Abstract: Some embodiments provide a file system for purging content based on a single traversal of the file system that identifies the directory containing the content without performing a secondary traversal within the directory to target the operation to only the file that are associated with content such that other files contained in the directory are unaffected. The file system supplements traditional directory structures with file-level directories. Each file-level directory is created to contain a root file associated with particular content, different variants of the particular content, and supporting files. Consequently, the file system can complete an operation targeting particular content by performing that operation on the file-level directory containing the particular content, thereby eliminating the need to conduct a file-by-file traversal of the containing directory as a prerequisite to identifying the files associated with the particular content and performing the operation on the files individually.

Abstract: Some embodiments provide instantaneous and non-blocking content purging across storage servers of a distributed platform. When a server receives a purge operation, it extracts an identifier from the purge operation. The server then generates a content purge pattern from the identifier and injects the pattern to its configuration. Instantaneous purging is then realized as the server averts access to any cached content identified by the pattern. The purging also occurs in a non-blocking fashion as the physical purge of the content occurs in-line with the server's cache miss operation. The content purge pattern causes the server to respond to a subsequently received content request with a cache miss, whereby the server retrieves the requested content from an origin source, serves the retrieved content to the requesting user, and replaces a previously cached copy of the content that is to be purged with the newly retrieved copy.

Abstract: A specific item within an item class is identified by defining sets of descriptor data from a training library. The collected descriptor data is grouped and organized into a hierarchical tree, where each leaf node is defined by relations between corresponding parts of the descriptor data. Registrable sets of descriptor data are then identified from a collection of registrable samples. The registrable sets of descriptors are sorted into the hierarchical tree. When an input sample to be identified is received, a test set of descriptor data is generated from the input sample. The test set is then sorted into the hierarchical tree. Each leaf node that receives a part of the test set provides a vote for the registered samples it contains. The registered sample with the most votes is deemed a match for the input sample.

Abstract: New views of a 2D image are generated by identifying an object class within the image, such as through a face detector. The face is then fitted to a model face by means of an AAM, and the results extended to a fitted 3D polygon mesh face. A boundary perimeter with predefined anchor points and a predefined triangulation with the 3D polygon mesh is defined a predefined depth distance from the depth center of known landmarks within the 3D polygon mesh face. By rotating the 3D polygon mesh face relative to the boundary perimeter, which may follow the perimeter of the input image, new views of the input image are generated.

Abstract: Some embodiments provide a file system for purging content based on a single traversal of the file system that identifies the directory containing the content without performing a secondary traversal within the directory to target the operation to only the file that are associated with content such that other files contained in the directory are unaffected. The file system supplements traditional directory structures with file-level directories. Each file-level directory is created to contain a root file associated with particular content, different variants of the particular content, and supporting files. Consequently, the file system can complete an operation targeting particular content by performing that operation on the file-level directory containing the particular content, thereby eliminating the need to conduct a file-by-file traversal of the containing directory as a prerequisite to identifying the files associated with the particular content and performing the operation on the files individually.

Abstract: An output image of higher resolution than an input image is constructed by using a low resolution (LR) dictionary of triangle data entries, each having a one-to-one correlation with a high resolution (HR) data entry in an HR dictionary of triangle data entries. The input image is triangularized, and the closest matching LR data entry in the LR dictionary for each triangle in the triangularized input image is identified. The HR data entry correlated to each identified matching LR data entry is then used to construct the output image by wrapping the correlated HR data entry onto the corresponding triangle on the triangularized input image.

Abstract: In a face recognition system, overlapping patches are defined on a canonical face. Random clusters of pixel pairs are defined within each patch, and binary features are determined for each pixel pair by comparing their respective feature values. An inverted index hash table is constructed of the binary features. Similar binary features are then determined on a library of registrable samples of identified faces. A log probability of each registrable sample generating a binary feature from a corresponding cluster of pixel pairs at each specific patch location is determined and stored in the hash table. In a search phase, similar binary features are determined, and a hash key is determined for each binary feature. The log probabilities for each identity found in the hash table are summed for all clusters of pixel pairs and locations and sorted to find the high probability match.

Abstract: A specific item within an item class is identified by defining sets of descriptor data from a training library. The collected descriptor data is grouped and organized into a hierarchical tree, where each leaf node is defined by relations between corresponding parts of the descriptor data. Registrable sets of descriptor data are then identified from a collection of registrable samples. The registrable sets of descriptors are sorted into the hierarchical tree. When an input sample to be identified is received, a test set of descriptor data is generated from the input sample. The test set is then sorted into the hierarchical tree. Each leaf node that receives a part of the test set provides a vote for the registered samples it contains. The registered sample with the most votes is deemed a match for the input sample.

Abstract: In a face recognition system, overlapping patches are defined on a canonical face. Random clusters of pixel pairs are defined within each patch, and binary features are determined for each pixel pair by comparing their respective feature values. An inverted index hash table is constructed of the binary features. Similar binary features are then determined on a library of registrable samples of identified faces. A log probability of each registrable sample generating a binary feature from a corresponding cluster of pixel pairs at each specific patch location is determined and stored in the hash table. In a search phase, similar binary features are determined, and a hash key is determined for each binary feature. The log probabilities for each identity found in the hash table are summed for all clusters of pixel pairs and locations and sorted to find the high probability match.

Abstract: New views of a 2D image are generated by identifying an object class within the image, such as through a face detector. The face is then fitted to a model face by means of an AAM, and the results extended to a fitted 3D polygon mesh face. A boundary perimeter with predefined anchor points and a predefined triangulation with the 3D polygon mesh is defined a predefined depth distance from the depth center of known landmarks within the 3D polygon mesh face. By rotating the 3D polygon mesh face relative to the boundary perimeter, which may follow the perimeter of the input image, new views of the input image are generated.

Abstract: An output image of higher resolution than an input image is constructed by using a low resolution (LR) dictionary of triangle data entries, each having a one-to-one correlation with a high resolution (HR) data entry in an HR dictionary of triangle data entries. The input image is triangularized, and the closest matching LR data entry in the LR dictionary for each triangle in the triangularized input image is identified. The HR data entry correlated to each identified matching LR data entry is then used to construct the output image by wrapping the correlated HR data entry onto the corresponding triangle on the triangularized input image.

Abstract: An Active Appearance Model, AAM, uses an L1 minimization-based approach to aligning an input test image. In each iterative application of its statistical model fitting function, a shape parameter coefficient p and an appearance parameter coefficient ? within the statistical model fitting function are updated by L1 minimization. The AAM further includes a canonical classifier to determine if an aligned image is a true example of the class of object being sought before the AAM is permitted to output its aligned image.

Abstract: An active appearance model is built by arranging the training images in its training library into a hierarchical tree with the training images at each parent node being divided into two child nodes according to similarities in characteristic features. The number of node levels is such that the number of training images associated with each leaf node is smaller than a predefined maximum. A separate AAM, one per leaf node, is constructed using each leaf node's corresponding training images. In operation, starting at the root node, a test image is compared with each parent node's two child nodes and follows a node-path of model images that most closely matches the test image. The test image is submitted to an AAM selected for being associated with the leaf node at which the test image rests. The selected AAM's output aligned image may be resubmitted to the hierarchical tree if sufficient alignment is not achieved.

Abstract: Aspects of the present invention include systems and methods for forming generative models, for utilizing those models, or both. In embodiments, an object model fitting system can be developed comprising a 3D active appearance model (AAM) model. The 3D AAM comprises an appearance model comprising a set of subcomponent appearance models that is constrained by a 3D shape model. In embodiments, the 3D AAM may be generated using a balanced set of training images. The object model fitting system may further comprise one or more manifold constraints, one or more weighting factors, or both. Applications of the present invention include, but are not limited to, modeling and/or fitting face images, although the teachings of the present invention can be applied to modeling/fitting other objects.

Abstract: Aspects of the present invention include systems and methods for forming generative models, for utilizing those models, or both. In embodiments, an object model fitting system can be developed comprising a 3D active appearance model (AAM) model. The 3D AAM comprises an appearance model comprising a set of subcomponent appearance models that is constrained by a 3D shape model. In embodiments, the 3D AAM may be generated using a balanced set of training images. The object model fitting system may further comprise one or more manifold constraints, one or more weighting factors, or both. Applications of the present invention include, but are not limited to, modeling and/or fitting face images, although the teachings of the present invention can be applied to modeling/fitting other objects.

Abstract: An active appearance model is built by arranging the training images in its training library into a hierarchical tree with the training images at each parent node being divided into two child nodes according to similarities in characteristic features. The number of node levels is such that the number of training images associated with each leaf node is smaller than a predefined maximum. A separate AAM, one per leaf node, is constructed using each leaf node's corresponding training images. In operation, starting at the root node, a test image is compared with each parent node's two child nodes and follows a node-path of model images that most closely matches the test image. The test image is submitted to an AAM selected for being associated with the leaf node at which the test image rests. The selected AAM's output aligned image may be resubmitted to the hierarchical tree if sufficient alignment is not achieved.

Abstract: Aspects of the present invention include systems and methods for forming generative models, for utilizing those models, or both. In embodiments, an object model fitting system can be developed comprising a 3D active appearance model (AAM) model. The 3D AAM comprises an appearance model comprising a set of subcomponent appearance models that is constrained by a 3D shape model. In embodiments, the 3D AAM may be generated using a balanced set of training images. The object model fitting system may further comprise one or more manifold constraints, one or more weighting factors, or both. Applications of the present invention include, but are not limited to, modeling and/or fitting face images, although the teachings of the present invention can be applied to modeling/fitting other objects.

Abstract: Aspects of the present invention include systems and methods for forming generative models, for utilizing those models, or both. In embodiments, an object model fitting system can be developed comprising a 3D active appearance model (AAM) model. The 3D AAM comprises an appearance model comprising a set of subcomponent appearance models that is constrained by a 3D shape model. In embodiments, the 3D AAM may be generated using a balanced set of training images. The object model fitting system may further comprise one or more manifold constraints, one or more weighting factors, or both. Applications of the present invention include, but are not limited to, modeling and/or fitting face images, although the teachings of the present invention can be applied to modeling/fitting other objects.

Abstract: An Active Appearance Model, AAM, uses an L1 minimization-based approach to aligning an input test image. In each iterative application of its statistical model fitting function, a shape parameter coefficient p and an appearance parameter coefficient ? within the statistical model fitting function are updated by L1 minimization. The AAM further includes a canonical classifier to determine if an aligned image is a true example of the class of object being sought before the AAM is permitted to output its aligned image.

Abstract: An Active Appearance Model AAM is trained using expanded library having examples of true outlier images. The AAM creates a first statistical fitting pair (a model image of the class of object and corresponding statistical model fitting) using characteristic features drawn only from the expanded library. All images within the expanded library that the first statistical fitting pair cannot align are collected into a smaller, second library of true outlier cases. A second statistical fitting pair is created using characteristic features drawn only from the second library, and samples not aligned by the second statistical fitting pair are collected into a still smaller, third library. This process is repeated until a desired percentage of all the images within the initial, expanded library have been aligned. In operation, the AAM applies each of its created statistical fitting pairs, in turn, until it has successfully aligned a submitted test image, or until a stop criterion has been reached.