G
PHYSICS

Note(s)

  • In this section, the following term is used with the meaning indicated:
    • "variable" as a noun means a feature or property, e.g. a dimension, a physical condition such as temperature, a quality such as density or colour, which, in respect of a particular entity, e.g. an object, a quantity of a substance, a beam of light, and at a particular instant, is capable of being measured; the variable may change, so that its numerical expression may assume different values at different times, in different conditions or in individual cases, but may be constant in respect of a particular entity in certain conditions or for practical purposes, e.g. the length of a bar may be regarded as constant for many purposes.
  • Attention is drawn to the definitions of terms or expressions used. Some appear in the notes of several of the classes in this section, see in particular the definition of "measuring" in class G01. Others appear in paragraph 187 of the Guide to the IPC, see in particular the definitions of "control" and "regulation".
  • Classification in this section may present more difficulty than in other sections, because the distinction between different fields of use rests to a considerable extent on differences in the intention of the user rather than on any constructional differences or differences in the manner of use, and because the subjects dealt with are often in effect systems or combinations, which have features or parts in common, rather than "things", which are readily distinguishable as a whole. For example, information, e.g. a set of figures, may be displayed for the purpose of education or advertising covered by class G09, for enabling the result of a measurement to be known covered by class G01, for signalling the information to a distant point or for giving information which has been signalled from a distant point covered by class G08. The words used to describe the purpose depend on features that may be irrelevant to the form of the apparatus concerned, for example, such features as the desired effect on the person who sees the display, or whether the display is controlled from a remote point. Again, a device which responds to some change in a condition, e.g. in the pressure of a fluid, may be used, without modification of the device itself, to give information about the pressure covered by subclass G01L or about some other condition linked to the pressure covered by another subclass of class G01, e.g. G01K for temperature, to make a record of the pressure or of its occurrence covered by subclass G07C, to give an alarm covered by subclass G08B, or to control another apparatus covered by class G05.
    • The classification scheme is intended to enable things of a similar nature, as indicated above, to be classified together. It is therefore particularly necessary for the real nature of any technical subject to be decided before it can be properly classified.
G06
COMPUTING; CALCULATING OR COUNTING

Note(s) [2011.01]

  • This class covers :
    • simulators which are concerned with the mathematics of computing the existing or anticipated conditions within the real device or system;
    • simulators which demonstrate, by means involving computing, the function of apparatus or of a system, if no provision exists elsewhere;
    • image data processing or generation.
  • This class does not cover :
    • combinations of writing implements with computing devices, which are covered by group B43K 29/08;
    • control functions derived from simulators, in general, which are covered by class G05, although such functions may be covered by the subclass of this class for the device controlled;
    • measurement or analysis of an individual variable to serve as an input to a simulator, which is covered by class G01;
    • simulators regarded as teaching or training devices which is the case if they give perceptible sensations having a likeness to the sensations a student would experience in reality in response to actions taken by him. Such simulators are covered by class G09;
    • components of simulators, if identical with real devices or machines, which are covered by the relevant subclass for these devices or machines and not by class G09.
  • In this class, the following terms or expressions are used with the meanings indicated:
    • "data" is used as the synonym of "information". Therefore, the term "information" is not used in subclass G06C;
    • "ICT [information and communication technology]" also covers "IT [information technology]";
    • "calculating or computing" includes, inter alia, operations on numerical values and on data expressed in numerical form. Of these terms "computing" is used throughout the class; "computation" is derived from this interpretation of "computing". In the French language the term "calcul" will serve for either term;
    • "simulator" is a device which may use the same time scale as the real device or operate on an expanded or compressed time scale. In interpreting this term models of real devices to reduced or expanded scales are not regarded as simulators;
    • "record carrier" means a body, such as a cylinder, disc, card, tape, or wire, capable of permanently holding information, which can be read-off by a sensing element movable relative to the recorded information.
  • Attention is drawn to the Notes following the title of section G, especially as regards the definition of the term "variable".
G06V
IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING [2022.01]

Note(s) [2022.01]

G06V 10/00
Arrangements for image or video recognition or understanding (character recognition in images or video G06V 30/10) [2022.01]
G06V 10/10
Image acquisition (document image scanning and transmission H04N 1/00; control of digital cameras H04N 23/60) [2022.01]
G06V 10/12
Details of acquisition arrangements; Constructional details thereof [2022.01]
G06V 10/14
Optical characteristics of the device performing the acquisition or on the illumination arrangements [2022.01]
G06V 10/141
Control of illumination [2022.01]
G06V 10/143
Sensing or illuminating at different wavelengths [2022.01]
G06V 10/145
Illumination specially adapted for pattern recognition, e.g. using gratings [2022.01]
G06V 10/147
Details of sensors, e.g. sensor lenses (fingerprint or palmprint sensors G06V 40/13; vascular sensors G06V 40/145; eye sensors G06V 40/19) [2022.01]
G06V 10/20
Image preprocessing [2022.01]
G06V 10/22
by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition [2022.01]
G06V 10/24
Aligning, centring, orientation detection or correction of the image [2022.01]
G06V 10/25
Determination of region of interest [ROI] or a volume of interest [VOI] [2022.01]
G06V 10/26
Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion [2022.01]
G06V 10/28
Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns [2022.01]
G06V 10/30
Noise filtering [2022.01]
G06V 10/32
Normalisation of the pattern dimensions [2022.01]
G06V 10/34
Smoothing or thinning of the pattern; Morphological operations; Skeletonisation [2022.01]
G06V 10/36
Applying a local operator, i.e. means to operate on image points situated in the vicinity of a given point; Non-linear local filtering operations, e.g. median filtering [2022.01]
G06V 10/40
Extraction of image or video features [2022.01]
G06V 10/42
Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation [2022.01]
G06V 10/422
for representing the structure of the pattern or shape of an object therefor [2022.01]
G06V 10/424
Syntactic representation, e.g. by using alphabets or grammars [2022.01]
G06V 10/426
Graphical representations [2022.01]
G06V 10/44
Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components [2022.01]
G06V 10/46
Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features (colour feature extraction G06V 10/56) [2022.01]
G06V 10/48
by mapping characteristic values of the pattern into a parameter space, e.g. Hough transformation [2022.01]
G06V 10/50
by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis [2022.01]
G06V 10/52
Scale-space analysis, e.g. wavelet analysis (multi-scale boundary representations G06V 10/42) [2022.01]
G06V 10/54
relating to texture [2022.01]
G06V 10/56
relating to colour [2022.01]
G06V 10/58
relating to hyperspectral data [2022.01]
G06V 10/60
relating to illumination properties, e.g. using a reflectance or lighting model [2022.01]
G06V 10/62
relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking [2022.01]
G06V 10/70
using pattern recognition or machine learning (optical pattern recognition or electronic computations therefor G06V 10/88) [2022.01]
G06V 10/72
Data preparation, e.g. statistical preprocessing of image or video features [2022.01]
G06V 10/74
Image or video pattern matching; Proximity measures in feature spaces [2022.01]
G06V 10/75
Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries [2022.01]
G06V 10/762
using clustering, e.g. of similar faces in social networks [2022.01]
G06V 10/764
using classification, e.g. of video objects [2022.01]
G06V 10/766
using regression, e.g. by projecting features on hyperplanes [2022.01]
G06V 10/77
Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation [2022.01]
G06V 10/771
Feature selection, e.g. selecting representative features from a multi-dimensional feature space [2022.01]
G06V 10/772
Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries [2022.01]
G06V 10/774
Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting [2022.01]
G06V 10/776
Validation; Performance evaluation [2022.01]
G06V 10/778
Active pattern-learning, e.g. online learning of image or video features [2022.01]
G06V 10/80
Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level (multimodal speaker identification or verification G10L 17/10) [2022.01]
G06V 10/82
using neural networks [2022.01]
G06V 10/84
using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks [2022.01]
G06V 10/86
using syntactic or structural representations of the image or video pattern, e.g. symbolic string recognition; using graph matching [2022.01]
G06V 10/88
Image or video recognition using optical means, e.g. reference filters, holographic masks, frequency domain filters or spatial domain filters [2022.01]
G06V 10/94
Hardware or software architectures specially adapted for image or video understanding [2022.01]
G06V 10/96
Management of image or video recognition tasks [2022.01]
G06V 10/98
Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns [2022.01]
G06V 20/00
Scenes; Scene-specific elements (control of digital cameras H04N 23/60) [2022.01]

Note(s) [2022.01]

  • In this group, the following term is used with the meaning indicated:
    • scene” is a visual representation of the world or of some elements of it, as captured by a sensor or generated by a computer.
G06V 20/05
Underwater scenes [2022.01]
G06V 20/10
Terrestrial scenes (scenes under surveillance with static cameras G06V 20/52; scenes perceived from the exterior of a vehicle G06V 20/56; scenes perceived from the interior of a vehicle G06V 20/59) [2022.01]
G06V 20/13
Satellite images [2022.01]
G06V 20/17
taken from planes or by drones [2022.01]
G06V 20/20
in augmented reality scenes [2022.01]
G06V 20/30
in albums, collections or shared content, e.g. social network photos or video [2022.01]
G06V 20/40
in video content (extracting overlay text G06V 20/62; video retrieval G06F 16/70; processing of video elementary streams in video servers H04N 21/234; processing of video elementary streams in video clients H04N 21/44) [2022.01]
G06V 20/50
Context or environment of the image [2022.01]
G06V 20/52
Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V 20/69) [2022.01]
G06V 20/54
of traffic, e.g. cars on the road, trains or boats [2022.01]
G06V 20/56
exterior to a vehicle by using sensors mounted on the vehicle [2022.01]
G06V 20/58
Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads [2022.01]
G06V 20/59
inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions [2022.01]
G06V 20/60
Type of objects [2022.01]
G06V 20/62
Text, e.g. of license plates, overlay texts or captions on TV images [2022.01]
G06V 20/64
Three-dimensional objects [2022.01]
G06V 20/66
Trinkets, e.g. shirt buttons or jewellery items (recognising microscopic objects G06V 20/69) [2022.01]
G06V 20/68
Food, e.g. fruit or vegetables [2022.01]
G06V 20/69
Microscopic objects, e.g. biological cells or cellular parts [2022.01]
G06V 20/70
Labelling scene content, e.g. deriving syntactic or semantic representations [2022.01]
G06V 20/80
Recognising image objects characterised by unique random patterns [2022.01]
G06V 20/90
Identifying an image sensor based on its output data [2022.01]
G06V 30/00
Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition (scanning, transmission or reproduction of documents or the like H04N 1/00) [2022.01]

Note(s) [2022.01]

  • This group covers recognition of characters or digital ink, where the characters or the digital ink can include representations in three dimensions, e.g. as written by performing gestures in the air.
G06V 30/10
Character recognition [2022.01]
G06V 30/12
Detection or correction of errors, e.g. by rescanning the pattern [2022.01]
G06V 30/14
Image acquisition [2022.01]
G06V 30/142
using hand-held instruments; Constructional details of the instruments [2022.01]
G06V 30/144
using a slot moved over the image; using discrete sensing elements at predetermined points; using automatic curve following means [2022.01]
G06V 30/146
Aligning or centering of the image pick-up or image-field [2022.01]
G06V 30/148
Segmentation of character regions [2022.01]
G06V 30/16
Image preprocessing [2022.01]
G06V 30/162
Quantising the image signal [2022.01]
G06V 30/164
Noise filtering [2022.01]
G06V 30/166
Normalisation of pattern dimensions [2022.01]
G06V 30/168
Smoothing or thinning of the pattern; Skeletonisation [2022.01]
G06V 30/18
Extraction of features or characteristics of the image [2022.01]
G06V 30/182
by coding the contour of the pattern [2022.01]
G06V 30/184
by analysing segments intersecting the pattern [2022.01]
G06V 30/186
by deriving mathematical or geometrical properties from the whole image [2022.01]
G06V 30/19
Recognition using electronic means [2022.01]
G06V 30/192
using simultaneous comparisons or correlations of the image signals with a plurality of references [2022.01]
G06V 30/194
References adjustable by an adaptive method, e.g. learning [2022.01]
G06V 30/196
using sequential comparisons of the image signals with a plurality of references [2022.01]
G06V 30/198
the selection of the next reference depending on the result of the preceding comparison [2022.01]
G06V 30/199
Arrangements for recognition using optical reference masks, e.g. holographic masks [2022.01]
G06V 30/20
Combination of acquisition, preprocessing or recognition functions [2022.01]
G06V 30/22
characterised by the type of writing [2022.01]
G06V 30/222
of characters separated by spaces [2022.01]
G06V 30/224
of printed characters having additional code marks or containing code marks [2022.01]
G06V 30/226
of cursive writing [2022.01]
G06V 30/228
of three-dimensional handwriting, e.g. writing in the air [2022.01]
G06V 30/24
characterised by the processing or recognition method (segmentation of character regions G06V 30/148) [2022.01]
G06V 30/242
Division of the character sequences into groups prior to recognition; Selection of dictionaries [2022.01]
G06V 30/244
using graphical properties, e.g. alphabet type or font [2022.01]
G06V 30/246
using linguistic properties, e.g. specific for English or German language [2022.01]
G06V 30/26
Techniques for post-processing, e.g. correcting the recognition result [2022.01]
G06V 30/262
using context analysis, e.g. lexical, syntactic or semantic context [2022.01]
G06V 30/28
specially adapted to the type of the alphabet, e.g. Latin alphabet [2022.01]
G06V 30/30
based on the type of data [2022.01]
G06V 30/302
Images containing characters for discriminating human versus automated computer access [2022.01]
G06V 30/304
Music notations [2022.01]
G06V 30/32
Digital ink [2022.01]
G06V 30/40
Document-oriented image-based pattern recognition [2022.01]
G06V 30/41
Analysis of document content (recognition of printed characters based on code marks G06V 30/224) [2022.01]
G06V 30/412
Layout analysis of documents structured with printed lines or input boxes, e.g. business forms or tables [2022.01]
G06V 30/413
Classification of content, e.g. text, photographs or tables [2022.01]
G06V 30/414
Extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text [2022.01]
G06V 30/416
Extracting the logical structure, e.g. chapters, sections or page numbers; Identifying elements of the document, e.g. authors [2022.01]
G06V 30/418
Document matching, e.g. of document images [2022.01]
G06V 30/42
based on the type of document [2022.01]
G06V 30/422
Technical drawings; Geographical maps [2022.01]
G06V 30/424
Postal images, e.g. labels or addresses on parcels or postal envelopes [2022.01]
G06V 40/00
Recognition of biometric, human-related or animal-related patterns in image or video data [2022.01]
G06V 40/10
Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands [2022.01]
G06V 40/12
G06V 40/13
Sensors therefor [2022.01]
G06V 40/14
G06V 40/145
Sensors therefor [2022.01]
G06V 40/16
Human faces, e.g. facial parts, sketches or expressions [2022.01]
G06V 40/18
Eye characteristics, e.g. of the iris [2022.01]
G06V 40/19
Sensors therefor [2022.01]
G06V 40/20
Movements or behaviour, e.g. gesture recognition (recognition of facial expressions G06V 40/16) [2022.01]
G06V 40/30
Writer recognition; Reading and verifying signatures [2022.01]
G06V 40/40
Spoof detection, e.g. liveness detection [2022.01]
G06V 40/50
Maintenance of biometric data or enrolment thereof [2022.01]
G06V 40/60
Static or dynamic means for assisting the user to position a body part for biometric acquisition [2022.01]
G06V 40/70
Multimodal biometrics, e.g. combining information from different biometric modalities [2022.01]