Massachusetts Institute of Technology

David J Beymer

Tomaso A Poggio

Thomas Vetter

Michael Jones

Amnon Shashua

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

Synthesis of novel images from example images is achieved by determining a pixelwise optical flow among example images, computing a parameter set for a new image, and synthesizing the new image based on the parameter vector and the example images. The parameter set may describe characteristics of the image, in which case the characteristics are applied to a neural network trained with the example images in order to synthesize the novel image. The parameter set may also be an estimate of relative contributions of each of the example images to the new image, in which case the new image may be synthesized by taking a linear combination of the example images, weighted by the parameter set. In one embodiment, both the example and the new images are of the same object. In another embodiment, the example image represents a first object while the new image represents a second object. In yet another embodiment, a set of target images is synthesized from a single target image and a set of source images. Analysis of existing images to determine image parameters is achieved by determining parameters for the existing image based on comparison of the existing image with an image set for which parameters are known.

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Cambridge, MA, United States of America

David J Beymer