Massachusetts Institute of Technology

Mitsubishi Electric Corporation

Michael Jones

Tomaso A Poggio

David J Beymer

Thomas Vetter

Kuan-Chuan Peng

Ejaz Ahmed

An image processing system (100) analyzes novel images in one of three modes. In a first mode, the system learns a model based on vectorized prototype images, and uses the model to characterize the shape of a novel image as a pixel-by-pixel linear combination of the prototype shapes. In a second mode, both shape and texture vectors are used to characterize the novel image as a linear combination of the prototypes. In a third mode, shape information is modeled by a linear combination of prototypes and texture information is modeled by an optical flow, and processing of shape and texture information is interleaved. Novel images are synthesized by analysis of input images using transformed prototypes. Image compression is achieved by storing or transmitting parameter vectors relating the novel or applied images to the prototypes.

Image compression by pointwise prototype correspondence using shape and

An image processing system (100) analyzes novel images in one of three modes. In a first mode, the system learns a model based on prototype images, and uses the model to characterize the shape of a novel image as a pixel-by-pixel linear combination of the prototypes. In a second mode, both shape and texture are used to characterize the novel image as a linear combination of the prototypes. In a third mode, shape information is modeled by a linear combination of prototypes and texture information is modeled by an optical flow, and processing of shape and texture information is interleaved. Novel images are synthesized by analysis of input images using transformed prototypes.

Image analysis and synthesis networks using shape and texture information

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Cambridge, MA, United States of America

Michael Jones