Method of generating feature vector, generating histogram, and learning classifier for recognition of behavior

Patent metadata

Field	Value
Publication number	US-9436890-B2
Application number	US-201414562948-A
Country	US
Kind code	B2
Filing date	Dec 8, 2014
Priority date	Jan 23, 2014
Publication date	Sep 6, 2016
Grant date	Sep 6, 2016

Abstract

Example embodiments disclose a method of generating a feature vector, a method of generating a histogram, a learning unit classifier, a recognition apparatus, and a detection apparatus, in which a feature point is detected from an input image based on a dominant direction analysis of a gradient distribution, and a feature vector corresponding to the detected feature point is generated.

First claim

What is claimed is:

1. A method of generating a feature vector, the method comprising:
detecting a feature point from an input image based on a dominant direction of a gradient distribution in the input image, the detecting including,
detecting a pixel corresponding to a window having a contrast of coherence in a dominant direction; and

generating a feature vector corresponding to the feature point.

2. The method of claim 1 , wherein the
contrast of coherence is an indication of differences between at least one eigenvalue associated with the detected pixel and eigenvalues of other pixels in the window.

3. The method of claim 1 , wherein the generating of the feature vector comprises:
accumulating a strength of coherence in a dominant direction within a local area corresponding to the feature point to generate the feature vector.

4. The method of claim 1 , wherein the detecting of the feature point comprises:
calculating a gradient for a plurality of pixels comprised in the input image;
calculating a structure tensor for the plurality of pixels based on the gradient;
calculating a maximum eigenvalue for the plurality of pixels by performing an Eigen analysis on the structure tensor; and
determining the feature point through a contrast amongst maximum eigenvalues.

5. The method of claim 4 , wherein the calculating of the structure tensor for the plurality of pixels comprises:
when the input image is a video image, calculating a structure tensor of a single pixel based on a matrix

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,

wherein G x denotes a gradient in an x axis direction, G y denotes a gradient in a y axis direction, G t denotes a gradient in a time axis direction, and B denotes a predetermined size of a block comprising the single pixel.

6. The method of claim 4 , wherein the calculating of the structure tensor for the plurality of pixels comprises:
when the input image is a still image, calculating a structure tensor of a single pixel based on a matrix

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Assignees

• Samsung Electronics Co Ltd

Inventors

• Kim Won Jun
• Yoo Byung In
• Han Jae Joon

Classifications

• G06V40/20 Primary

  Movements or behaviour, e.g. gesture recognition (recognition of facial expressions G06V40/16) · CPC title

• G06V10/50

  by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis · CPC title

• G06V10/44

  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 · CPC title

• G06K9/00335

  Physics · mapped topic

• G06K9/4642 Primary

  Physics · mapped topic