Tandent Vision Science, Inc.

Kristin Jean Dana

Bruce Allen Maxwell

Casey Arthur Smith

Richard Mark Friedhoff

Andrew Neil Stein

Steven Joseph Bushell

David Tolliver

Timothy King Rodgers, Jr

James Andrew Bagnell

Neil Alldrin

Bartholomeus Christianus Leonardus Nabbe

Iljung Samuel Kwak

Milan Diebel

Kshitiz Garg

Youngrock R Yoon

Besma Roui Abidi

David Allen Tolliver

Lljung Samuel Kwak

Richard Mark Friedhof

In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, forming a set of selectively varied representations of the image file and performing an image segregation operation on at least one preselected representation of the image of the image file, to generate intrinsic images corresponding to the image. According to a feature of the exemplary embodiment of the present invention, the selectively varied representations comprise multi-resolution representations such as a scale-spaced pyramid of representations. In a further feature of the exemplary embodiment of the present invention, the intrinsic images comprise a material image and an illumination image.

Multi-resolution analysis in image segregation

In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image file depicting an image, in a computer memory, assembling a feature vector for the image file, the feature vector containing information regarding a likelihood that a selected pair of regions of the image file are of a same intrinsic characteristic, for example, a same texture, providing a classifier derived from a computer learning technique, computing a classification score for the selected pair of regions of the image file, as a function of the feature vector and the classifier and classifying the regions as being of the same intrinsic characteristic, as a function of the classification score.

Image segregation system with method for handling textures

In a first exemplary embodiment of the present invention, an automated, computerized method is provided for determining an illumination flux condition in a scene. The method comprises the steps of generating and storing a sequence of images of the scene, each one of the sequence of images comprising an array of pixels and corresponding to the scene photographed in a preselected polarization direction, different from the polarization direction of other ones of the sequence of images, determining a polarization sequence vector for at least one pixel in the array, as a function of color information for the pixel in the array, among the sequence of images; and utilizing the polarization sequence vector to determine one of a shadowed and lit illumination condition for the at least one pixel.

Polarization-based shadow detection

In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of identifying token regions in the image, each token region comprising a connected image region of similar color and intensity, and utilizing the identified token regions to generate single material token regions for use in processing material and illumination aspects of the image.

System and method for identifying complex tokens in an image

In an exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of providing an image, organizing spatio-spectral information for the image in a matrix equation expressed by: [A][x]=[b], wherein [A] expresses values determined by a constraining relationship imposed upon the spatio-spectral information, [b] expresses recorded information for the image, and [x] expresses an unknown material/illumination component of the image, and utilizing the matrix equation in an image segregation operation.

Constraint generation for use in image segregation

In an exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of generating spatio-spectral information for the image, defining a constraint as a function of the spatio-spectral information, and performing an optimization operation as a function of the constraint to generate an intrinsic image corresponding to the image.

Image segregation system architecture

In an exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the steps of converting a color band representation of the image to a homogeneous representation of spectral and spatial characteristics of a texture region in the image and utilizing the homogeneous representation of spectral and spatial characteristics of a texture region in the image to identify homogeneous tokens in the image.

In a first exemplary embodiment of the present invention, an automated, computerized method is provided for determining illumination flux in an image. According to a feature of the present invention, the method comprises the steps of performing a computer learning technique to determine spatio-spectral information for the images, and utilizing the spatio-spectral information to identify illumination flux.

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Spring Lake, NJ, United States of America

Kristin Jean Dana