Detection of objects in an image using self similarities
First Claim
1. An image processor for detection of an object in an image or sequence of images, each image being formed of pixels, and the image processor comprising:
- a window selector for choosing a detection window within the image,a self-similarity computation part configured to determine similarity information for a group of the pixels in any part of the detection window, to represent an amount of self-similarity of that group to other groups in every other part of the detection window, and for repeating the determination for groups in all parts of the detection window, to generate a global self-similarity descriptor for the chosen detection window, wherein the self-similarity computation part is configured to determine a plurality of similarity values, each similarity value being based on a comparison of self-similarity information for a first group of the pixels with a second group of the pixels, and wherein the plurality of similarity values are comprised in the global self-similarity descriptor;
a classifier configured to classify whether the object is present in the detection window of the image from the global self-similarity descriptor for that detection window; and
a color distribution part arranged to determine a distribution of colors of the pixels of the groups,wherein the self-similarity information comprises an amount of self-similarity of the color distributions, andwherein the self-similarity computation part comprises;
a histogram generator arranged to determine a histogram of values for a feature of pixels in the group, by using interpolation; and
a histogram similarity part arranged to determine similarities between histograms for different groups of pixels in the detection window by a histogram intersection.
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Accused Products
Abstract
An image processor (10) has a window selector for choosing a detection window within the image, and a self similarity computation part (40) for determining self-similarity information for a group of the pixels in any part of the detection window, to represent an amount of self-similarity of that group to other groups in any other part of the detector window, and for repeating the determination for groups in all parts of the detection window, to generate a global self similarity descriptor for the detection window. A classifier (50) is used for classifying whether an object is present based on the global self-similarity descriptor. By using global self-similarity rather than local similarities more information is captured which can lead to better classification. In particular, it helps enable recognition of more distant self-similarities inherent in the object, and self-similarities present at any scale.
27 Citations
11 Claims
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1. An image processor for detection of an object in an image or sequence of images, each image being formed of pixels, and the image processor comprising:
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a window selector for choosing a detection window within the image, a self-similarity computation part configured to determine similarity information for a group of the pixels in any part of the detection window, to represent an amount of self-similarity of that group to other groups in every other part of the detection window, and for repeating the determination for groups in all parts of the detection window, to generate a global self-similarity descriptor for the chosen detection window, wherein the self-similarity computation part is configured to determine a plurality of similarity values, each similarity value being based on a comparison of self-similarity information for a first group of the pixels with a second group of the pixels, and wherein the plurality of similarity values are comprised in the global self-similarity descriptor; a classifier configured to classify whether the object is present in the detection window of the image from the global self-similarity descriptor for that detection window; and a color distribution part arranged to determine a distribution of colors of the pixels of the groups, wherein the self-similarity information comprises an amount of self-similarity of the color distributions, and wherein the self-similarity computation part comprises; a histogram generator arranged to determine a histogram of values for a feature of pixels in the group, by using interpolation; and a histogram similarity part arranged to determine similarities between histograms for different groups of pixels in the detection window by a histogram intersection. - View Dependent Claims (2, 3, 4, 5, 6, 11)
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7. A method of using an image processor for detection of an object in an image or sequence of images, each image being formed of pixels, and the method having the steps of:
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choosing a detection window within the image, determining self-similarity information for a group of the pixels in every part of the detection window, to represent an amount of self-similarity of that group to other groups in every other part of the detection window; comparing self-similarity information for a first group of pixels to self-similarity information of a second group of pixels to determine a similarity value based on a result of the comparing; repeating the determination and comparing for groups in all parts of the detection window, to generate a global self-similarity descriptor for the chosen detection window including the determined similarity values; classifying whether the object is present in the detection window of the image from the global self-similarity descriptor for that detection window; and determining a distribution of colors of the pixels of the groups, wherein the self-similarity information comprises an amount of self-similarity of the color distributions, and wherein the determining comprises; determining a histogram of values for a feature of pixels in the group, by using interpolation; and determining similarities between histograms for different groups of pixels in the detection window by a histogram intersection. - View Dependent Claims (8, 9, 10)
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Specification