Abstract: A method for determining a high-resolution depth map of a scene, the method comprising: obtaining a low-resolution depth map of the scene, obtaining a high-resolution image of the scene, initializing an estimated reflectance map, an estimated lighting vector and an estimated depth map, wherein the estimated depth map is in high-resolution, iteratively simultaneously updating the estimated reflectance map, the estimated lighting vector, and the estimated depth-map, wherein updating the estimated depth map is partially based on the high-resolution image, and determining the high-resolution depth map based on the iteratively updated estimated depth-map.

Abstract: A method for histogram calculation using a graphics processing unit (GPU), comprises storing image data in a two-dimensional (2D) texture domain; subdividing the domain into independent regions or tiles; calculating in parallel, in a GPU, a plurality of tile histograms, one for each tile; and summing up in parallel, in the GPU, the tile histograms so as to derive a final image histogram.

Abstract: A method for detecting and tracking a deformable object having a sequentially changing behavior, comprising: developing a temporal statistical shape model of the oscillatory behavior of the embedding function representing the object from prior motion; and then applying the model against future, sequential motion of the object in the presence of unwanted phenomena by maximizing the probability that the developed statistical shape model matches the sequential motion of the object in the presence of unwanted phenomena.

Abstract: Methods and systems for image segmentation are disclosed. A nonlinear statistical shape model of an image is integrated with a non-parametric intensity model to estimate characteristics of an image and create segmentations of an image based on Bayesian inference from characteristics of prior learned images based on the same models.

Abstract: A method for segmentation of an image interactively with a user utilizes level set segmentation and includes selecting by user input respective areas of object and of background; initializing an embedding function implementing a segmentation boundary according to the selecting; computing intensity distributions and for the respective areas of object and of background; and performing repeatedly the steps below until convergence is reached: (a) evolving the embedding function, (b) recomputing the intensity distributions, and (c) checking for new user input and, if so: (d) updating labeling of the areas of object and background.

Abstract: Methods of statistical learning for Bayesian inference in the context of efficient optimization schemes for image restoration are presented. Second and third order priors that may be learned while maintaining graph representability are identified. A framework to learn and impose prior knowledge on the distribution of pairs and triplets of labels via graph cuts is presented. The disclosed methods optimally restore binary textures from very noisy images with runtimes in the order of seconds while imposing hundreds of statistically learned constraints per node.

Abstract: A method for segmentation of an image interactively with a user utilizes level set segmentation and includes selecting by user input respective areas of object and of background; initializing an embedding function implementing a segmentation boundary according to the selecting; computing intensity distributions and for the respective areas of object and of background; and performing repeatedly the steps below until convergence is reached: (a) evolving the embedding function, (b) recomputing the intensity distributions, and (c) checking for new user input and, if so: (d) updating labeling of the areas of object and background.

Abstract: A method for image registration includes receiving first and second image information. A library of joint intensity distributions, spanning a space of non-parametric statistical priors, derived from earlier perfect matching results is received. From among this library, a preferred learned joint intensity distribution is automatically selected during the registration process. As a result, a displacement field is generated both (i) maximizing the statistical dependency between an intensity distribution of the first and second image information and (ii) minimizing the statistical distance to the learned joint intensity distributions. The generated displacement field is used to transform an image structure from the first image information to an image structure of the second image information.

Abstract: A method for histogram calculation using a graphics processing unit (GPU), comprises storing image data in a two-dimensional (2D) texture domain; subdividing the domain into independent regions or tiles; calculating in parallel, in a GPU, a plurality of tile histograms, one for each tile; and summing up in parallel, in the GPU, the tile histograms so as to derive a final image histogram.

Abstract: A method for detecting and tracking a deformable object having a sequentially changing behavior, comprising: developing a temporal statistical shape model of the oscillatory behavior of the embedding function representing the object from prior motion; and then applying the model against future, sequential motion of the object in the presence of unwanted phenomena by maximizing the probability that the developed statistical shape model matches the sequential motion of the object in the presence of unwanted phenomena.

Abstract: Methods of statistical learning for Bayesian inference in the context of efficient optimization schemes for image restoration are presented. Second and third order priors that may be learned while maintaining graph representability are identified. A framework to learn and impose prior knowledge on the distribution of pairs and triplets of labels via graph cuts is presented. The disclosed methods optimally restore binary textures from very noisy images with runtimes in the order of seconds while imposing hundreds of statistically learned constraints per node.

Abstract: Methods and systems for image segmentation are disclosed.