The image data analytics for computation configuration method and computer system with computation accessibility is provided. The computation accessibility and configuration on image data processing are implemented by a plurality of computers having a plurality of processors and a plurality of data storages, the image data analytics for computation accessibility and configuration includes the following steps: inputting an original image by an input device and initializing the original image; defining a plurality of tiles equally dividing the original image into a same size of a regular shape; transferring the plurality of image regions to form a graph having vertices and edges; cutting the graph into a plurality of sub-graphs; arranging the plurality of sub-graphs to the plurality of processors or cores to conduct parallel processing simultaneously for analyzing the image data; and storing a plurality of processing results respectively in the plurality of data storages.

A processor-implemented method includes acquiring, by a processor, a first undirected graph and a second undirected graph, generating, by the processor, a first lattice for the first undirected graph and a second lattice for the second undirected graph; matching, by the processor, the first lattice and the second lattice based on a first global structure of the first lattice and a second global structure of the second lattice, the first global structure corresponding to nodes of the first undirected graph and the second global structure corresponding to nodes of the second undirected graph, and processing the first undirected graph and the second undirected graph based on a result of the matching of the first lattice and the second lattice.

A method and a system for pose estimation are provided. The method includes: extracting a plurality of sets of part-feature maps from an image, each set of the extracted part-feature maps encoding the messages for a particular body part and forming a node of a part-feature network; passing a message of each set of the extracted part-feature maps through the part-feature network to update the extracted part-feature maps, resulting in each set of the extracted part-feature maps incorporating the message of upstream nodes; estimating, based on the updated part-feature maps, the body part within the image.

A method of deep graph representation learning includes: calculating a plurality of base features from a graph and adding the plurality of base features to a feature matrix. The method further includes generating, by a processing device, a current feature layer from the feature matrix and a set of relational feature operators, wherein the current feature layer corresponds to a set of current features, evaluating feature pairs associated with the current feature layer, and selecting a subset of features from the set of current features based on the evaluated feature pairs. The method further includes adding the subset of features to the feature matrix to generate an updated feature matrix.

The disclosures relate to a method and a system for pose estimation. The method comprises: extracting a plurality of sets of part-feature maps from an image, each set of the extracted part-feature maps encoding the messages for a particular body part and forming a node of a part-feature network; passing a message of each set of the extracted part-feature maps through the part-feature network to update the extracted part-feature maps, resulting in each set of the extracted part-feature maps incorporating the message of upstream nodes; estimating, based on the updated part-feature maps, the body part within the image.

An object recognition apparatus, a classification tree learning apparatus, an operation method of the object recognition apparatus, and an operation method of the classification tree learning apparatus are provided. The object recognition apparatus may include an input unit to receive, as an input, a depth image representing an object to be analyzed, and a processing unit to recognize a visible object part and a hidden object part of the object, from the depth image, using a classification tree.

Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. In particular, a multi-view interactive digital media representation (MVIDMR) of a person can be generated from live images of a person captured from a hand-held camera. Using the image data from the live images, a skeleton of the person and a boundary between the person and a background can be determined from different viewing angles and across multiple images. Using the skeleton and the boundary data, effects can be added to the person, such as wings. The effects can change from image to image to account for the different viewing angles of the person captured in each image.

Image processing apparatus programmed to: continuously shoot a subject to obtain images, and detect the object and extract a position of the object from a three-dimensional position of the subject in the images; detect the person and extract a position of the person from the three-dimensional position, and extract, from the position of the person, part information pieces including respective positions of characteristic parts of the person; generate a pose class for each set of part information pieces, the part information pieces being similar to one another in correlation between parts of the person calculated from each of the part information pieces; identify a pose class to which the correlation between parts of the person belongs, among generated pose classes, when a distance between the person and the object is within a predetermined range; and store the identified pose class in association with the object.

The present disclosure herein provides methods and systems that solves the technical problems of generating an efficient, accurate and light-weight 3-Dimensional (3-D) pose estimation framework for estimating the 3-D pose of an object present in an image used for the 3-dimensional (3D) model registration using deep learning, by training a composite network model with both shape features and image features of the object. The composite network model includes a graph neural network (GNN) for capturing the shape features of the object and a convolution neural network (CNN) for capturing the image features of the object. The graph neural network (GNN) utilizes the local neighbourhood information through the image features of the object and at the same time maintaining global shape property through the shape features of the object, to estimate the 3-D pose of the object.