METHOD FOR DETECTING PARTICULAR OBJECT FROM IMAGE AND APPARATUS THEREOF

• US 20100014758A1
• Filed: 07/14/2009
• Published: 01/21/2010
• Est. Priority Date: 07/15/2008
• Status: Abandoned Application

• Current Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Original Assignee: Canon Kabushiki Kaisha (Canon Inc.)
• Inventors: Ito, Yasuhiro, Yano, Kotaro
• Application Number:
  US12/502,921
• Publication Number:
  US 20100014758A1
• Time in Patent Office: Days
• US Class Current: 382/195
• CPC Class Codes: G06F 18/2148 characterised by the process organisation or structure, e.g. boosting cascade
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06V 10/25 Determination of region of interest [ROI] or a volume of interest [VOI]
• CPC Class Codes: G06V 10/7747 Organisation of the process, e.g. bagging or boosting
• CPC Class Codes: G06V 10/809 of classification results, e.g. where the classifiers operate on the same input data
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation

Abstract

When discriminating a plurality of types of objects, a plurality of local feature quantities are extracted from local regions in an image, and positions of the local regions, and attributes according to image characteristics of the local feature quantities are stored in correspondence with each other. Then, object likelihoods with respect to a plurality of objects are determined from attributes of feature quantities in a region-of-interest, an object whose object likelihood is not less than a threshold value is determined as an object candidate, and whether an object candidate is a predetermined object is determined.

Claims

1. An image processing apparatus comprising:

a first derivation unit configured to derive feature quantities in a plurality of local regions in an image;

an attribute discrimination unit configured to discriminate respective attributes of the derived feature quantities according to characteristics of the feature quantities;

a region setting unit configured to set a region-of-interest in the image;

a second derivation unit configured to discriminate attributes of the feature quantities contained in the region-of-interest based on the attributes discriminated by the attribute discrimination unit, and to derive likelihoods with respect to a predetermined plurality of types of objects in the region-of-interest according to discriminated attributes;

a dictionary selection unit configured to select a dictionary, from among a plurality of dictionaries set in advance, which represents a feature quantity specific to the object, according to the derived likelihoods; and

an object discrimination unit configured to discriminate objects in the region-of-interest, based on the feature quantity specific to the object extracted from the selected dictionary, and the feature quantities in the region-of-interest.

2. The image processing apparatus according to claim 1, further comprising a storage unit configured to store attributes of feature quantities derived by the first derivation unit, and positions of the local regions corresponding to the attributes in association with each other,

wherein the second derivation unit reads out attributes stored in the storage unit associated with positions of the region-of-interest, and derives likelihoods in the region-of-interest with respect to a predetermined plurality of types of objects, from the read out attributes.

3. The image processing apparatus according to claim 1, wherein the dictionary selection unit selects a dictionary corresponding to an object whose likelihood derived by the second derivation unit is not less than a threshold value, and

wherein the object discrimination unit discriminates an object whose likelihood derived by the second derivation unit in the region-of-interest is not less than the threshold value.

4. The image processing apparatus according to claim 1, further comprising a division unit configured to divide the images into a plurality of blocks,

wherein the first derivation unit derives feature quantities in the blocks divided by the division unit.

5. The image processing apparatus according to claim 1, further comprising a reduction unit configured to reduce the images by a predetermined scale factor,

wherein the first derivation unit derives feature quantities in a plurality of local regions in the images reduced by the reduction unit,

wherein the region setting unit sets a region-of-interest in the images reduced by the reduction unit.

6. The image processing apparatus according to claim 1, wherein the first derivation unit derives invariant feature quantities with respect to geometric transformations.

7. An image processing method comprising:

deriving, from a plurality of local regions in an image, feature quantities in the local regions;

discriminating respective attributes of the derived feature quantities, according to characteristics of the feature quantities;

setting a region-of-interest in the image;

discriminating attributes of feature quantities contained in the set region-of-interest, according to attributes of feature quantities in the local regions,

deriving likelihoods with respect to a predetermined plurality of types of objects in the region-of-interest from discriminated attributes;

selecting a dictionary that represents a feature quantity specific to the object, according to the derived likelihoods, from among a plurality of dictionaries set in advance with respect to objects; and

discriminating an object in the region-of-interest, based on the feature quantity specific to the object extracted from the selected dictionary that was set, and feature quantities in the region-of-interest.

8. A computer-readable storage medium that stores a program for instructing a computer to implement an image processing method, the method comprising:

deriving, from a plurality of local regions in an image, feature quantities in the local regions;

discriminating respective attributes of the derived feature quantities, according to characteristics of the feature quantities;

setting a region-of-interest in the image;

discriminating attributes of feature quantities contained in the set region-of-interest, according to attributes of feature quantities in the local regions,

deriving likelihoods with respect to a predetermined plurality of types of objects in the region-of-interest from discriminated attributes;

selecting a dictionary that represents a feature quantity specific to the object, according to the derived likelihoods, from among a plurality of dictionaries set in advance with respect to objects; and

discriminating an object in the region-of-interest, based on the feature quantity specific to object extracted from the selected dictionary that was set, and feature quantities in the region-of-interest.