Sarnoff Corporation

Sri International

Imax Corporation

Yanlin Guo

Harpreet Singh Sawhney

Rakesh Kumar

Keith James Hanna

Hui Ting Cheng

James Russell Bergen

Ying Shan

Stephen Charles Hsu

Feng Han

Sang-Hack Jung

Jiangjian Xiao

Jane Asmuth

Steve Hsu

Harpreet S Swahney

The present invention relates to a method and system for creating a strong classifier based on motion patterns wherein the strong classifier may be used to determine an action being performed by a body in motion. When creating the strong classifier, action classification is performed by measuring similarities between features within motion patterns. Embodiments of the present invention may utilize candidate part-based action sets and training samples to train one or more weak classifiers that are then used to create a strong classifier.

Real-time action detection and classification

A method and apparatus for tracking a movable object using a plurality of images, each of which is separated by an interval of time is disclosed. The plurality of images includes first and second images. The method and apparatus include elements for aligning the first and second images as a function of (i) at least one feature of a first movable object captured in the first image, and (ii) at least one feature of a second movable object captured in the second image; and after aligning the first and second images, comparing at least one portion of the first image with at least one portion of the second image.

Method and apparatus for tracking a movable object

A method and apparatus for automatically combining aerial images and oblique images to form a three-dimensional (3D) site model. The apparatus or method is supplied with aerial and oblique imagery. The imagery is processed to identify building boundaries and outlines as well as to produce a depth map. The building boundaries and the depth map may be combined to form a 3D plan view model or used separately as a 2D plan view model. The imagery and plan view model is further processed to determine roof models for the buildings in the scene. The result is a 3D site model having buildings represented rectangular boxes with accurately defined roof shapes.

Method and apparatus for automatically generating a site model

Systems and techniques for synthesizing high-resolution images are described. Using as input hybrid high- and low-resolution images (), processing techniques including three-dimensional image warp-based () rendering may assist in developing the high-resolution output images. Enhanced results may in some instances be obtained by creating a high-resolution images of one member of a pair of stereo images by utilizing multiple frames of the other member of the pair.

Techniques and systems for developing high-resolution imagery

An apparatus and method for generating automated multi-view three dimensional pose and geometry estimation for the insertion of realistic and authentic views of synthetic objects into a real scene. A multi-view three dimensional estimation routine comprising the steps of feature tracking, pairwise camera pose estimation, computing camera pose for overlapping sequences and performing a global block adjustment to provide camera pose and scene geometric information for each frame of a scene. A match move routine may be used to insert a synthetic object into one frame of a video sequence based on the pose and geometric information of the frame, and calculate all other required object views of the synthetic object for the remaining frames using the pose and geometric information acquired as a result of the multi-view three dimensional estimation routine.

Method and apparatus for multi-view three dimensional estimation

A system and method that detects independently moving objects in 3D scenes which are viewed under camera motion progressively applies constraints to the images to ensure the stability of the constraints. The system first calculates 2D view geometry constraints for a set of images. These constraints are tested to determine if the imaged scene exhibits significant 3D characteristics. If it does, then 3D shape constraints, are applied to the set of images. The 3D shape constraints are themselves constrained by the 2D view geometry constraints. The set of images is then tested to identify areas that are inconsistent with the 2D or 3D constraints. These areas correspond to the moving objects. The 2D view geometry constraints are calculated by computing a dominant image alignment for successive pairs of images and then computing constrained epipolar transformations for the two image pairs. This 2D view geometry is further refined based on a plurality of target point correspondences among the plurality of frames. The epipolar geometry for the point correspondence having a minimum median error is selected as the 2D view geometry of the scene. The 3D shape constraint is a parallax geometry that is calculated by iteratively minimizing errors in a parametric alignment of the images using an estimated parallax geometry.

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Lawrenceville, NJ, United States of America

Yanlin Guo