B. Martín, A.B. Moreno, A. Sánchez, and E. Frías-Martínez (Spain)
Euclidean distance transforms, SIMD optimization, intrinsics, image analysis, pattern recognition.
This paper describes a SIMD optimization method for
computing different Euclidean distance algorithms.
Distance transforms have been widely applied to image
analysis and pattern recognition problems. The
proposed approach is based on the inherent fine and
medium-grain parallelism of considered distance
algorithms and has been implemented using Intel
Streaming SIMD Extensions (SSE), intrinsics and
VTune Analyzer. Experimental results show that
optimized prefetched SIMD algorithms improve by four
the number of execution cycles in comparison with the
initial SISD solutions.