related to processing standard film or digitized X-ray images (e.g., bone fractures or mammography) such as for enhancement, segmentation, tone generation. | |
133 | .. Cell analysis, classification, or counting: |
This subclass is indented under subclass 128. Subject matter related to optically viewing or digitally
storing cell images for evaluating characteristics of cell images
by chromaticity of features and feature counting.
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134 | ... Blood cells: |
This subclass is indented under subclass 133. Subject matter wherein the cell analysis, classification, or counting is directed to red, white, or other types of blood cells. | |
135 | . Reading paper currency: |
This subclass is indented under subclass 100. Subject matter including means or process that can sense
images of paper money to verify authenticity, discriminate denominations,
sense condition, or count documents, based on optical or magnetic
scanning.
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136 | . Reading coins: |
This subclass is indented under subclass 100. Subject matter wherein a means or process is provided for
inspecting a coin so as to identify the coin or determine its numismatic
condition or irregularities.
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137 | . Reading bank checks (e.g., documents bearing E-13B type characters): |
This subclass is indented under subclass 100. Subject matter including apparatus that can sense images
of a bank check and extract measurements to verify authenticity
or classify the sensed bank based on optical scanning.
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138 | .. Reading monetary amount: |
This subclass is indented under subclass 137. Subject matter wherein user-entered data corresponding to the cash value of the bank check are recognized. | |
139 | .. Reading MICR data: |
This subclass is indented under subclass 137. Subject matter related to reading preprinted magnetic ink
character or symbol data from the bank check.
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140 | ... Including an optical imager or reader: |
This subclass is indented under subclass 139. Subject matter wherein the bank check is also optically scanned for viewing or storage or for obtaining optical as well as magnetic characteristics useful in character recognition. | |
141 | . Manufacturing or product inspection: |
This subclass is indented under subclass 100. Subject matter wherein the image analysis* system
has been designed for use in product manufacturing (e.g., integrated
circuits or metal parts), including as part of automated inspection
systems for recognizing defects or irregularities or as part of
the system to control the manufacturing by image analysis*.
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142 | .. Bottle inspection: |
This subclass is indented under subclass 141. Subject matter wherein a rigid or semirigid container typically
of glass or plastic having a comparatively narrow neck or mouth
is inspected to determine the dimensions or a condition of the container.
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143 | .. Inspection of packaged consumer goods: |
This subclass is indented under subclass 141. Subject matter wherein packaged products (e.g., cigarettes)
or labels on the goods are inspected for irregularities in appearance
or other defects.
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144 | .. Mask inspection (e.g., semiconductor photomask): |
This subclass is indented under subclass 141. Subject matter wherein photomasks for semiconductor or printed circuit board fabrication are scanned for defects, holes, etc. | |
145 | .. Inspection of semiconductor device or printed circuit board: |
This subclass is indented under subclass 141. Subject matter wherein semiconductor wafers, chips, or similar
materials or an insulating board on which circuit has been printed
are inspected for defect detection, dimension checking, mark reading,
or other conditions.
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146 | ... Measuring external leads: |
This subclass is indented under subclass 145. Subject matter wherein external leads of a component are inspected, such as for coplanarity, shape, or alignment prior to insertion into a printed circuit board or the like. | |
147 | ... Inspecting printed circuit boards: |
This subclass is indented under subclass 145. Subject matter wherein a printed circuit or printed wiring
board is inspected to locate defects in conductors, holes, the presence
or absence of components, etc.
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148 | ... At plural magnifications or resolution: |
This subclass is indented under subclass 145. Subject matter wherein inspection is performed at more than one image magnification or resolution. | |
149 | ... Fault or defect detection: |
This subclass is indented under subclass 145. Subject matter wherein a device is inspected for defects relating to dimensional tolerances, surface irregularities, etc. | |
150 | .... Faulty soldering: |
This subclass is indented under subclass 149. Subject matter wherein means or process is provided for inspecting printed circuit board packages containing IC or other devices to identify defective or missing soldering or bonding points. | |
151 | ... Alignment, registration, or position determination: |
This subclass is indented under subclass 145. Subject matter wherein semiconductor or other electrical component devices are inspected to determine position or alignment with respect to a process mask or during installation. | |
152 | .. Tool, workpiece, or mechanical component inspection: |
This subclass is indented under subclass 141. Subject matter related to inspection for defects in manufactured
objects including raw sheet metal, punched, stamped, or engraved
machine parts or fasteners, or welding seams; also for inspection
of wear of tool working surfaces (e.g., drilling or cutting tools).
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153 | . Robotics: |
This subclass is indented under subclass 100. Subject matter related to autonomous vehicle navigation
via scene analysis (i.e., object recognition and avoidance) or reference
to known markings (e.g., guide lines on a warehouse floor) or to
positioning articles by automated manufacturing systems using image
analysis*.
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154 | . 3-D or stereo imaging analysis: |
This subclass is indented under subclass 100. Subject matter wherein a three-dimensional scene is imaged
using at least two cameras or camera locations for the generation
of XYZ coordinate data of any object within the scene.
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155 | LEARNING SYSTEMS: |
This subclass is indented under the class definition. Subject matter where the image analysis* system
is not rigidly structured but is adaptive and capable of changing
during a test or training period and/or according to experience gained.
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156 | . Neural networks: |
This subclass is indented under subclass 155. Subject matter in which the learning system comprises multiple
layers of interconnected neurons.
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157 | .. Network learning techniques (e.g., back propagation): |
This subclass is indented under subclass 156. Subject matter which includes details of how connections
to individual neurons are weighted.
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158 | .. Network structures: |
This subclass is indented under subclass 156. Subject matter which includes structural features of the
network such as number of layers, number of neurons per layer, long
and short term weights, and neuron construction.
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159 | . Trainable classifiers or pattern* recognizers (e.g., adaline, perceptron): |
This subclass is indented under subclass 155. Subject matter wherein the learning system compares unknown
input pattern*s to reference pattern*s, the reference
pattern*s being generated through a series of training
steps.
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160 | .. Generating a standard by statistical analysis: |
This subclass is indented under subclass 159. Subject matter wherein means or process is provided for creating the reference pattern*s based on probability or frequency of occurrence of data within the training pattern*s. | |
161 | .. Alphanumerics: |
This subclass is indented under subclass 159. Subject matter wherein the input pattern*s classified or recognized are alphanumeric symbols. | |
162 | COLOR IMAGE PROCESSING: |
This subclass is indented under the class definition. Subject matter wherein the image analysis* is specifically
adapted for color images represented in various color spaces.
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163 | . Drop-out color in image (i.e., color to be removed): |
This subclass is indented under subclass 162. Subject matter wherein an object to be imaged includes colors
which are either (a) detected and removed from the image of the
object or (b) ignored or not detected by the imaging system*.
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164 | . Image segmentation using color: |
This subclass is indented under subclass 162. Subject matter wherein regions of the image are discriminated
and separated based on color features, including locating areas
on an original document that are encircled by a color marker.
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165 | . Pattern* recognition* or classification using color: |
This subclass is indented under subclass 162. Subject matter which includes selecting and measuring color features to be used in recognizing a pattern*, structure, or object. | |
166 | . Compression of color images: |
This subclass is indented under subclass 162. Subject matter wherein the quantity of data used to represent
a color image is reduced without loss of essential information.
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167 | . Color correction: |
This subclass is indented under subclass 162. Subject matter wherein signals representative of the colors
in the image are modified to achieve improvements such as gamma
correction, gradation correction, color balancing, contrast enhancement,
and noise reduction.
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168 | HISTOGRAM PROCESSING: |
This subclass is indented under the class definition. Subject matter wherein a representation of the frequency of occurrence of image intensity or image features is used to derive properties of the image, to locate pattern*s within the image, or to otherwise process the image. | |
169 | . With a gray level transformation (e.g., uniform density transformation): |
This subclass is indented under subclass 168. Subject matter wherein the histogram is manipulated to achieve
gray level transformations including histogram equalization, histogram normalization,
contrast enhancement, and tone scale transformations.
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170 | . With pattern* recognition* or classification: |
This subclass is indented under subclass 168. Subject matter wherein the histograms of unknown input pattern*s are compared to histograms of known pattern*s in order to determine the identity of the unknown pattern*. | |
171 | . For segmenting an image: |
This subclass is indented under subclass 168. Subject matter where the histogram is processed in order
to separate distinct regions in an image including distinguishing
text and s:graphics areas and isolating lines and characters.
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172 | . For setting a threshold: |
This subclass is indented under subclass 168. Subject matter where the histogram is analyzed in order
to set one or multiple thresholds including methods based on locating
histogram peaks and valleys.
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173 | IMAGE SEGMENTATION: |
This subclass is indented under the class definition. Subject matter wherein operations are carried out on an
image so that certain meaningful regions of pattern*s of
interest, as defined by an observer, are distinguishable from other
regions or pattern*s.
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174 | . Using projections (i.e., shadow or profile of characters): |
This subclass is indented under subclass 173. Subject matter wherein a profile, indicating a sum or count of image elements along one dimension of the image, or a shadow, indicating the existence of an image element along one direction, is used to isolate identifiable regions. | |
175 | . Separating document regions using preprinted guides or markings: |
This subclass is indented under subclass 173. Subject matter wherein machine-printed marks (such as control
marks, rectangular frames, boundary lines, or identification codes)
or hand-printed marks on an original document are used to identify
distinguishable regions of interest.
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176 | . Distinguishing text from other regions: |
This subclass is indented under subclass 173. Subject matter including means or process for identifying
regions of text from other regions on a document.
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177 | . Segmenting individual characters or words: |
This subclass is indented under subclass 173. Subject matter wherein individual characters and words are isolated by determining the position of the characters or words, often including determining the coordinates of a bounding box circumscribing the character or word. | |
178 | .. Separating touching or overlapping characters: |
This subclass is indented under subclass 177. Subject matter wherein separating characters that are run together includes processes such as dekerning, predicting separation lines, and comparing possible segmented objects to library objects. | |
179 | .. Segmenting hand-printed characters: |
This subclass is indented under subclass 177. Subject matter wherein the characters to be isolated are handwritten. | |
180 | . Region labelling (e.g., page description language): |
This subclass is indented under subclass 173. Subject matter which includes assigning new or merging existing object labels to derive a set of connected components in the image or establishing relationships between regions on a page and storing the relationship. | |
181 | PATTERN* RECOGNITION*: |
This subclass is indented under the class definition. Subject matter wherein the image analyzing system possesses a further capability of identifying discrete pattern*s (such as alphanumeric characters) viewed within a scene or image; or of assigning pattern*s to appropriate categories as determined by resident categorization rules. | |
182 | . Limited to specially coded, human-readable characters: |
This subclass is indented under subclass 181. Subject matter wherein the pattern* recognition* unit
is designed specifically to read a special alphabet of highly stylized
letters or numbers that have incorporated into their form a machine-readable
code.
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183 | .. Characters formed entirely of parallel bars (e.g., CMC-7): |
This subclass is indented under subclass 182. Subject matter wherein the machine-readable, human language
symbols are constructed entirely of spaced-apart, substantially
parallel bars, lines, or strokes.
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184 | .. With separate timing or alignment marks: |
This subclass is indented under subclass 182. Subject matter further requiring that machine-readable indicia are used for alignment or timing purposes during scanning. | |
185 | . Ideos:graphic characters (e.g., Japanese or Chinese): |
This subclass is indented under subclass 181. Subject matter wherein the pattern*s to be recognized comprise ideographic or pictos:graphic symbols such as, for example, kanji (Chinese characters), kana (Japanese phonetic alphabets), or hangul (Korean characters). | |
186 | . Unconstrained handwriting (e.g., cursive): |
This subclass is indented under subclass 181. Subject matter wherein the pattern*s to be recognized comprise handwritten characters which do not conform to a particular form or style, such as continuous cursive script. | |
187 | . On-line recognition of handwritten characters: |
This subclass is indented under subclass 181. Subject matter further requiring that: (a) a signal be produced as a character or symbol is being formed by hand, (b) the signal be suitable for processing by a pattern* recognition* device, and (c) the pattern* recognition* device receives the signal as the character or symbol is being formed. | |
188 | .. Writing on ordinary surface (i.e., electronics are in pen): |
This subclass is indented under subclass 187. Subject matter wherein the recognition system utilizes a pen which includes means to output a signal indicative of the motion, direction of travel, pressure, speed, or acceleration, for example, of the pen. | |
189 | .. With a display: |
This subclass is indented under subclass 187. Subject matter wherein the recognition unit includes a means for displaying inputted characters, results of recognition, or information relevant to processing. | |
190 | . Feature extraction: |
This subclass is indented under subclass 181. Subject matter which includes the process of selecting and
measuring pieces of information, such as size, shape, texture, or
position, to be used in recognizing a pattern*, structure,
or object.
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191 | .. Multispectral features (e.g., frequency, phase): |
This subclass is indented under subclass 190. Subject matter wherein the frequency or phase of an electromagnetic
spectrum of the pattern* is used to recognize the pattern*.
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192 | .. Feature counting: |
This subclass is indented under subclass 190. Subject matter in which characteristics are extracted from
a pattern* by any of the following methods: (a) Counting
lines or points of intersection between the pattern* and
either a generally two-dimensional raster or any array of scanning
elements to derive count values that may be used either alone or
in combination with other feature data to identify the pattern*; (b)
computing, from a digital image of the pattern*, the relative
frequency of occurrence of specific pattern*s in the image;
or (c) counting any other property of the pattern*s to
be recognized in order to facilitate recognition.
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193 | ... Counting intersections of scanning lines with pattern*: |
This subclass is indented under subclass 192. Subject matter wherein the distinguishing features counted are the points or locations at which scanning lines intersect portions of a pattern*. | |
194 | ... Counting individual pixel*s or pixel* pattern*s: |
This subclass is indented under subclass 192. Subject matter wherein the distinguishing features counted are pixel*s; for example, the total number of pixel*s, the number of pixel*s in an area or window, pattern*s of pixel*, etc. | |
195 | .. Local or regional features: |
This subclass is indented under subclass 190. Subject matter which includes analyzing the content of partial areas and elementary regions of a pattern* to produce what are, in essence, simpler subpattern*s or component parts of the original pattern*. | |
196 | ... Slice codes: |
This subclass is indented under subclass 195. Subject matter wherein the pattern* to be recognized
is intersected by one or more scan lines, and each scan line is
individually encoded according to the particular configuration of
pattern* and background elements sampled along the line.
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197 | ... Directional codes and vectors (e.g., Freeman chains, compasslike codes): |
This subclass is indented under subclass 195. Subject matter wherein the outline of a pattern* is encoded as a connected sequence or chain of feature signals representing headings or points of the compass. | |
198 | .... Extracted from alphanumeric characters: |
This subclass is indented under subclass 197. Subject matter wherein the directional features or vectors are extracted from letters, numerals, or other human language symbols. | |
199 | ... Pattern* boundary and edge measurements: |
This subclass is indented under subclass 195. Subject matter wherein local features are extracted specifically
in areas of transition between the pattern* and the background, thereby
producing so-called transition, edge, or boundary signals.
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200 | .... Measurements made on alphanumeric characters: |
This subclass is indented under subclass 199. Subject matter wherein the boundary or edge measurements are made on letters, numerals, or other human language symbols. | |
201 | ... Point features (e.g., spatial coordinate descriptors): |
This subclass is indented under subclass 195. Subject matter including any of the following: an image
is sampled at only a few key locations to determine whether essential
points in a pattern* are present at those locations; every measurement
on the image results in a set of values representing spatial coordinates
only; or each of the features sought within a pattern* can
be defined by a specific point.
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202 | ... Linear stroke analysis (e.g., limited to straight lines): |
This subclass is indented under subclass 195. Subject matter wherein the only local features ever measured
for recognition purposes are the straight-line strokes in a pattern*.
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203 | ... Shape and form analysis: |
This subclass is indented under subclass 195. Subject matter wherein the local features extracted for recognition processing include empirically derived character components (i.e., curves, bays, loops, convex arcs, etc.), geometrical configurations (i.e., rectangles, circles, triangles, parabolas, etc.), or fundamental space measurements (i.e., distance, area, circumference, ratio of perimeter to area, etc.). | |
204 | .... Topological properties (e.g., number of holes in a pattern*, connectivity, etc.): |
This subclass is indented under subclass 203. Subject matter in which the features do not depend on measurements of dimensions or areas but are concerned instead with numbers or relationships of the different geometrical units (vertices, edges, faces, holes) involved. | |
205 | ... Local neighborhood operations (e.g., 3x3 kernel, window, or matrix operator): |
This subclass is indented under subclass 195. Subject matter in which the image under analysis, together
with any pattern* therein, is divided into a plurality
of generally two-dimensional neighborhoods (also known as windows,
sections, or regions), and a computation is executed on each neighborhood
based on a local computational algorithm or on a set of mapping
operators which transform various qualitative geometric properties
of the image into quantitative values.
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206 | .. Global features (e.g., measurements on image as a whole, such as area, projections, etc.): |
This subclass is indented under subclass 190. Subject matter wherein the features extracted for recognition purposes are not the component parts of what is essentially a more complex pattern*, but rather are measurements characterizing the entire pattern* as a single entity. | |
207 | .. Waveform analysis: |
This subclass is indented under subclass 190. Subject matter wherein the pattern* to be recognized
is first converted into an equivalent electrical analog signal,
and this signal is then analyzed according to waveform analysis
techniques in order to obtain useful measurements for recognition
processing.
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208 | ... With a tapped delay line: |
This subclass is indented under subclass 207. Subject matter wherein the waveform analyzing system utilizes a delay line with tapped outputs for detecting the value of a waveform at various points along the waveform, thereby allowing sampling of the waveform at time-spaced intervals. | |
209 | . Template matching (e.g., specific devices that determine the best match): |
This subclass is indented under subclass 181. Subject matter wherein a pattern* is detected directly by looking for a match between the input image and a representation of the pattern*, traditionally a two-dimensional template or mask. | |
210 | .. Spatial filtering (e.g., holography): |
This subclass is indented under subclass 209. Subject matter in which an optical image of each pattern* to
be recognized is transformed into a light amplitude distribution
that is proportional to the two-dimensional Fourier transform of
the pattern* image, and a spatial filter located in the
transform plane modifies selected Fourier components of the resulting Fourier
spectrum.
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211 | ... With electrically controlled light modulator or filter: |
This subclass is indented under subclass 210. Subject matter wherein the spatial filtering unit comprises
an electrically, or electronically controllable light modulator
to impart an amplitude or phase modulation onto light passing through
or reflected by the modulator.
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212 | .. Nonholographic optical mask or transparency: |
This subclass is indented under subclass 209. Subject matter wherein the optically formed image of each pattern* to be identified is compared to a set of optically stored prototypes, holograms not included. | |
213 | ... Using both positive and negative masks or transparencies: |
This subclass is indented under subclass 212. Subject matter wherein pattern* matching is performed using optically stored prototypes which represent both positive and negative versions of the pattern*s to be recognized. | |
214 | ... With a display: |
This subclass is indented under subclass 212. Subject matter wherein the recognition system includes means for displaying inputted pattern*s or for displaying the results of the matching. | |
215 | .. Using dynamic programming or elastic templates (e.g., warping): |
This subclass is indented under subclass 209. Subject matter wherein an image signal is deformed to optimally match another image signal for comparison in time or space, such as a template being geometrically distorted to achieve geometric conformity with another template. | |
216 | .. At multiple image orientations or positions: |
This subclass is indented under subclass 209. Subject matter wherein the matching of pattern*s
is performed as the pattern*s are translated or rotated
with respect to each other.
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217 | .. Electronic template: |
This subclass is indented under subclass 209. Subject matter wherein the recognition system, having converted an input pattern* into corresponding electrical signals, looks for a match between those signals and any one of a set of pattern* standards that are implemented electronically. | |
218 | ... Comparator: |
This subclass is indented under subclass 217. Subject matter wherein standards, held in a storage unit
of the pattern* recognition* system and representing
known pattern*s, are compared to an as-yet-unrecognized
input pattern* in a manner such that a signal is developed
reflecting the similarity between the standards as stored and the
input pattern*.
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219 | .... Determining both similarities and differences: |
This subclass is indented under subclass 218. Subject matter wherein both the matched and mismatched portions of pattern*s during a comparison, are utilized for recognition. | |
220 | .... Calculating weighted similarity or difference (e.g., don"t-care areas): |
This subclass is indented under subclass 218. Subject matter wherein certain areas or features that are more important than others are assigned a greater weight or value so that their correspondence with an input pattern* yields a larger output match value; this subclass l so includes using reference pattern*s that have areas that can be of any value and still produce a match, such as don"t-care areas. | |
221 | .... Counting difference pixel*s: |
This subclass is indented under subclass 218. Subject matter wherein pixel*s of a pattern* which do not match another pattern* are used to determine the degree of correspondence between the pattern*s. | |
222 | ..... Using an Exclusive-OR gate: |
This subclass is indented under subclass 221. Subject matter wherein means are included for performing an Exclusive OR function, to indicate similarities or differences between pattern*s. | |
223 | ... Resistor matrix: |
This subclass is indented under subclass 217. Subject matter wherein various configurations of resistors form electronic masks representing pattern* standards or criteria, and the resistors combine elements or groups of elements of the pattern* to be identified and develop a match voltage or current signal. | |
224 | . Classification: |
This subclass is indented under subclass 181. Subject matter including a specific mechanism for assigning
the pattern* to one of several possible pattern* classes (categories) based
on measurements of intraclass similarity or interclass differences.
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225 | .. Cluster analysis: |
This subclass is indented under subclass 224. Subject matter wherein pattern*s are classified according
to clusters or groups of points or vectors indicative of features
in a multidimensional feature space.
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226 | .. Sequential decision process (e.g., decision tree structure): |
This subclass is indented under subclass 224. Subject matter wherein the classification of a pattern* proceeds sequentially through logical stages, each successive stage reducing typically the number of likely choices of pattern* classes, culminating finally in either a definite class assignment for the pattern* or a reject signal. | |
227 | ... With a multilevel classifier: |
This subclass is indented under subclass 226. Subject matter wherein a different classification principle
is utilized at each stage or level.
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228 | .. Statistical decision process: |
This subclass is indented under subclass 224. Subject matter in which statistics or the laws of probability play a significant role in determining the proper classification of a pattern*. | |
229 | . Context analysis or word recognition (e.g., character string): |
This subclass is indented under subclass 181. Subject matter wherein the recognition system examines the
environment of a pattern* for clues as to the pattern*"s
identity.
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230 | .. Trigrams or digrams: |
This subclass is indented under subclass 229. Subject matter wherein the recognition system utilizes the characteristics of strings of two or three characters. | |
231 | .. Checking spelling for recognition: |
This subclass is indented under subclass 229. Subject matter wherein recognition is verified based on
whether a recognized character string is a valid character combination (i.e., it
is a known or acceptable word).
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232 | IMAGE COMPRESSION OR CODING: |
This subclass is indented under the class definition. Subject matter in which the quantity of data used to represent
an image is reduced to minimize storage or transmission requirements.
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233 | . Including details of decompression: |
This subclass is indented under subclass 232. Subject matter which further includes apparatus, elements
or operations for decompressing or decoding the compressed coded
image data so as to restore the original image.
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234 | . Parallel coding architecture: |
This subclass is indented under subclass 232. Subject matter which includes two or more processing paths in parallel so as to compress or code a plurality of pixel*s or pixel* groups at substantially the same time. | |
235 | . Substantial processing of image in compressed form: |
This subclass is indented under subclass 232. Subject matter in which compressed image data is operated
upon or manipulated without being expanded or decompressed.
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236 | . Interframe coding (e.g., difference or motion detection): |
This subclass is indented under subclass 232. Subject matter including means or processes for encoding
a plurality of image frames based on at least one relationship
between the data of two or more of the image frames.
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237 | . Gray level to binary coding: |
This subclass is indented under subclass 232. Subject matter in which multi-bit pixel* values representing
a plurality of image intensity values are compressed or coded into
single bit pixel* values representing one of two image intensities.
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238 | . Predictive coding: |
This subclass is indented under subclass 232. Subject matter wherein the value or characteristic of at
least one future pixel* is predicted based upon the value
or characteristic of at least one earlier or neighboring pixel* and
the image data is coded using or based upon the prediction.
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239 | . Adaptive coding (i.e., changes based upon history, activity, busyness, etc.): |
This subclass is indented under subclass 232. Subject matter including a means or process for selecting
one of a plurality of coding routines, or modifying a single
coding routine based upon the characteristics of either the original image
or a coded image.
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240 | . Pyramid, hierarchy or tree structure: |
This subclass is indented under subclass 232. Subject matter wherein the compression of the image data
proceeds sequentially through logical stages, each successive
stage reducing the amount of data required to represent the image.
SEE OR SEARCH THIS CLASS, SUBCLASS:
SEE OR SEARCH CLASS:
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241 | . Polygonal approximation: |
This subclass is indented under subclass 232. Subject matter in which objects in an image are coded by
approximating the shape of each object using polygons.
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242 | . Contour or chain coding (e.g., Bezier): |
This subclass is indented under subclass 232. Subject matter in which lines or edges in an image are approximated
using curves, line segments or using mathematical approximations.
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243 | . Shape, icon or feature-based compression: |
This subclass is indented under subclass 232. Subject matter including means or processes for assigning codes to shapes, symbols and other features detected in the image. | |
244 | . Lossless compression: |
This subclass is indented under subclass 232. Subject matter in which no image data is lost during compression
and decompression.
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245 | .. Run length coding: |
This subclass is indented under subclass 244. Subject matter in which the length of each run in the image (i.e., the
number of adjacent pixel*s in a row having the same value) is
used to encode the image.
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246 | .. Huffman or variable length coding: |
This subclass is indented under subclass 244. Subject matter in which image data are encoded using a variable
length code, such that the most common image data have
the shortest codes.
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247 | .. Arithmetic coding: |
This subclass is indented under subclass 244. Subject matter in which the image data, consisting of a sequence of source symbols, is assigned a single arithmetic code word that defines an interval of real numbers between 0 and 1. | |
248 | . Transform coding: |
This subclass is indented under subclass 232. Subject matter in which the image data undergoes a mathematical
transformation such as a Fourier or Laplace transform, and
the transform coefficients are used to encode the image.
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249 | .. Fractal: |
This subclass is indented under subclass 248. Subject matter in which the transform coding of an image
is achieved by translating the image into fractal equations, a
fractal being a complex pattern* that recurs at various
sizes in the image.
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250 | .. Discrete cosine or sine transform: |
This subclass is indented under subclass 248. Subject matter in which image data is partitioned into blocks
and transformed using the discrete cosine or the discrete sine transform.
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251 | . Quantization: |
This subclass is indented under subclass 232. Subject matter in which a broad range of input image values
are mapped to a limited number of output image values.
SEE OR SEARCH THIS CLASS, SUBCLASS:
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252 | .. Error diffusion or dispersion: |
This subclass is indented under subclass 251. Subject matter in which any error in a given output image
value that results from quantization is distributed among surrounding
values so as to reduce the losses that accumulated errors would
have on the output image as a whole.
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253 | .. Vector quantization: |
This subclass is indented under subclass 251. Subject matter in which a limited number of image values
are stored in a codebook or dictionary and only those image values
are outputted that are closest to the input image values.
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254 | IMAGE ENHANCEMENT OR RESTORATION: |
This subclass is indented under the class definition. Subject matter directed to the improvement of pictorial
or image information so that the result is more suitable than the
original information for human or machine interpretation.
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255 | . Focus measuring or adjustment (e.g., deblurring): |
This subclass is indented under subclass 254. Subject matter wherein the focal length between an image
sensor and an object being sensed is either measured or adjusted
in order to correct distortions such as blurring.
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256 | . Object boundary expansion or contraction: |
This subclass is indented under subclass 254. Subject matter in which pixel*s are added or deleted
from the boundaries of objects in an image.
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257 | .. Dilation or erosion (e.g., opening or closing): |
This subclass is indented under subclass 256. Subject matter in which pixel*s are added or deleted
by a specific morphological operation that passes a structuring
element over the entire image.
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258 | .. Line thinning or thickening: |
This subclass is indented under subclass 256. Subject matter in which the thickness of any object in the
image is either reduced or broadened to some uniform standard.
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259 | ... Skeletonizing: |
This subclass is indented under subclass 258. Subject matter wherein the objects which, for example, may be the strokes or lines forming an alpha-numeric character, are reduced to a one pixel* width while their connectedness is maintained. | |
260 | . Image filter: |
This subclass is indented under subclass 254. Subject matter directed to any electrical apparatus or image
processing operations that enhance images by suppressing or minimizing certain
spatial frequencies.
SEE OR SEARCH THIS CLASS, SUBCLASS:
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261 | .. Adaptive filter: |
This subclass is indented under subclass 260. Subject matter wherein parameters of the filter change in
accordance with the input image data.
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262 | .. Median filter: |
This subclass is indented under subclass 260. Subject matter wherein a gray level of each pixel* in
an image is replaced by the median of the gray levels in a neighborhood
of that pixel*.
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263 | .. Highpass filter (i.e., for sharpening or enhancing details): |
This subclass is indented under subclass 260. Subject matter wherein low-frequency components
of the image are attenuated or eliminated.
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264 | .. Lowpass filter (i.e., for blurring or smoothing): |
This subclass is indented under subclass 260. Subject matter wherein high-frequency components
of the image are attenuated or eliminated.
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265 | .. Recursive filter: |
This subclass is indented under subclass 260. Subject matter in which the filter is repeatedly applied to the image until a specified condition is met. | |
266 | . Edge or contour enhancement: |
This subclass is indented under subclass 254. Subject matter wherein processing is done to visually enhance
the outlines of individual characters or objects of interest in
the image by emphasizing high frequency, transitional image data
while deemphasizing or removing low-frequency, homogeneous
background image data.
SEE OR SEARCH THIS CLASS, SUBCLASS:
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267 | .. Minimize discontinuities in dot-matrix image data (i.e., connecting or merging the dots): |
This subclass is indented under subclass 266. Subject matter including specific processing to reduce gaps
or breaks in the borders of image objects such as by "filling-in"
pixel*s which are closer than a predetermined distance
to the edge of the object.
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268 | .. Minimize discontinuities at boundaries of image blocks (i.e., reducing blocking effects or effects of wrap-around): |
This subclass is indented under subclass 266. Subject matter including means or processes to smooth undesirable
transitions at the boundaries separating discrete pixel* blocks
after the blocks have undergone processing such as compression or
decompression, or to process pixel*s at the edges
of an image when the process requires neighboring or context pixel*s.
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269 | .. Minimize jaggedness in edges (e.g., anti-aliasing): |
This subclass is indented under subclass 266. Subject matter which includes processing to reduce the "stair-step"
effect at curved edges of characters or other objects to accurately
represent the high frequency detail of the original image.
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270 | . Variable threshold, gain or slice level: |
This subclass is indented under subclass 254. Subject matter wherein quantization of an input analog or
gray scale image to produce an output gray scale image or bit map
utilizes a threshold, gain or slice level which self-adjusts according
to characteristics such as the contrast or brightness of the image
or portion of the image being processed.
SEE OR SEARCH THIS CLASS, SUBCLASS:
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271 | .. Based on the results of a count: |
This subclass is indented under subclass 270. Subject matter wherein the threshold or quantization value
derived for a particular element in the image depends upon the relative
frequency of occurrence of each brightness level in a region around
the image element or the number of "black" image elements at a particular
location over a number of images of the same object.
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272 | .. Based on a local average, mean or median: |
This subclass is indented under subclass 270. Subject matter wherein the threshold or quantization value of a particular image element depends on an average, mean or median measurement of neighboring elements, with a similar average, mean or median operation being applied to each element of the whole image. | |
273 | .. Based on peak levels: |
This subclass is indented under subclass 270. Subject matter wherein the threshold or quantization value of a particular image element is set according to the highest and/or lowest amplitude of some measured signal, such as the grey levels of neighboring image elements or a white or black signal produced by scanning a reference plate prior to scanning the image. | |
274 | . Intensity, brightness, contrast or shading correction: |
This subclass is indented under subclass 254. Subject matter which involves correcting pixel* values
for variations in ambient lighting or the optimum brightness, intensity
or contrast range of an image to enhance desired image features.
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275 | . Artifact removal or suppression (e.g., distortion correction): |
This subclass is indented under subclass 254. Subject matter directed to correcting undesirable image characteristics such as spatial distortion (i.e. subtracting difference data between frames to correct for blurring due to motion), sensor or optical system induced artifacts (i.e. geometric aberrations), process induced artifacts (i.e. "worm" artifacts caused by error diffusion) or physical deterioration of a scanned object itself (i.e. dirt or dust on photos:graphic negatives). | |
276 | IMAGE TRANSFORMATION OR PRE-PROCESSING: |
This subclass is indented under the class definition. Subject matter directed either to transformation of a given
representation of an image into another representation by some mathematically-derived
transform or process; or to operations performed on an
image representation, prior to any attempt at recognition, for
the specific purpose of facilitating acquisition or subsequent recognition
of imagery pattern*s.
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277 | . Transforming each dimension separately: |
This subclass is indented under subclass 276. Subject matter wherein a multidimensional transformation or process (such as a two-dimensional FFT) is performed using separate one-dimensional transforms or processes. In other words, the rows of an image array are transformed separately from the columns, the horizontal components are transformed separately from the vertical components, or each axis is transformed separately from the other axes. | |
278 | . Correlation: |
This subclass is indented under subclass 276. Subject matter wherein a correlation operation is performed
on image data.
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279 | . Convolution: |
This subclass is indented under subclass 276. Subject matter wherein a convolution operation is performed
on image data.
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280 | . Fourier transform: |
This subclass is indented under subclass 276. Subject matter wherein a Fourier transform is performed
on image data.
SEE OR SEARCH THIS CLASS, SUBCLASS:
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281 | . Walsh, Hough or Hadamard transform: |
This subclass is indented under subclass 276. Subject matter wherein a Walsh, Hough, or Hadamard
transform is performed on image data.
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282 | . Selecting a portion of an image: |
This subclass is indented under subclass 276. Subject matter wherein transformation or pre-processing
operations are performed on a limited subset of the total image
data which has been designated using a scanning window or preliminary
step which identifies specific regions of the image (i.e., only
text portions).
SEE OR SEARCH THIS CLASS, SUBCLASS:
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283 | .. Using a mask: |
This subclass is indented under subclass 282. Subject matter wherein input image data is compared with a standard, such as coordinate data stored in a memory, so that certain image data may be removed or masked, allowing only the data in a desired area to be extracted for further processing. | |
284 | . Combining image portions (e.g., portions of oversized documents): |
This subclass is indented under subclass 276. Subject matter wherein a final image is created by selectively
combining, merging, or superimposing regions from
multiple images (e.g. pasting a first
image portion into a base image), or regions from
the same image which had to be scanned, stored or processed
in pieces.
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285 | . Mapping 2D image onto a 3D surface: |
This subclass is indented under subclass 276. Subject matter wherein a two-dimensional image
is projected onto a three-dimensional surface so as to
give the illusion that the image has a third dimension.
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286 | . Measuring image properties (e.g., length, width, or area): |
This subclass is indented under subclass 276. Subject matter encompassing the extraction of physical
properties exhibited by imaged objects such as length, width, thickness, size, area, shape, and
boundary points to aid in later processing.
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287 | .. Detecting alignment marks: |
This subclass is indented under subclass 286. Subject matter wherein the properties extracted from an
image relate to reference marks, sometimes called fiducials, which
are used to measure the image position or orientation and aid in
image alignment.
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288 | .. Determining center of gravity or moment: |
This subclass is indented under subclass 286. Subject matter wherein the properties extracted from an image relate to its center point or its various moments, such as the moment of inertia, center of gravity, center of mass, and so on. | |
289 | .. Determining amount an image is rotated or skewed: |
This subclass is indented under subclass 286. Subject matter wherein the properties extracted from an
image relate to its inclination or skew angle measured with respect
to a reference which may be, for example, a scanning
direction or the physical orientation of a sensor array.
SEE OR SEARCH THIS CLASS, SUBCLASS:
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290 | ... Where the image is a character, word, or text: |
This subclass is indented under subclass 289. Subject matter wherein the measurement is undertaken to determine the amount of skew or angular orientation of a character, word, or text. | |
291 | .. Determining the position of an object: |
This subclass is indented under subclass 286. Subject matter wherein the properties extracted from an
image result in a set of values representing spatial coordinates
or features, such as document edges, and the position
of an object relative to a reference.
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292 | ... Where the object is a character, word, or text: |
This subclass is indented under subclass 291. Subject matter wherein the set of values relates specifically
to the position of a character, word or text.
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293 | . Changing the image coordinates: |
This subclass is indented under subclass 276. Subject matter encompassing image coordinate transformations
undertaken to correct geometric distortions or misregistration between
the image and, for example, an image sensor.
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294 | .. Registering or aligning multiple images to one another: |
This subclass is indented under subclass 293. Subject matter encompassing the registration or alignment between two or more images. The images, for example, may be from consecutive image frames or fields; or they may be of the same scene taken at different viewing angles or at different times; or they may consist of an original image and stored prototypes. | |
295 | .. To position or translate an image: |
This subclass is indented under subclass 293. Subject matter wherein a coordinate transformation is undertaken
for the purpose of shifting the image (or a signal representation
thereof) from one position in space to another position relative
to a coordinate reference.
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296 | .. To rotate an image: |
This subclass is indented under subclass 293. Subject matter wherein a coordinate transformation is undertaken
for the purpose of turning the image (or a signal representation
thereof) about an axis or center, or adjusting
the image"s orientation and skew.
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297 | ... Rotation of image is limited to 90, 180, or 270: |
This subclass is indented under subclass 296. Subject matter wherein the coordinate transformation results in an image turned 90, 180, or 270 degrees relative to the original image. | |
298 | .. To change the scale or size of an image: |
This subclass is indented under subclass 293. Subject matter wherein a coordinate transformation is undertaken
for the purpose of either reducing or enlarging the overall size
of an image (or a signal representation thereof).
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299 | ... Raising or lowering the image resolution (e.g., sub pixel* accuracy): |
This subclass is indented under subclass 298. Subject matter wherein the image scaling is achieved by
altering the spatial resolution or density of dots, pixel*s
or image elements used to represent the image in a quantized form. Such
transformations may be utilized in a multiuse environment to achieve
compatibility between input and output devices.
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300 | .... Interpolation: |
This subclass is indented under subclass 299. Subject matter wherein the resolution of an image is increased
by the addition of counterfeit pixel*s (or image
elements) whose values are calculated (i.e., interpolated) based
on real image pixel*s that are in the neighborhood of the
counterfeit pixel*s to be added.
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301 | ... Where the image is an alphanumeric character: |
This subclass is indented under subclass 298. Subject matter wherein the image whose scale or size is
changed is a letter, number or other language symbol.
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302 | . Multilayered image transformations: |
This subclass is indented under subclass 276. Subject matter in which different portions of an image are
transformed separately or the image as a whole is subjected to a
set of image processing transformations.
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303 | .. Pipeline processing: |
This subclass is indented under subclass 302. Subject matter wherein several layers of transformations
are combined such that a first layer of transforms are applied to
an initial representation of an image, a second layer of
transforms is applied to the output of the first layer, and
so on.
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304 | .. Parallel processing: |
This subclass is indented under subclass 302. Subject matter wherein the transformations are applied simultaneously
to the image.
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305 | . Image storage or retrieval: |
This subclass is indented under subclass 276. Subject matter wherein the image inputted to the image analyzing
system is stored in, or retrieved from, a large
capacity storage medium such as an optical disk.
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306 | .. Using identification indicia on document: |
This subclass is indented under subclass 305. Subject matter wherein indicia, such as keywords, that appear on a document are used to file the document or to search the document data to be retrieved. | |
307 | . General purpose image processor: |
This subclass is indented under subclass 276. Subject matter directed to an arrangement of processing elements that may be programmed or reconfigured to perform a variety of image processing operations. | |
308 | .. Morphological operations (i.e., local neighborhood operations): |
This subclass is indented under subclass 307. Subject matter wherein the processing is limited to local neighborhood operations performed on an image and implemented by convolving the image with an image of a structuring element, typically a 3x3 or 5x5 pixel* object, kernel or window. | |
309 | EDITING, ERROR CHECKING OR CORRECTION (e.g., POST-RECOGNITION PROCESSING): |
This subclass is indented under the class definition. Subject matter directed to any operation for testing the
reliability and performance of an image analyzing system, uncovering
or correcting errors, making editorial changes in images read by
the system, or preparing the output of the system for further processing.
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310 | . Correcting alphanumeric recognition errors: |
This subclass is indented under subclass 309. Subject matter directed to correcting textual data such as the letters, numbers and other characters that make up a text and that could not be recognized or that were misrecognized by a character recognition system. | |
311 | . Including operator interaction: |
This subclass is indented under subclass 309. Subject matter wherein a human operator checks, corrects
or edits the image data usually with the aid of a keyboard and a
display.
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312 | IMAGE SENSING: |
This subclass is indented under the class definition. Subject matter wherein an image sensor is specifically claimed
for converting an image into signals that are readily usable in
image analysis*.
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313 | . Hand-held: |
This subclass is indented under subclass 312. Subject matter wherein the sensor must be handled by a human
operator to generate the necessary image signals.
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314 | .. Sensing mechanism in stylus: |
This subclass is indented under subclass 313. Subject matter wherein the image sensor, which may be an
optical, electrical, magnetic, or piezoelectric device, is housed
within a pen or stylus held by the human operator.
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315 | .. Sensing mechanism in platen: |
This subclass is indented under subclass 313. Subject matter wherein the image sensor is mounted within,
underneath or around the surface (often referred to as a platen)
on which the image is formed.
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316 | . Curve tracer: |
This subclass is indented under subclass 312. Subject matter having means for causing the image sensor
to follow the edge, contour or boundary of an image pattern* (such
as an alphanumeric character) so that measurements may be made that
are useful in analyzing or recognizing the pattern*.
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317 | . Sensor control (e.g., OCR sheet controls copier or fax): |
This subclass is indented under subclass 312. Subject matter wherein the image sensor is directed and
controlled by certain special markings or guides on a document or
by specific internal programming so as to skip nonessential items
of information, to skip lines on a document, to control timing and
sampling, to regulate speed, to use the sensor output for a specific
control function.
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318 | . Multiple scanning: |
This subclass is indented under subclass 312. Subject matter wherein the same image area is scanned more than once. | |
319 | .. Prescanning: |
This subclass is indented under subclass 318. Subject matter in which a preliminary scan of the image area is done for the purpose of pre-recognition processing (i.e., image alignment, evaluation of print quality, etc.). | |
320 | . Magnetic: |
This subclass is indented under subclass 312. Subject matter wherein magnetic properties of an image are
sensed by suitable magnetic transducer(s).
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321 | . Optical (e.g., OCR): |
This subclass is indented under subclass 312. Subject matter wherein the optical properties of an image
are sensed by suitable optical transducer(s).
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322 | .. Single spot: |
This subclass is indented under subclass 321. Subject matter wherein an optical transducer (cooperating with a flying-spot scanner, for example) senses a property at a single spot in the image, and that spot is swept progressively over an image area. | |
323 | .. Single line: |
This subclass is indented under subclass 321. Subject matter wherein a plurality of optical transducers sufficient to form a single line of sensing elements, sense the optical properties in a corresponding line of the image, and that line is swept progressively over an image area. | |
324 | .. Full retina: |
This subclass is indented under subclass 321. Subject matter wherein a plurality of optical transducers sufficient to form a two-dimensional array of sensing elements, simultaneously sense the optical properties in a corresponding two-dimensional area of the image. | |
325 | MISCELLANEOUS: |
This subclass is indented under the class definition. Subject matter for analysis of image data and not elsewhere
classified.
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FOR000 | CLASS-RELATED FOREIGN DOCUMENTS |
This subclass has no definition. | |