Nec Laboratories America, Inc.

International Business Machines Corporation

Nec Corporation

Xiaoyu Wang

Yuanqing Lin

Ming M Yang

Sharathchandra UmapathiRao Pankanti

Rogerio Schmidt Feris

Ankur Datta

Shenghuo Zhu

Kai Hung Yu

Manmohan Chandraker

Shiyu Song

Will Zou

Miao Sun

Qi Tian

Tianbao Yang

Timothee Cour

Saining Xie

Foreground objects of interest are distinguished from a background model by dividing a region of interest of a video data image into a grid array of individual cells. Each of the cells are labeled as foreground if accumulated edge energy within the cell meets an edge energy threshold, or if color intensities for different colors within each cell differ by a color intensity differential threshold, or as a function of combinations of said determinations.

Multi-cue object detection and analysis

Systems and methods are disclosed for classifying vehicles by performing scale aware detection; performing detection assisted sampling for convolutional neural network (CNN) training, and performing deep CNN fine-grained image classification to classify the vehicle type.

Object-centric fine-grained image classification

An object detector includes a bottom-up object hypotheses generation unit; a top-down object search with supervised descent unit; and an object re-localization unit with a localization model.

Object detection with Regionlets re-localization

Systems and methods are disclosed for detecting an object in an image by determining convolutional neural network responses on the image; mapping the responses back to their spatial locations in the image; and constructing features densely extract shift invariant activations of a convolutional neural network to produce dense features for the image.

Regionlets with shift invariant neural patterns for object detection

Systems and methods are disclosed for autonomous driving with only a single camera by moving object localization in 3D with a real-time framework that harnesses object detection and monocular structure from motion (SFM) through the ground plane estimation; tracking feature points on moving cars a real-time framework to and use the feature points for 3D orientation estimation; and correcting scale drift with ground plane estimation that combines cues from sparse features and dense stereo visual data.

Moving object localization in 3D using a single camera

Systems and methods are disclosed for object detection by receiving an image and extracting features therefrom; applying a learning process to determine sub-regions and select predetermined pooling regions; and performing selective max-pooling to choose one or more feature regions without noises.

Selective max-pooling for object detection

Systems and methods are disclosed for object detection by receiving an image; segmenting the image; extracting features from the image; and performing a dimension-wise spatial layout selection to pick up dimensions inside a discriminative spatial region for classification.

Dimension-wise spatial layout importance selection: an alternative way to handle object deformation

Systems and methods for object detection by receiving an image; segmenting the image and identifying candidate bounding boxes which may contain an object; for each candidate bounding box, dividing the box into overlapped small patches, and extracting dense features from the patches; during a training phase, applying a learning process to learn one or more discriminative classification models to classify negative boxes and positive boxes; and during an operational phase, for a new box generated from the image, applying the learned classification model to classify whether the box contains an object.

Window dependent feature regions and strict spatial layout for object detection

Foreground objects of interest are distinguished from a background model by dividing a region of interest of a video data image into a grid array of individual cells that are each smaller than that a foreground object of interest. More particularly, image data of the foreground object of interest spans a contiguous plurality of the cells. Each of the cells are labeled as foreground if accumulated edge energy within the cell meets an edge energy threshold, if color intensities for different colors within each cell differ by a color intensity differential threshold, or as a function of combinations of said determinations in view of one or more combination rules.

Systems and methods are disclosed to search for a query image, by detecting local invariant features and local descriptors; retrieving best matching images by quantizing the local descriptors with a vocabulary tree; and reordering retrieved images with results from the vocabulary tree quantization.

Contextual weighting and efficient re-ranking for vocabulary tree based image retrieval

An image retrieval method includes learning multiple object category classifiers with a processor offline and generating classifications scores of images as the semantic attributes; performing vocabulary tree based image retrieval using local features with semantic-aware co-indexing to jointly embed two distinct cues offline for near-duplicate image retrieval; and identifying top similar or dissimilar images using multiple semantic attributes.

Semantic-aware co-indexing for near-duplicate image retrieval

Systems and methods are disclosed to categorize images by detecting local features for each image; applying a tree structure to index local features in the images; and extracting a rank list of candidate images with category tags based on a tree indexing structure to estimate a label of a query image.

Growing community of inventors

Sunnyvale, CA, United States of America

Xiaoyu Wang