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A broad range of very powerful foreground detection methods exist because this is an essential step in many computer vision algorithms. However, because of memory and computational constraints, simple static background subtraction is very often the technique that is used in practice on a platform with limited resources such as a smart camera. In this paper we propose to apply more powerful techniques...
SIMD (single instruction multiple data) processors have been shown to possess high capability for vector-based image processing due to their massively parallel architecture. However, it is not always easy to map the general-purpose processor implementations of high-level vision algorithms to such processors due to the hardware-imposed characteristics of SIMD processors. In this paper, our focus is...
While providing a variety of intriguing application opportunities, a vision sensor network poses three key challenges. High computation capacity is required for early vision functions to enable real-time performance. Wireless links limit image transmission in the network due to both bandwidth and energy concerns. Last but not least, there is a lack of established vision-based fusion mechanisms when...
SIMD (single-instruction multiple-data) processors have demonstrated high performance for vector-based image processing, thereby facilitating real-time vision applications. However, to fully exploit the advantages of the SIMD architecture, implementation of a given vision algorithm needs to undergo a mapping from a general purpose CPU programming style to a pixel parallel style. This paper describes...
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