Abstract: A computer-based method and system of distributing biological sample data acquired as a digital image of a subject biological sample. The acquired digital image and image capture data are processed according to at least one user. This results in processed image data and capture metadata. The processed image data represents biological sample data of the subject biological sample. A package processing combines the processed image data and capture metadata into a working Package. The method and system enables simultaneous electronic access to the working Package by multiple users, across multiple sectors, in addition to the one user.

Abstract: A model-based compression codec applies higher-level modeling to produce better predictions than can be found through conventional block-based motion estimation and compensation. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks and related to specific blocks of video data to be encoded. The tracking information is used to produce model-based predictions for those blocks of data, enabling more efficient navigation of the prediction search space than is typically achievable through conventional motion estimation methods. A hybrid framework enables modeling of data at multiple fidelities and selects the appropriate level of modeling for each portion of video data.

Abstract: A computer-based method and system of distributing biological sample data acquired as a digital image of a subject biological sample. The acquired digital image and image capture data are processed according to at least one user. This results in processed image data and capture metadata. The processed image data represents biological sample data of the subject biological sample. A package processing combines the processed image data and capture metadata into a working Package. The method and system enables simultaneous electronic access to the working Package by multiple users, across multiple sectors, in addition to the one user.

Abstract: A model-based compression codec applies higher-level modeling to produce better predictions than can be found through conventional block-based motion estimation and compensation. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks and related to specific blocks of video data to be encoded. The tracking information is used to produce model-based predictions for those blocks of data, enabling more efficient navigation of the prediction search space than is typically achievable through conventional motion estimation methods. A hybrid framework enables modeling of data at multiple fidelities and selects the appropriate level of modeling for each portion of video data.

Abstract: Systems and methods of improving video encoding/decoding efficiency may be provided. A feature-based processing stream is applied to video data having a series of video frames. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks, and each track is given a representative, characteristic feature. Similar characteristic features are clustered and then stored in a model library, for reuse in the compression of other videos. A model-based compression framework makes use of the preserved model data by detecting features in a new video to be encoded, relating those features to specific blocks of data, and accessing similar model information from the model library.

Abstract: A computer-based method and system of distributing biological sample data acquired as a digital image of a subject biological sample. The acquired digital image and image capture data are processed according to at least one user. This results in processed image data and capture metadata. The processed image data represents biological sample data of the subject biological sample. A package processing combines the processed image data and capture metadata into a working Package. The method and system enables simultaneous electronic access to the working Package by multiple users, across multiple sectors, in addition to the one user.

Abstract: A computer-based method and system of distributing biological sample data acquired as a digital image of a subject biological sample. The acquired digital image and image capture data are processed according to at least one user. This results in processed image data and capture metadata. The processed image data represents biological sample data of the subject biological sample. A package processing combines the processed image data and capture metadata into a working Package. The method and system enables simultaneous electronic access to the working Package by multiple users, across multiple sectors, in addition to the one user.

Abstract: Personal object based archival systems and methods are provided for processing and compressing video. By analyzing features unique to a user, such as face, family, and pet attributes associated with the user, an invariant model can be determined to create object model adapters personal to each user. These personalized video object models can be created using geometric and appearance modeling techniques, and they can be stored in an object model library. The object models can be reused for processing other video streams. The object models can be shared in a peer-to-peer network among many users, or the object models can be stored in an object model library on a server. When the compressed (encoded) video is reconstructed, the video object models can be accessed and used to produce quality video with nearly lossless compression.

Abstract: Systems and methods of processing video data are provided. Video data having a series of video frames is received and processed. One or more instances of a candidate feature are detected in the video frames. The previously decoded video frames are processed to identify potential matches of the candidate feature. When a substantial amount of portions of previously decoded video frames include instances of the candidate feature, the instances of the candidate feature are aggregated into a set. The candidate feature set is used to create a feature-based model. The feature-based model includes a model of deformation variation and a model of appearance variation of instances of the candidate feature. The feature-based model compression efficiency is compared with the conventional video compression efficiency.

Abstract: Personal object based archival systems and methods are provided for processing and compressing video. By analyzing features unique to a user, such as face, family, and pet attributes associated with the user, an invariant model can be determined to create object model adapters personal to each user. These personalized video object models can be created using geometric and appearance modeling techniques, and they can be stored in an object model library. The object models can be reused for processing other video streams. The object models can be shared in a peer-to-peer network among many users, or the object models can be stored in an object model library on a server. When the compressed (encoded) video is reconstructed, the video object models can be accessed and used to produce quality video with nearly lossless compression.

Abstract: A method, system, and computer-readable set of instructions on a storage medium are provided for querying, analyzing, and processing image data and data/metadata associated with the image data. For example, a tissue sample is made into a slide. A digital or electronic image is made of the slide. That electronic image is then parsed with respect to color, brightness, magnification, intensity, and other available image parameters. The parsed information is then used in searching and reiteratively searching a database of images from one or more sources. If different magnification levels are observed, the images are normalized and/or color corrected. If different types or levels of results are desired, a difference magnification version of the image can be used, searched, and reiteratively searched for in a database of images from one or more sources. The database can be a dynamic database which is continuously being updated, enlarged, and/or reduced.

Abstract: Systems and methods of improving video encoding/decoding efficiency may be provided. A feature-based processing stream is applied to video data having a series of video frames. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks, and each track is given a representative, characteristic feature. Similar characteristic features are clustered and then stored in a model library, for reuse in the compression of other videos. A model-based compression framework makes use of the preserved model data by detecting features in a new video to be encoded, relating those features to specific blocks of data, and accessing similar model information from the model library.

Abstract: Systems and methods for processing video are provided. Video compression schemes are provided to reduce the number of bits required to store and transmit digital media in video conferencing or videoblogging applications. A photorealistic avatar representation of a video conference participant is created. The avatar representation can be based on portions of a video stream that depict the conference participant. A face detector is used to identify, track and classify the face. Object models including density, structure, deformation, appearance and illumination models are created based on the detected face. An object based video compression algorithm, which uses machine learning face detection techniques, creates the photorealistic avatar representation from parameters derived from the density, structure, deformation, appearance and illumination models.

Abstract: Systems and methods of processing video data are provided. Video data having a series of video frames is received and processed. One or more instances of a candidate feature are detected in the video frames. The previously decoded video frames are processed to identify potential matches of the candidate feature. When a substantial amount of portions of previously decoded video frames include instances of the candidate feature, the instances of the candidate feature are aggregated into a set. The candidate feature set is used to create a feature-based model. The feature-based model includes a model of deformation variation and a model of appearance variation of instances of the candidate feature. The feature-based model compression efficiency is compared with the conventional video compression efficiency.

Abstract: Systems and methods of processing video data are provided. Video data having a series of video frames is received and processed. One or more instances of a candidate feature are detected in the video frames. The previously decoded video frames are processed to identify potential matches of the candidate feature. When a substantial amount of portions of previously decoded video frames include instances of the candidate feature, the instances of the candidate feature are aggregated into a set. The candidate feature set is used to create a feature-based model. The feature-based model includes a model of deformation variation and a model of appearance variation of instances of the candidate feature. The feature-based model compression efficiency is compared with the conventional video compression efficiency.

Abstract: A method and apparatus for image data compression includes detecting a portion of an image signal that uses a disproportionate amount of bandwidth compared to other portions of the image signal. The detected portion of the image signal result in determined components of interest. Relative to certain variance, the method and apparatus normalize the determined components of interest to generate an intermediate form of the components of interest. The intermediate form represents the components of interest reduced in complexity by the certain variance and enables a compressed form of the image signal where the determined components of interest maintain saliency. In one embodiment, the video signal is a sequence of video frames.

Abstract: Systems and methods of improving video encoding/decoding efficiency may be provided. A feature-based processing stream is applied to video data having a series of video frames. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks, and each track is given a representative, characteristic feature. Similar characteristic features are clustered and then stored in a model library, for reuse in the compression of other videos. A model-based compression framework makes use of the preserved model data by detecting features in a new video to be encoded, relating those features to specific blocks of data, and accessing similar model information from the model library.

Abstract: Systems and methods for processing video are provided. Video compression schemes are provided to reduce the number of bits required to store and transmit digital media in video conferencing or videoblogging applications. A photorealistic avatar representation of a video conference participant is created. The avatar representation can be based on portions of a video stream that depict the conference participant. A face detector is used to identify, track and classify the face. Object models including density, structure, deformation, appearance and illumination models are created based on the detected face. An object based video compression algorithm, which uses machine learning face detection techniques, creates the photorealistic avatar representation from parameters derived from the density, structure, deformation, appearance and illumination models.

Abstract: Member object(s) of a computer system part(s) in an Enterprise Information System (EIS) are identified (discovered) and topographical relationship(s) are established among them to create digital assets by traversing computer file system(s) to find member object(s). For each member object found, a digital asset identifier thereof is placed in an intermediate representation, such as a graph with nodes and edges. Each digital asset identifier corresponds to a node. The edges represent the topographical relationship. A digital asset is created by placing the member object in a logic/data section of the digital asset, attaching an extended environment data structure to the logic/data section and storing the digital asset in an asset inventory container object. This is repeated for each found member object until the intermediate representation fully describes the computer system part and the asset inventory container object is a complete inventory of the digital assets in the computer system part.

Abstract: Provided are methods and systems for distributing an asset to a multi-tiered network node. A pending notice is received from a distribution server. If the notice indicates that at least one asset is pending, i.e., awaiting deployment, an asset descriptor manifest is received from the distribution server. The asset descriptor manifest, which is stored in a memory on a node, identifies at least one asset to be deployed to the node and includes an offset associated with the asset identifier. A fragment, associated with the asset, is received and stored in the memory. The offset associated with the asset is marked with the end of the fragment and a second fragment, beginning at the offset, is received. Additional fragments are received, and the offset updated, until the entire asset is deployed to the node. Alternately, the entire asset or multiple assets are received in the first fragment.