Lempitsky
Victor Lempitsky, Cambridge GB
Patent application number | Description | Published |
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20100128984 | Labeling Image Elements - An image processing system is described which automatically labels image elements of a digital image. In an embodiment an energy function describing the quality of possible labelings of an image is globally optimized to find an output labeled image. In the embodiment, the energy function comprises terms that depend on at least one non-local parameter. For example, the non-local parameter describes characteristics of image elements having the same label. In an embodiment the global optimization is achieved in a practical, efficient manner by using a tree structure to represent candidate values of the non-local parameter and by using a branch and bound process. In some embodiments, the branch and bound process comprises evaluating a lower bound of the energy function by using a min-cut process. For example, the min-cut process enables the lower bound to be evaluated efficiently using a graphical data structure to represent the lower bound. | 05-27-2010 |
20100322525 | Image Labeling Using Multi-Scale Processing - Multi-scale processing may be used to reduce the memory and computational requirements of optimization algorithms for image labeling, for example, for object segmentation, 3D reconstruction, stereo correspondence, optical flow and other applications. For example, in order to label a large image (or 3D volume) a multi-scale process first solves the problem at a low resolution, obtaining a coarse labeling of an original high resolution problem. This labeling is refined by solving another optimization on a subset of the image elements. In examples, an energy function for a coarse level version of an input image is formed directly from an energy function of the input image. In examples, the subset of image elements may be selected using a measure of confidence in the labeling. | 12-23-2010 |
20110007933 | 3D Image Processing - Three dimensional image processing is described for example, for digital rendering of real-world objects, medical imaging applications and digital effects rendering. In an example, a 3D image processing apparatus takes a binary volume representation of a three dimensional image and forms an embedding function substantially consistent with the binary volume. In examples, the embedding function is smoothed by regularizing the at least second order derivatives and optimized using a convex optimization engine. In an embodiment the optimized embedding function is used to create a surface representation of the three dimensional object using an iso-surface extraction engine. In another embodiment the surface representation may be directly rendered on a display using volume rendering techniques. | 01-13-2011 |
Victor Lempitsky, Moscow RU
Patent application number | Description | Published |
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20080310743 | Optimizing Pixel Labels for Computer Vision Applications - Computer vision applications often require each pixel within an image to be assigned one of a set of labels. A method of improving the labels assigned to pixels is described which uses the quadratic pseudoboolean optimization (QPBO) algorithm. Starting with a partially labeled solution, an unlabeled pixel is assigned a value from a fully labeled reference solution and the energy of the partially labeled solution plus this additional pixel is calculated. The calculated energy is then used to generate a revised partially labeled solution using QPBO. | 12-18-2008 |
20090074292 | Optimization of Multi-Label Problems in Computer Vision - A method of labeling pixels in an image is described where the pixel label is selected from a set of three or more labels. The pixel labeling problem is reduced to a sequence of binary optimizations by representing the label value for each pixel as a binary word and then optimizing the value of each bit within the word, starting with the most significant bit. Data which has been learned from one or more training images is used in the optimization to provide information about the less significant bits within the word. | 03-19-2009 |
Victor Lempitsky, Oxford GB
Patent application number | Description | Published |
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20110164819 | Optimization of Multi-Label Problems in Computer Vision - A method of labeling pixels in an image is described where the pixel label is selected from a set of three or more labels. The pixel labeling problem is reduced to a sequence of binary optimizations by representing the label value for each pixel as a binary word and then optimizing the value of each bit within the word, starting with the most significant bit. Data which has been learned from one or more training images is used in the optimization to provide information about the less significant bits within the word. | 07-07-2011 |
20120207346 | Detecting and Localizing Multiple Objects in Images Using Probabilistic Inference - An object detection system is disclosed herein. The object detection system allows detection of one or more objects of interest using a probabilistic model. The probabilistic model may include voting elements usable to determine which hypotheses for locations of objects are probabilistically valid. The object detection system may apply an optimization algorithm such as a simple greedy algorithm to find hypotheses that optimize or maximize a posterior probability or log-posterior of the probabilistic model or a hypothesis receiving a maximal probabilistic vote from the voting elements in a respective iteration of the algorithm. Locations of detected objects may then be ascertained based on the found hypotheses. | 08-16-2012 |