Scalable data parallel implementations of object recognition using geometric hashing
Journal of Parallel and Distributed Computing - Special issue on data parallel algorithms and programming
Exploiting SIMD parallelism in DSP and multimedia algorithms using the AltiVec technology
ICS '99 Proceedings of the 13th international conference on Supercomputing
Complete Guide to Mmx Technology
Complete Guide to Mmx Technology
The Long And Winding Road to High-Performance Image Processing with MMX/SSE
CAMP '00 Proceedings of the Fifth IEEE International Workshop on Computer Architectures for Machine Perception (CAMP'00)
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Streaming SIMD Extensions (SSE) is a unique feature embedded in the Pentium III and Pentium IV classes of microprocessors. By fully exploiting SSE, parallel algorithms can be implemented on a standard personal computer and a significant speedup can be achieved comparing to sequential code. PCs, mainly employing Intel Pentium processors, are the most commonly available and inexpensive solutions to many applications. Therefore, the performance of SSE in common image and signal processing algorithms has been studied extensively in the literature. Nevertheless, most of the studies concerned with low-level image processing algorithms, which involves pixels in pixels out type of operations. In this paper, we study higher-level image processing algorithms where image features and recognition is the output of the operations. Hough transform and Geometric hashing techniques are commonly used algorithms for this purpose. Here, their implementation using SSE are presented.