Hewlett-packard Development Company, L.p.

Amazon Technologies, Inc.

Hewlett-packard Company

Yacov Hel-Or

Gerard Guy Medioni

Dilip Kumar

Ehud Benyamin Rivlin

George Leifman

Dmitri Veikherman

Eli Osherovich

Doron Shaked

Renato Keshet

Ruth Bergman

Ron Maurer

Mark Majette

Michael Elad

Patrick J Chase

Renato Kresch

Stephen B Pollard

Described is a multiple-camera system and process for detecting, tracking, and re-verifying agents within a materials handling facility. In one implementation, a plurality of feature vectors may be generated for an agent and maintained as an agent model representative of the agent. When the object being tracked as the agent is to be re-verified, feature vectors representative of the object are generated and stored as a probe agent model. Feature vectors of the probe agent model are compared with corresponding feature vectors of candidate agent models for agents located in the materials handling facility. Based on the similarity scores, the agent may be re-verified, it may be determined that identifiers used for objects tracked as representative of the agents have been flipped, and/or to determine that tracking of the object representing the agent has been dropped.

Agent re-verification and resolution using imaging

A scanning device includes a scanning mechanism and a processing mechanism. The scanning mechanism scans an image fixed on a medium to generate a digital infrared representation of the image and a digital visible light representation of the image. The processing mechanism substantially reduces effects of noise and distortions within the digital visible light representation of the image in one pass. The processing mechanism at least decorrelates visible light aspects from the infrared representation of the image and employs a one-pass filter that uses both the infrared and the visible light representations of the image.

One-pass filtering and infrared-visible light decorrelation to reduce noise and distortions

A method of processing, by a computer, an input signal including obtaining input signal samples that represents a physical quantity. The method includes transforming the samples from an original domain into a plurality of coefficients in a transform domain, using an over-complete transform, such that the plurality of coefficients is sufficient to redundantly reconstruct the input signal samples. The method also includes modifying the coefficients independently of each other by applying a correction function, obtaining a set of corrected coefficients. The method also includes transforming the set of corrected coefficients back to the original domain. In the method, the correction function is determined by using a set of training pairs, each training pair including an uncorrected signal and a corrected signal, and by reducing a specified aggregate measure of error between the uncorrected signal and the corrected signal in the original domain across the training pairs.

Processing an input signal using a correction function based on training pairs

A full color output image is estimated from a mosaic digital image, the output image is transformed from an original color space into a luminance/chrominance color space, chrominance components of the transformed output image are smoothed, the image is re-transformed back to its original color space, and corresponding pixels in the re-transformed image are re-set to sampled values.

Processing of mosaic digital images

Processing of a mosaic digital image includes interpolating values of a first color at pixels where the first color was not sampled. The interpolation of the first color value at a given pixel includes determining likelihoods of the given pixel belonging to the same region as each of at least two other pixels having sampled values of the first color. The other pixels are in different directions relative to the given pixel. The interpolation further includes using the likelihoods and the sampled values of the other pixels to interpolate the first color at the given pixel.

Method of processing a digital image

A system and a method for classifying input vectors into one of two classes, a target class and a non-target class, utilize iterative rejection stages to first label the input vectors that belong in the non-target class in order to identify the remaining non-labeled input vectors as belonging in the target class. The system and method may be used in a number applications, such as face detection, where the members of the two classes can be represented in a vector form. The operation of the system can be divided into an off-line (training) procedure and an on-line (actual classification) procedure. During the off-line procedure, projection vectors and their corresponding threshold values that will be used during the on-line procedure are computed using a training set of sample non-targets and sample targets. Each projection vector facilitates identification of a large portion of the sample non-targets as belonging in the non-target class for a given set of sample targets and sample non-targets. During the on-line procedure, an input vector is successively projected onto each computed projection vector and compared with a pair of corresponding threshold values to determine whether the input vector is a non-target. If the input vector is not determined to be a non-target during the successive projection and thresholding, the input vector is classified as a target.

Approximated invariant method for pattern detection

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Zichron Yacov, Israel

Yacov Hel-Or