Abstract: A system and method for classifying compartments at a security checkpoint includes classifying a compartment into a first category or a second category using a first stage neural network that analyzes a three-dimensional representation of the compartment extracted from an imaging device coupled to the computing system, and in response to classifying the compartment into the second category, screening the compartment using a second stage neural network that performs image segmentation to isolate a hazardous object present in the compartment.

Abstract: A system and method for classifying compartments at a security checkpoint includes classifying a compartment into a first category or a second category using a first stage neural network that analyzes a three-dimensional representation of the compartment extracted from an imaging device coupled to the computing system, and in response to classifying the compartment into the second category, screening the compartment using a second stage neural network that performs image segmentation to isolate a hazardous object present in the compartment.

Abstract: In general, the present invention is directed to systems and methods for identifying behaviors of two or more animal using video. The invention includes a system with at least two video cameras coupled to a computer where the computer is configured to automatically perform animal segmentation and identification, animal body parts identification, and behavior identification.

Abstract: A robust recognition-by-parts authentication system for comparing and authenticating a test image with at least one training image is disclosed. This invention applies the concepts of recognition-by-parts, boosting, and transduction.

Abstract: A robust recognition-by-parts authentication system for comparing and authenticating a test image with at least one training image is disclosed. This invention applies the concepts of recognition-by-parts, boosting, and transduction.

Abstract: In general, the present invention is directed to systems and methods for identifying behaviors of two or more animal using video. The invention includes a system with at least two video cameras coupled to a computer where the computer is configured to automatically perform animal segmentation and identification, animal body parts identification, and behavior identification.

Abstract: An open set recognition system utilizing transductive inference including capture device(s), a basis, quality checker(s), feature extractor(s), a gallery, a rejection threshold, a storage mechanism, and a recognition stage. The basis encodes sample(s) and is derived using training samples. The feature extractor(s) generates signature(s) from sample(s) using the basis. The rejection threshold is created using a rejection threshold learning mechanism that calculates the rejection threshold using sample(s) by: swapping a sample identifier with other sample identifier(s); computing a credibility value for the swapped sample identifiers; deriving a peak-to-side ratio distribution using the credibility values; and determining the rejection threshold using the peak-to-side ratio distribution. The open set recognition stage authenticates or reject as unknown the identity of unknown sample(s) using derived credibility values, derived peak-to-side ratios for the unknown sample and the rejection threshold.

Abstract: Disclosed is an open set recognition system that utilizes transductive inference. One embodiment of this open set recognition system includes capture device(s), a basis, quality checker(s), feature extractor(s), a gallery, a rejection threshold, a storage mechanism, and a recognition stage. The basis is configured to encode the sample(s) and is derived using representative training samples. The feature extractor(s) generates signature(s) from sample(s) using the basis. The rejection threshold may be created using a rejection threshold learning mechanism configured to calculate the rejection threshold using the sample(s) by: swapping one of the sample identifier with other possible sample identifier(s); computing a credibility value (p) for each of the swapped sample identifiers; deriving a peak-to-side ratio distribution using a multitude of the credibility values; and determining the rejection threshold using the peak-to-side ratio distribution.