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A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each endmember within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.

Remote identification of people is an important capability for security systems. Automatically controlling a pan-tiltzoom camera is an effective way to collect high resolution video or images of people in an unconstrained environment. Often there will be more people in an area than cameras available. The cameras must then divide their time among the people in order to view everyone. In this paper, we discuss the challenges involved in scheduling an active camera to observe multiple people. We present some candidate scheduling policies to address these challenges and evaluate their performance. The evaluation was conducted with a simulation based on data collected with our cooperative active camera system.

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Abstract-A perceptual image hash function maps an image to a short binary string based on an image's appearance to the human eye. Perceptual image hashing is useful in image databases, watermarking, and authentication. In this paper, we decouple image hashing into feature extraction (intermediate hash) followed by data clustering (final hash). We show that the decision version of our clustering problem is NP complete. Then, for any perceptually significant feature extractor, we propose a polynomial-time heuristic clustering algorithm that automatically determines the final hash length needed to satisfy a specified distortion. Based on the proposed algorithm, we develop two variations to facilitate perceptual robustness vs. fragility trade-offs. We validate the perceptual significance of our hash by testing under Stirmark attacks. Finally, we develop randomized clustering algorithms for the purposes of secure image hashing.

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