Image processing apparatus, image processing method, and program

An image processing method comprising:

• generating, with respect to a plurality of feature points to be detected from a first image, a first local feature amount group including local feature amounts representing feature amounts of a plurality of local regions containing the respective feature points;
• generating one or more first coordinate position information groups which include coordinate position information;
• calculating a correspondence information group representing a correlation between a correlation between the feature points of the first image and feature points of a second image based on an inter-feature amount distance between the first local feature amount group and second local feature amount groups which is formed from local feature amounts of feature points detected from the second images;
• clustering the feature points of the first image based on a coordinate position of a predefined reference point, in the first image, of the second image, the coordinate position being estimated based on a relative coordinate position of each of the feature points of the second image and the reference point, the correspondence information groups, and the first coordinate position information groups;
• dividing the first image into regions in accordance with the result of the clustering;
• collating the first local feature amount groups for the respective regions of the first image with the second local feature amount group from the second image;
• identifying different subjects within the first and second images based on the collated first and second local feature amount groups;
• and
• calculating a rotation amount of a subject of the first image by using the first coordinate position information groups, the second coordinate position information groups, and the correspondence information groups.

A non-transitory computer readable storage medium storing a program for causing an image processing apparatus to execute:

• generating, with respect to a plurality of feature points to be detected from a first image, a first local feature amount group including local feature amounts representing feature amounts of a plurality of local regions containing the respective feature points;
• generating one or more first coordinate position information groups which include coordinate position information;
• calculating a correspondence information group representing a correlation between a correlation between the feature points of the first image and feature points of a second image based on an inter-feature amount distance between the first local feature amount group and second local feature amount groups which is formed from local feature amounts of feature points detected from the second images;
• clustering the feature points of the first image based on a coordinate position of a predefined reference point, in the first image, of the second image, the coordinate position being estimated based on a relative coordinate position of each of the feature points of the second image and the reference point, the correspondence information groups, and the first coordinate position information groups;
• dividing the first image into regions in accordance with the result of the clustering;
• collating, the first local feature amount groups for the respective regions of the first image with the second local feature amount group from the second image;
• identifying different subjects within the first and second images based on the collated first and second local feature amount groups;
• and
• calculating a rotation amount of a subject of the first image by using the first coordinate position information groups, the second coordinate position information groups, and the correspondence information groups.