System and method for attentional selection

• US 20050047647A1
• Filed: 06/10/2004
• Published: 03/03/2005
• Est. Priority Date: 06/10/2003
• Status: Abandoned Application

• Current Assignee: California Institute of Technology
• Original Assignee: California Institute of Technology
• Inventors: Perona, Pietro, Walther, Dirk, Rutishauser, Ueli, Koch, Christof
• Application Number:
  US10/866,311
• Publication Number:
  US 20050047647A1
• Time in Patent Office: Days
• US Class Current: 382/159
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 10/454 Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
• CPC Class Codes: G06V 10/462 Salient features, e.g. scale invariant feature transforms [SIFT]

Abstract

The present invention relates to a system and method for attentional selection. More specifically, the present invention relates to a system and method for the automated selection and isolation of salient regions likely to contain objects, based on bottom-up visual attention, in order to allow unsupervised one-shot learning of multiple objects in cluttered images.

Claims

1. A method for learning and recognizing objects comprising acts of:

receiving an input image;

automatedly identifying a salient region of the input image; and

automatedly isolating the salient region of the input image, resulting in an isolated salient region.

2. The method of claim 1, wherein the act of automatedly identifying comprises acts of:

receiving a most salient location associated with a saliency map;

determining a conspicuity map that contributed most to activity at the winning location;

providing a conspicuity location on the conspicuity map that corresponds to the most salient location;

determining a feature map that contributed most to activity at the conspicuity location;

providing a feature location on the feature map that corresponds to the conspicuity location; and

segmenting the feature map around the feature location resulting in a segmented feature map.

3. The method of claim 2, wherein the act of automatedly isolating comprises acts of:

generating a mask based on the segmented feature map, and

modulating the contrast of the input image in accordance with the mask, resulting in a modulated input image.

4. The method of claim 2, further comprising an act of:

displaying the modulated input image to a user.

5. The method of claim 2, further comprising acts of:

identifying most active coordinates in the segmented feature map which are associated with the feature location;

translating the most active coordinates in the segmented feature map to related coordinates in the saliency map; and

blocking the related coordinates in the saliency map from being declared the most salient location,

whereby a new most salient location is identified.

6. The method of claim 5, wherein the acts of claim 1 are repeated with the new most salient location.

7. The method of claim 1 further comprising an act of:

providing the isolated salient region to a recognition system,

whereby the recognition system either performs an act selected from the group comprising of;

identifying an object within the isolated salient region and learning an object within the isolated salient region.

8. The method of claim 7 further comprising an act of:

providing the object learned by the recognition system to a tracking system.

9. The method of claim 7 further comprising an act of:

displaying the object learned by the recognition system to a user.

10. The method of claim 8 further comprising an act of:

displaying the object identified by the recognition system to a user.

11. A computer program product for learning and recognizing objects, the computer program product comprising computer-executable instructions, stored on a computer-readable medium for causing operations to be performed, for:

receiving an input image;

automatedly identifying a salient region of the input image; and

automatedly isolating the salient region of the input image, resulting in an isolated salient region.

12. A computer program product as set forth in claim 11, further comprising computer-executable instructions, stored on a computer-readable medium for causing, in the act of automatedly identifying, operations of:

receiving a most salient location associated with a saliency map;

determining a conspicuity map that contributed most to activity at the winning location;

providing a conspicuity location on the conspicuity map that corresponds to the most salient location;

determining a feature map that contributed most to activity at the conspicuity location;

providing a feature location on the feature map that corresponds to the conspicuity location; and

segmenting the feature map around the feature location resulting in a segmented feature map.

13. A computer program product as set forth in claim 12, wherein the computer-executable instructions for causing the operations of automatedly isolating are further configured to cause operations of:

generating a mask based on the segmented feature map, and

modulating the contrast of the input image in accordance with the mask, resulting in a modulated input image.

14. A computer program product as set forth in claim 12, further comprising computer-executable instructions for causing the operation of:

displaying the modulated input image to a user.

15. A computer program product as set forth in claim 12, further comprising computer-executable instructions for causing the operation of:

identifying most active coordinates in the segmented feature map which are associated with the feature location;

translating the most active coordinates in the segmented feature map to related coordinates in the saliency map; and

blocking the related coordinates in the saliency map from being declared the most salient location,

whereby a new most salient location is identified.

16. A computer program product as set forth in claim 15, wherein the computer-executable instructions are configured to repeat the operations of claim 11 with the new most salient location.

17. A computer program product as set forth in claim 11, further comprising computer-executable instructions for causing the operations of:

providing the isolated salient region to a recognition system,

whereby the recognition system either performs an act selected from the group comprising of;

identifying an object within the isolated salient region and learning an object within the isolated salient region.

18. A computer program product as set forth in claim 17, further comprising computer-executable instructions for causing the operations of:

providing the object learned by the recognition system to a tracking system.

19. A computer program product as set forth in claim 17, further comprising computer-executable instructions for causing the operations of:

displaying the object learned by the recognition system to a user.

20. A computer program product as set forth in claim 18, further comprising computer-executable instructions for causing the operations of:

displaying the object identified by the recognition system to a user.

21. A data processing system for the learning and recognizing of objects, comprising a data processor, having computer-executable instructions incorporated therein, for causing the data processor to perform operations, for:

receiving an input image;

automatedly identifying a salient region of the input image; and

automatedly isolating the salient region of the input image, resulting in an isolated salient region.

22. A data processing system for the learning and recognizing of objects as in claim 21, comprising a data processor, having computer-executable instructions incorporated therein, for causing the data processor, in the act of automatedly identifying, to perform operations of:

receiving a most salient location associated with a saliency map;

determining a conspicuity map that contributed most to activity at the winning location;

providing a conspicuity location on the conspicuity map that corresponds to the most salient location;

determining a feature map that contributed most to activity at the conspicuity location;

providing a feature location on the feature map that corresponds to the conspicuity location; and

segmenting the feature map around the feature location resulting in a segmented feature map.

23. A data processing system for the learning and recognizing of objects as in claim 22, comprising a data processor, having computer-executable instructions incorporated therein, for causing the data processor, in the act of automatedly isolating, to perform operations of:

generating a mask based on the segmented feature map, and

modulating the contrast of the input image in accordance with the mask, resulting in a modulated input image.

24. A data processing system for the learning and recognizing of objects as in claim 22, comprising a data processor, having computer-executable instructions incorporated therein, for causing the data processor to perform operations of:

displaying the modulated input image to a user.

25. A data processing system for the learning and recognizing ofobjects as in claim 22, comprising a data processor, having computer-executable instructions incorporated therein, for causing the data processor to perform operations of:

identifying most active coordinates in the segmented feature map which are associated with the feature location;

translating the most active coordinates in the segmented feature map to related coordinates in the saliency map; and

blocking the related coordinates in the saliency map from being declared the most salient location,

whereby a new most salient location is identified.

26. A data processing system for the learning and recognizing of objects as in claim 25, comprising a data processor, having computer-executable instructions incorporated therein, which are configured to repeat the operations of claim 21 with the new most salient location.

27. A data processing system for the learning and recognizing of objects as in claim 21, comprising a data processor, having computer-executable instructions incorporated therein, for causing the data processor to perform operations of:

providing the isolated salient region to a recognition system,

whereby the recognition system either performs an act selected from the group comprising of;

identifying an object within the isolated salient region and learning an object within the isolated salient region.

28. A data processing system for the learning and recognizing of objects as in claim 27, comprising a data processor, having computer-executable instructions incorporated therein, for causing the data processor to perform operations of:

providing the object learned by the recognition system to a tracking system.

29. A data processing system for the learning and recognizing of objects as in claim 27, comprising a data processor, having computer-executable instructions incorporated therein, for causing the data processor to perform operations of:

displaying the object learned by the recognition system to a user.

30. A data processing system for the learning and recognizing of objects as in claim 28, comprising a data processor, having computer-executable instructions incorporated therein, for causing the data processor to perform operations of:

displaying the object identified by the recognition system to a user.