Method and apparatus for image segmentation using Jensen-Shannon divergence and Jensen-Renyi divergence
First Claim
1. A method of identifying the boundary of an object in an image, the image being represented by a data set, the data set comprising a plurality of data elements, each data element having a data value corresponding to a feature of at least part of the image, the method comprising:
- adjusting a boundary estimate at least partially according to one of the group consisting of a Jensen-Shannon divergence value corresponding to the boundary estimate and a Jensen-Renyi divergence value corresponding to the boundary estimate such that the boundary estimate substantially matches the object boundary.
7 Assignments
0 Petitions
Accused Products
Abstract
A method of approximating the boundary of an object in an image, the image being represented by a data set, the data set comprising a plurality of data elements, each data element having a data value corresponding to a feature of the image, the method comprising determining which one of a plurality of contours most closely matches the object boundary at least partially according to a divergence value for each contour, the divergence value being selected from the group consisting of Jensen-Shannon divergence and Jensen-Renyi divergence. Each contour Ci defines a zone ZIi and a zone ZOi, ZIi representing the data elements inside the contour and ZOi representing the data elements outside the contour, each zone having a corresponding probability distribution of data values for the data elements therein, and wherein the divergence value for each contour Ci represents a measure of the difference between the probability distributions for the zones ZIi and ZOi. The boundary estimate is preferably a parametric contour. Further, the present invention supports the segmentation of multiple objects in a single data set simultaneously.
-
Citations
88 Claims
-
1. A method of identifying the boundary of an object in an image, the image being represented by a data set, the data set comprising a plurality of data elements, each data element having a data value corresponding to a feature of at least part of the image, the method comprising:
adjusting a boundary estimate at least partially according to one of the group consisting of a Jensen-Shannon divergence value corresponding to the boundary estimate and a Jensen-Renyi divergence value corresponding to the boundary estimate such that the boundary estimate substantially matches the object boundary. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
-
27. An apparatus for identifying the boundary of an object in an image, the image being represented by a data set, the data set comprising a plurality of data elements, each data element having a data value corresponding to a feature of at least part of the image, the apparatus comprising;
a processor configured to adjust a boundary estimate at least partially according to one of the group consisting of a Jensen-Shannon divergence value corresponding to the boundary estimate and a Jensen-Renyi divergence value corresponding to the boundary estimate such that the boundary estimate substantially matches the object boundary. - View Dependent Claims (28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57)
-
58. A method of approximating the boundary of an object in an image, the image being represented by a data set, the data set comprising a plurality of data elements, each data element having a data value corresponding to a feature of at least part of the image, the method comprising:
determining which one of a plurality of contours most closely matches the object boundary at least partially according to a divergence value for each contour, the divergence value being selected from the group consisting of Jensen-Shannon divergence and Jensen-Renyi divergence. - View Dependent Claims (59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71)
-
72. A method of segmenting an object from its background in an image, the image being represented by a data set, the data set comprising a plurality of data elements, each data element having a data value corresponding to a feature of at least part of the image, the method comprising:
-
associating the data elements of the data set with either a zone ZI or a zone ZO, ZI being an estimation of the object in the image, ZO being an estimation of the background in the image; and adjusting ZI and ZO at least partially according to one of the group consisting of a Jensen-Shannon divergence value and a Jensen-Renyi divergence value such that ZI substantially matches the object. - View Dependent Claims (73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83)
-
-
84. A method of segmenting an object in an image through optimization of a segmentation estimate, the image being represented by a data set, the data set comprising a plurality of pixels, each pixel having a value corresponding to a feature of at least part of the image, each pixel value being any member of the pixel value set X′
- , X′
comprising a plurality of possible pixel values x1 through xn, the segmentation estimate defining a zone ZI representing a set of pixels corresponding to the object and a zone ZO representing a set of pixels not corresponding to the object, wherein a plurality of pixels are located in ZI and a plurality of pixels are located in ZO, the method comprising using one of the group consisting of (1) a Jensen-Shannon divergence value representing the difference between a probability distribution p(X|ZI) and a probability distribution p(X|ZO) and (2) a Jensen-Renyi divergence value representing the difference between a probability distribution p(X|ZI) and a probability distribution p(X|ZO) as a criterion for optimizing the segmentation estimate to substantially match the object boundary. - View Dependent Claims (85, 86, 87, 88)
- , X′
Specification