Nec Laboratories America, Inc.

Vidient Systems, Inc

Nec Soft, Ltd.

Yihong Gong

Shenghuo Zhu

Mei Han

Wei Xu

Yun Chi

Kai Hung Yu

Hai Tao

Jinjun Wang

Fengjun Lv

Shengyang Dai

Kazuya Ueki

Koji Hino

Ying Wu

Dingding Wang

Jianchao Yang

Huazhong Ning

Tianbao Yang

Systems and methods are disclosed for generating super resolution images by building a set of multi-resolution bases from one or more training images; estimating a sparse resolution-invariant representation of an image, and reconstructing one or more missing patches at any resolution level.

Systems and methods for resolution-invariant image representation

Systems and methods are disclosed for summarizing multiple documents by generating a model of the documents as a mixture of document clusters, each document in turn having a mixture of sentences, wherein the model simultaneously representing summarization information and document cluster structure; and determining a loss function for evaluating the model and optimizing the model.

Multiple-document summarization using document clustering

Systems and methods are disclosed to classify an input image by determining a spatial-pyramid image representation based on sparse coding; determining a descriptor for each interest point in the input image; encoding the descriptor; and applying max pooling to form the spatial pyramid representation of images.

Linear spatial pyramid matching using sparse coding

Systems and methods are disclosed to analyze a social network by generating a data tensor from social networking data; applying a non-negative tensor factorization (NTF) with user prior knowledge and preferences to generate a core tensor and facet matrices; and rendering information to social networking users based on the core tensor and facet matrices.

Social network analysis with prior knowledge and non-negative tensor factorization

A method and system for training a neural network of a visual recognition computer system, extracts at least one feature of an image or video frame with a feature extractor; approximates the at least one feature of the image or video frame with an auxiliary output provided in the neural network; and measures a feature difference between the extracted at least one feature of the image or video frame and the approximated at least one feature of the image or video frame with an auxiliary error calculator. A joint learner of the method and system adjusts at least one parameter of the neural network to minimize the measured feature difference.

Transfer learning methods and systems for feed-forward visual recognition systems

Systems and methods are disclosed for processing a low resolution image by performing a high resolution edge segment extraction on the low resolution image; performing an image super resolution on each edge segment; performing reconstruction constraint reinforcement; and generating a high quality image from the low quality image.

Soft edge smoothness prior and application on alpha channel super resolution

A human tracking apparatus and method capable of highly accurately tracking the movement of persons photographed in moving images includes: an image memorythat stores an inputted frame image; a human detecting unitthat detects persons photographed in the inputted frame image; a candidate registering unitthat registers already detected persons as candidates; a similarity index calculating unitthat calculates similarity indices indicating the similarity between the persons detected in the inputted frame image and the registered candidates for two or more types of parameters based on the stored frame images in relation to all combinations of the persons and the candidates; a normalizing unitthat normalizes the similarity indices; an integrating unitthat integrates the normalized indices for each combination of the detected persons and the candidates; and a tracking unitthat identifies a person the same as an arbitrary candidate based on the similarity indices.

Human tracking apparatus, human tracking method, and human tracking processing program

Systems and methods are disclosed for determining 3D human pose by generating an Appearance and Position Context (APC) local descriptor that achieves selectivity and invariance while requiring no background subtraction; jointly learning visual words and pose regressors in a supervised manner; and estimating the 3D human pose.

Recovery of 3D human pose by jointly learning metrics and mixtures of experts

A system is disclosed with a collaborative filtering engine to predict an active user's ratings/interests/preferences on a set of new products/items. The predictions are based on an analysis the database containing the historical data of many users' ratings/interests/preferences on a large set of products/items.

Recommender system with fast matrix factorization using infinite dimensions

System and methods are disclosed to perform multi-human 3D tracking with a plurality of cameras. At each view, a module receives each camera output and provides 2D human detection candidates. A plurality of 2D tracking modules are connected to the CNNs, each 2D tracking module managing 2D tracking independently. A 3D tracking module is connected to the 2D tracking modules to receive promising 2D tracking hypotheses. The 3D tracking module selects trajectories from the 2D tracking modules to generate 3D tracking hypotheses.

Efficient multi-hypothesis multi-human 3D tracking in crowded scenes

Systems and methods are disclosed for factorizing high-dimensional data by simultaneously capturing factors for all data dimensions and their correlations in a factor model, wherein the factor model provides a parsimonious description of the data; and generating a corresponding loss function to evaluate the factor model.

Processing high-dimensional data via EM-style iterative algorithm

Systems and methods are disclosed to find dynamic social networks by applying a dynamic stochastic block model to generate one or more dynamic social networks, wherein the model simultaneously captures communities and their evolutions, and inferring best-fit parameters for the dynamic stochastic model with online learning and offline learning.

Finding communities and their evolutions in dynamic social network

Systems and methods are disclosed that performs active feature probing using data augmentation. Active feature probing is a means of actively gathering information when the existing information is inadequate for decision making. The data augmentation technique generates factitious data which complete the existing information. Using the factitious data, the system is able to estimate the reliability of classification, and determine the most informative feature to probe, then gathers the additional information. The features are sequentially probed until the system has adequate information to make the decision.

Active feature probing using data augmentation

A computer implemented technique for producing super resolution images from ordinary images or videos containing a number of images wherein a number of non-smooth low resolution patches comprising an image are found using edge detection methodologies. The low resolution patches are then transformed using selected basis of a Radial Basis Function (RBF) and Gaussian process regression is used to generate high resolution patches using a trained model. The high resolution patches are then combined into a high resolution image or video.

Super resolution using gaussian regression

Systems and methods are disclosed to predict one or more missing elements from a partially-observed matrix by receiving one or more user item ratings; generating a model parameterized by matrices U, S, V; applying the model to display an item based on one or more predicted missing elements; and applying the model at run-time and determining USV.

Growing community of inventors

Saratoga, CA, United States of America

Yihong Gong