SYSTEMS AND METHODS FOR ESTIMATION OF OBJECTS FROM AN IMAGE
First Claim
1. A method for estimating one or more semi-transparent objects from an image comprising:
- receiving an image having at least one object for estimation, the at least one object being semi-transparent and overlaid over at least one other object, the image having a plurality of pixels;
calculating a probability map of the at least one object, the probability map comprising a plurality of pixels corresponding to the plurality of pixels of the received image, wherein each probability map pixel has a value proportional to the probability that the pixel of the received image contains the at least one object;
calculating an approximation image of an object suppressed image based on the object probability map, wherein the approximation image is substantially equal to corresponding regions of the received image at portions with low probability values, and the approximation image denotes a smooth approximation of the image with the at least one object suppressed at portions with high probability values of the object probability map; and
calculating the at least one object for estimation based on the calculated approximation of the object suppressed image.
1 Assignment
0 Petitions
Accused Products
Abstract
There is provided a method for estimating semi-transparent object(s) from an image comprising: receiving an image having semi-transparent and overlaid object(s) for estimation; calculating a probability map of the object(s), the probability map comprising multiple pixels corresponding to the plurality of pixels of the received image, wherein each probability map pixel has a value proportional to the probability that the pixel of the received image contains the object(s); calculating an approximation image of an object suppressed image based on the object probability map, wherein the approximation image is substantially equal to corresponding regions of the received image at portions with low probability values, and the approximation image denotes a smooth approximation of the image with the object(s) suppressed at portions with high probability values of the object probability map; and calculating the object(s) for estimation based on the calculated approximation of the object suppressed image.
-
Citations
29 Claims
-
1. A method for estimating one or more semi-transparent objects from an image comprising:
-
receiving an image having at least one object for estimation, the at least one object being semi-transparent and overlaid over at least one other object, the image having a plurality of pixels; calculating a probability map of the at least one object, the probability map comprising a plurality of pixels corresponding to the plurality of pixels of the received image, wherein each probability map pixel has a value proportional to the probability that the pixel of the received image contains the at least one object; calculating an approximation image of an object suppressed image based on the object probability map, wherein the approximation image is substantially equal to corresponding regions of the received image at portions with low probability values, and the approximation image denotes a smooth approximation of the image with the at least one object suppressed at portions with high probability values of the object probability map; and calculating the at least one object for estimation based on the calculated approximation of the object suppressed image. - 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. A method for generating a trained regression function for use in a process to estimate semi-transparent objects from an image, comprising:
-
receiving a plurality of pairs of training images, each pair of training images comprises a training image with at least one object for estimation and an object image of the at least one object for estimation, the at least one object being semi-transparent and overlaid over at least one other object, wherein each of the pairs of training images comprise a plurality of pixels; and training a regression function to generate an object probability map for an acquired image with at least one semi-transparent object for estimation, the training based on the pairs of training images, the object probability map comprising a plurality of pixels corresponding to the plurality of pixels of the received image, wherein each probability map pixel has a value proportional to the probability that the pixel of the received image contains the at least one object.
-
-
25. A method for suppressing semi-transparent objects in an image comprising:
-
receiving an image having at least one object for suppression, the at least one object being semi-transparent and overlaid over at least one other object, the image having a plurality of pixels; receiving an object probability map, the object probability map is an object probability map comprising a plurality of pixels corresponding to the plurality of pixels of the received image, wherein each probability map pixel has a value proportional to the probability that the pixel of the received image is for suppression; receiving a difference image or calculating the difference image based on the received image and received object probability map, wherein regions of the difference image corresponding to at least one portion of the object probability map for suppression have substantially zero value, and regions of the difference image corresponding to portions other than the at least one portion of the object probability map for suppression have substantially equivalent value to the regions of the at least one object overlaid on fine background image details; identifying separate instances of the at least one object within the difference image based on the object probability map; dividing the identified instances of the at least one object within the difference image into segments; and calculating a generative model based on the segments, for generating an approximation image of the at least one object for suppression. - View Dependent Claims (26, 27, 28, 29)
-
Specification