Image recognition method and image recognition apparatus

Patent metadata

Field	Value
Publication number	US-9852159-B2
Application number	US-201013375448-A
Country	US
Kind code	B2
Filing date	Jun 15, 2010
Priority date	Jun 18, 2009
Publication date	Dec 26, 2017
Grant date	Dec 26, 2017

Abstract

An image recognition apparatus is provided which comprises a first extracting means for extracting, from every registration image previously registered, a set of registration partial images of a predetermined size, and a second extracting means for extracting, from an input new image, a set of new partial images of a predetermined size. The apparatus further comprises a discriminating means for discriminating an attribute of the new partial image based on a rule formed by dividing the set of the registration partial images extracted by the first extracting means, and a collecting means for deriving a final recognition result of the new image by collecting discrimination results by the discriminating means at the time when the new partial images as elements of the set of the new partial images are input.

First claim

The invention claimed is:

1. An apparatus comprising:
a first obtaining unit configured to obtain a target image including a target object which belongs to at least one category of a plurality of categories;
a second obtaining unit configured to obtain a plurality of partial target images from the obtained target image;
a holding unit configured to hold a dictionary in which, for each of a plurality of partial learning images each of which is a part of a learning image for recognizing the target object, a category of the partial learning image and position information of the partial learning image are registered, the category and the position information of the partial learning image being classified based on a comparison result of pixel values obtained from a plurality of pixels in the partial learning image, and the position information representing information on a relative position between the target object and the partial learning image in the learning image;
an acquiring unit configured to acquire a plurality of pixel values, from each of the plurality of partial target images obtained by the second obtaining unit, of positions corresponding to the positions where the plurality of pixel values are obtained from each of the partial learning images;
a comparing unit configured to compare the plurality of pixel values acquired by the acquiring unit to each other;
a third obtaining unit configured to obtain from the dictionary, for each of the plurality of partial target images, a corresponding category and position information of the partial learning image, based on a result of a comparison by the comparing unit;
a voting unit configured to vote, for each of the plurality of partial target images, the result obtained by the third obtaining unit at a position indicated by the position information obtained by the third obtaining unit in a voting surface corresponding to the obtained category of a plurality voting surfaces corresponding to the plurality of categories, and
a recognizing unit configured to recognize a category and a position of the target object included in the target image by collecting a result of voting by the voting unit.

2. The apparatus according to claim 1 , wherein the learning image is a computer graphics image.

3. The apparatus according to claim 1 , wherein the target image is a grey-scale image.

4. The apparatus according to claim 1 , wherein the position information of the partial learning image indicates position information of the partial learning image in the learning image.

5. The apparatus according to claim 1 , wherein the dictionary comprises a tree-structured discriminator.

6. The apparatus according to claim 1 , wherein the plurality of pixels of which the pixel values are acquired are decided at random.

7. The apparatus according to claim 1 , wherein the plurality of partial images overlap each other.

8. The apparatus according to claim 1 , wherein the category is an orientation of the target object.

9. The apparatus according to claim 1 , wherein the obtained image includes a plurality of the target objects.

10. The apparatus according to claim 1 , wherein the plurality of the partial target images are obtained by shifting a predetermined partial image.

11. The apparatus according to claim 1 ,
wherein the recognizing unit recognizes the category and the position of the target object included in the target image based on the distribution of the result of voting by the voting unit.

12. The apparatus according to claim 1 ,
wherein the recognizing unit acquires, for each of the obtained categories, a score of a peak position in the distribution of the result of voting by the voting unit, recognizes one of the obtained categories that corresponds to a voting surface having a peak position with the highest score as the category of the target object, and recognizes the peak position with the highest score as the position of the target object.

13. The apparatus according to claim 1 , wherein the voting surface is formed with a multi-dimension table.

14. The apparatus according to claim 13 ,
wherein the voting surface is formed with a two-dimension table having elements corresponding to the respective pixels of the target image.

15. The apparatus according to claim 1 ,
wherein the holding unit holds a plurality of the dictionaries,
wherein the third obtaining unit obtains from each of the plurality of dictionaries, for each of the plurality of partial target images, the corresponding category and position information of the partial learning image, and
wherein the voting unit votes, for each of the plurality of partial target images, the result obtained by the third obtaining unit at a position indicated by the position information in a voting surface corresponding to the every obtained category.

16. The apparatus according to claim 1 ,
wherein the third obtaining unit obtains the corresponding category and position information of the partial learning image as a set.

17. The apparatus according to claim 1 , wherein each of the plurality of pixel values indicates a luminance value.

18. The apparatus according to claim 1 , wherein the acquiring unit is configured to acquire, from each of the plurality of partial target images obtained by the second obtaining unit, a plurality of pixel values of positions identical to the positions where the plurality of pixel values are obtained from each of the partial learning images.

19. The apparatus according to claim, 1 , wherein an acquiring processing by the acquiring unit and a comparing processing by the comparing unit are repeated.

20. The apparatus according to claim 19 , wherein the positions where the plurality of pixel values are obtained from each of the partial target images can be changed for each time a process including the acquiring processing and the comparing processing is repeated.

21. A method for recognizing a category and a position of a target object included in an image, comprising:
holding a dictionary in which, for each of a plurality of partial learning images each of which is part of a learning image for recognizing the target object, a category of the partial learning image and position information of the partial learning image are registered, the category and the position information of the partial learning image being classified based on a comparison result of pixel values obtained from a plurality of pixels in the partial learning image and the position information representing information on a relative position between the target object and the partial learning image in the learning image;
obtaining the target image including the target object which belongs to at least one category of a plurality of categories;
obtaining a plurality of partial target images from the obtained target image;
acquiring a plurality of pixel values, from each of the obtained plurality of partial target images, of positions corresponding to the positions where the plurality of pixel values are obtained from each of the partial learning images;
comparing the plurality of acquired pixel values to each other;
obtaining from the dictionary, for each of the plurality of partial target images, a corresponding category and the position information of the partial learning image, based on a result of the comparison;
voting, for each of the plurality of partial target images, the obtained result at a position indicated by the obtained position information by the in a voting surface corresponding to the obtained

Assignees

• Yoshii Hiroto
• Matsugu Masakazu
• Canon Kk

Inventors

• Yoshii Hiroto
• Matsugu Masakazu

Classifications

• G06V10/772

  Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries · CPC title

• G06V10/809

  of classification results, e.g. where the classifiers operate on the same input data · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06F18/24323

  Tree-organised classifiers · CPC title

• G06F18/254

  of classification results, e.g. of results related to same input data · CPC title