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On the ranging and recognition of small targets in compressed stereoscopic imagery .2. Stereo matching and ATR algorithms 19

On the ranging and recognition of small targets in compresse...

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Conference on Applications of Digital image Processing XIX

作者： Schmalz, MS Department of Computer and Information Science and Engineering University of Florida Gainesville 32611 FL United States

ISBN: (纸本)0819422355

In Part 1 of this two-part series [1], we showed that the processing of compressed data generally yields decreased computational cost, due to the requirement of fewer operations in the presence of fewer (compressed) data. We described several compressive transformations (block truncation coding or BTC, vector quantization or VQ, and visual pattern image coding or VPIC) in terms of image algebra, a rigorous,concise notation that unifies linear and nonlinear mathematics in the image domain. In this paper, we continue our development of compressive automated target recognition (ATR) algorithms to include compressive stereo matching. In particular, we elucidate:techniques for mapping a cepstrum-based stereo matching algorithm to stereoscopic images represented by the aforementioned compressive formats. Analyses emphasize computational cost, stereo disparity error, and applicability to ATR over various classes of surveillance imagery. Since the study notation, image algebra, has been implemented on numerous sequential and parallel computers, our algorithms are feasible and widely portable.

关键词： image and data compression compressive processing stereo vision

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Small-target detection in multispectral imagery with cyclic overlay processing 2

Small-target detection in multispectral imagery with cyclic ...

引用

Conference on Algorithms for Multispectral and Hyperspectral imagery II

作者： Schmalz, MS Hu, WC Ritter, GX UNIV FLORIDA DEPT COMP & INFORMAT SCI & ENGNGAINESVILLEFL 32611

ISBN: (纸本)0819421391

The detection of small, hostile targets in multispectral imagery is generally complicated by sensor noise, atmospheric obscurants, and spatial distortions induced by the point-spread function (PSF). Traditional methods for multispectral detection of small targets, such as signature- based discrimination predicated upon deterministic physical models, have not proven robust in the presence of camera noise at low light levels. The additional problem of target variability also confounds a signature-based approach. Due to time-dependent variations in illuminant spectra response, targets can appear to have different spectral properties at different times of day and under various weather conditions. In this paper, we discuss computationally efficient methods for locating targets that differ spectrally from their spatially adjacent backgrounds but are similar to features located elsewhere in the source image. In particular, we note that flicker effects can be produced in which target intensity appears to vary differently with background intensity. Such effects are produced computationally by cyclic overlay processing (COP), which sequentially displays monospectral band images to achieve different perceived flicker rates of target and background. When combined with knowledge about the human visual system (HVS), COP can be successfully used in conjunction with neighborhood operations to segment target regions in highly cluttered imagery. We emphasize the role of target-background contrast in potentiating flicker effects, and discuss algorithms for computing COP. Analyses emphasize computational cost and effectiveness of various COP filter configurations for detecting targets that are similar to, or partially obscured by, surrounding cover or earth. We also discuss the implementation of our algorithms on parallel processors, in particular, the parallel algebraic logic (PAC) architecture currently being implemented in cooperation with Lockheed- Martin and USAF Wright Laboratory

关键词： automated target recognition image and data compression compressive processing

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