Aol Inc.

Warner Bros. Entertainment Inc.

America Online, Inc.

Facebook, Inc.

California Institute of Technology

Isis Innovation Limited

Oath Inc.

Time Warner Entertainment Company, L.p.

Warner Bros. Enterianment Inc.

Keren O Perlmutter

Sharon M Perlmutter

Paul Klamer

Eric Wang

Christopher J Cookson

Massimiliano Gasparri

Pietro Perona

Joshua Alspector

John Mantegna

Alex Holub

Andrew Zisserman

Mark Everingham

Shuwu Wu

Leng Chua

Paul R Klamer

Barry Appelman

Shuwun Wu

Separations or images relating to film or other fields may be registered using a variety of features, such as, for example: (1) correcting one or more film distortions; (2) automatically determining a transformation to reduce a film distortion; (3) applying multiple criteria of merit to a set of features to determine a set of features to use in determining a transformation; (4) determining transformations for areas in an image or a separation in a radial order; (5) comparing areas in images or separations by weighting feature pixels differently than non-feature pixels; (6) determining distortion values for transformations by applying a partial distortion measure and/or using a spiral search configuration; (7) determining transformations by using different sets of features to determine corresponding transformation parameters in an iterative manner; and (8) applying a feathering technique to neighboring areas within an image or separation.

Registration of separations

Images are searched to locate faces that are the same as a query face. Images that include a face that is the same as the query face may be presented to a user as search result images. Images also may be sorted by the faces included in the images and presented to the user as sorted search result images. The user may provide explicit or implicit feedback regarding the search result images. Additional feedback may be inferred regarding the search result images based on the user-provided feedback, and the results may be updated based on the user-provided and inferred feedback.

Using relevance feedback in face recognition

Computer-implemented systems and methods for identifying an object in an image are provided. In one example, the method includes identifying a first object related to an electronic image. The image includes at least a second object. Based at least in part on the identity of the first object, social networking information related to the first object is used to programmatically identify the second object. The first object and/or the second object may be a person. In some embodiments, metadata associated with the image may be used to identify the second object. Based at least in part on the identifications, social networking information may be associated between the first object and the second object.

Systems and methods for building and using social networks in image analysis

Blotches may be identified and processed to reduce or eliminate the blotch. The blotch may be in just one of several separations and multiple separations may be used, for example, to identify the blotch. An implementation (i) compares a first component image of an image with a first component image of a reference image, (ii) compares a second component image of the image with a second component image of the reference image, and (iii) determines based on these comparisons whether the first component image of the image includes a blotch. Multiple image separations also, or alternatively, may be used, for example, to modify the blotch, as well as to evaluate whether a modification is beneficial.

Correction of blotches in component images

Similar faces may be determined within images based on human perception of facial similarity. The user may provide an image including a query face to which the user wishes to find faces that are similar. Similar faces may be determined based on similarity information. Similarity information may be generated from information related to a human perception of facial similarity. Images that include faces determined to be similar, based on the similarity information, may be provided to the user as search result images. The user then may provide feedback to indicate the user's perception of similarity between the query face and the search result images.

Detecting facial similarity based on human perception of facial similarity

A method and system for reduction of quantization-induced block-discontinuities arising from lossy compression and decompression of continuous signals, especially audio signals. One embodiment encompasses a general purpose, ultra-low latency, efficient audio codec algorithm. More particularly, the invention includes a method and apparatus for compression and decompression of audio signals using a novel boundary analysis and synthesis framework to substantially reduce quantization-induced frame or block discontinuity; a novel adaptive cosine packet transform (ACPT) as the transform of choice to effectively capture the input audio characteristics; a signal-residue classifier to separate the strong signal clusters from the noise and weak signal components (collectively called residue); an adaptive sparse vector quantization (ASVQ) algorithm for signal components; a stochastic noise model for the residue; and an associated rate control algorithm. The invention further includes corresponding computer program implementations of these and other algorithms.

Method and system for reduction of quantization-induced block-discontinuities and general purpose audio codec

Systems and methods are provided for ultra-low latency decompression for a general-purpose audio input signal. In accordance with one implementation, a computer-implemented method is provided that includes decoding, by a processor, an input bit stream into quantization indices and residue quantization indices; applying an inverse quantization algorithm to the quantization indices to generate signal coefficients; applying an inverse transform to the signal coefficients to generate a time-domain reconstructed signal waveform; applying a stochastic noise synthesis algorithm to the residue quantization indices to generate a time-domain reconstructed residue waveform; combining, by the processor, the reconstructed signal waveform and the reconstructed residue waveform as a reconstructed signal waveform block; and generating an output signal by applying a boundary synthesis algorithm to the reconstructed signal waveform blocks.

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Pacific Palisades, CA, United States of America

Keren O Perlmutter