Learning pixel visual context from object characteristics to generate rich semantic images
First Claim
1. A method comprising:
- dividing a digital image into tiles;
determining a degree of local contrast in each of the tiles;
selecting a first plurality of the tiles that exhibits a greatest degree of local contrast;
determining an average color of each of the first plurality of tiles;
dividing the first plurality of tiles into clusters of tiles with similar colors;
selecting a learning tile from each cluster of tiles, wherein each learning tile has the greatest degree of local contrast from among the tiles of the cluster to which the learning tile belongs;
segmenting the learning tiles into objects;
classifying the objects into classes of objects;
associating a color with each class of objects;
determining characteristics of the objects that belong to distinct classes of objects;
generating pixelwise descriptors that indicate the class of objects to which each pixel most probably belongs;
generating a pixel heat map without again segmenting the digital image into objects by applying the pixelwise descriptors to each pixel of the digital image, wherein each pixel of the digital image is assigned the color associated with the class of objects to which that pixel most probably belongs; and
displaying the pixel heat map on a graphical user interface.
1 Assignment
0 Petitions
Accused Products
Abstract
Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel.
-
Citations
7 Claims
-
1. A method comprising:
-
dividing a digital image into tiles; determining a degree of local contrast in each of the tiles; selecting a first plurality of the tiles that exhibits a greatest degree of local contrast; determining an average color of each of the first plurality of tiles; dividing the first plurality of tiles into clusters of tiles with similar colors; selecting a learning tile from each cluster of tiles, wherein each learning tile has the greatest degree of local contrast from among the tiles of the cluster to which the learning tile belongs; segmenting the learning tiles into objects; classifying the objects into classes of objects; associating a color with each class of objects; determining characteristics of the objects that belong to distinct classes of objects; generating pixelwise descriptors that indicate the class of objects to which each pixel most probably belongs; generating a pixel heat map without again segmenting the digital image into objects by applying the pixelwise descriptors to each pixel of the digital image, wherein each pixel of the digital image is assigned the color associated with the class of objects to which that pixel most probably belongs; and displaying the pixel heat map on a graphical user interface. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
Specification