Mitsubishi Electric Research Laboratories, Inc.

At+t Corp.

Princeton University

Siemens Corporate Research, Inc.

Mitsubishi Electric Research Labs.

Sun-Yuan Kung

Anthony Vetro

Huifang Sun

I-Jong Lin

Ajay Divakaran

Ming Fang

Tsuhan Chen

Min Wu

Ling Guan

Yun-Ting Lin

Long-Ji Lin

Robert A Joyce

Yen-Kuang Chen

Hau-San Wong

Shang-Hung Lin

A method for dynamically allocating network resources while transferring multimedia at variable bit-rates in a network extracts first content features from the multimedia to determine renegotiation points and observation periods. Second content features and traffic features are extracted from the multimedia bit stream during the observation periods. The second content features and the traffic features are combined in a neural network to predict the network resources to be allocated at the renegotiation points.

Dynamic network resource allocation using multimedia content features and traffic features

A method for generating a representation of multimedia content by first segmenting the multimedia content spatially and temporally to extract objects. Feature extraction is applied to the objects to produce semantic and syntactic attributes, relations, and a containment set of content entities. The content entities are coded to produce directed acyclic graphs of the content entities, where each directed acyclic graph represents a particular interpretation of the multimedia content.

Method for representing and comparing multimedia content

A method determines a surface of an object in a sequence of images. The method begins by estimating a boundary of the object in each image of the sequence using motion information of adjacent images of the sequence. Then, portions of each image of the sequence are ordered to produce an ordered sequence of images. The ordered portions are exterior to the estimated object boundary. Edges in each ordered image are filtered using the motion information, and each ordered image of the sequence is searched to locate the filtered edges to form a new boundary outside the estimated boundary. The filtering and searching are repeated, while projecting the new object boundaries over the sequence of images, until the new object boundaries converges to a surface of the object.

Method for ordering image spaces to search for object surfaces

A technique is provided for calculating the time offsets between different video cameras and re-synchronizing the captured frames in a post-processing manner, thus eliminating the necessity of an explicit common clock for synchronization. This approach allows effective synchronization of frames from different cameras so that a multi-camera system can be used to more accurately analyze a subject under observation.

Frame synchronization in a multi-camera system

A method represents a shape of an object in an image. Portions of the image interior to a boundary of the object are ordered. A medial axis for the ordered portions of the image is derived. The medial axis is segmented to produce an ordered tree. The ordered tree is transformed to a partial ordered tree to represent the shape of the object.

Method for ordering image spaces to represent object shapes

A method detects a boundary in a sequence of two-dimensional images where each image has multiple intensity value points. Filtering and motion analysis is applied on each image to produce motion enhanced images. Initial search parameters are determined from a dynamic snake model applied to the motion enhanced images. Each motion enhanced image is searched for a potential boundary using the search parameters. The potential boundary is projected into the motion enhanced image of a previous, current, and next image, and the search parameters of the previous, current, and next images are updated. The searching, projecting, and updating repeat until a predetermined level of convergence is reached.

Object boundary detection using a constrained viterbi search

A method determines true motion vectors associated with a sequence of images. The images include fields made up of blocks of pixels. The method selects candidate feature blocks from the blocks of pixels. The candidate feature blocks have intensity variances above a threshold indicative of texture features. Candidate feature blocks in similarly numbered adjacent field intervals are compared to determine sets of displaced frame differences parameters for each candidate feature block. The true motion vectors for each candidate feature block are determined from a minimum weighted score derived from the difference parameters.

Motion compensated digital video signal processing

A system for automatically detecting and recognizing the identity of a deformable object such as a human face, within an arbitrary image scene. The system comprises an object detector implemented as a probabilistic DBNN, for determining whether the object is within the arbitrary image scene and a feature localizer also implemented as a probabilistic DBNN, for determining the position of an identifying feature on the object such as the eyes. A feature extractor is coupled to the feature localizer and receives coordinates sent from the feature localizer which are indicative of the position of the identifying feature and also extracts from the coordinates information relating to other features of the object such as the eyebrows and nose, which are used to create a low resolution image of the object. A probabilistic DBNN based object recognizer for determining the identity of the object receives the low resolution image of the object inputted from the feature extractor to identify the object.

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Princeton, NJ, United States of America

Sun-Yuan Kung