Abstract: In general, the color of the image taken by a camera changes when the illumination condition changes. However, human being can perceive almost the true color independent of the illumination (color constancy). The low dimensional image generation model has been discussed as a framework for eliminating illumination influence. It defines how the image color is determined from the illumination color, the object color and the camera spectral sensitivity. Traditionally, the low dimensional image generation model has been identified by setting object color basis functions, illumination color basis functions and the camera spectral sensitivity. In this paper, we propose a method for generating the image generation model by training without specifying any basis functions or camera spectral sensitivity. In that purpose, we prepare some sample images including various colors. Using these data, the image generation model is optimized. The experimental results are shown to confirm the effectiveness

• Publication Date: 10-Dec-2017
• DOI: 10.15224/978-1-63248-138-2-09
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Abstract: This work is aimed to investigate and to improve the performance of multiple sequence alignment software ClustalW on the test platform EURORA at CINECA, for the case study of the influenza virus sequences. The objective is code optimization, porting, scaling and performance evaluation of parallel multiple sequence alignment software ClustalW for Intel Xeon Phi (the MIC architecture). For this purpose a parallel multithreaded optimization including OpenMP has been implemented and verified. The experimental results show that the hybrid parallel implementation utilizing MPI and OpenMP provides considerably better performance than the original code.

• Publication Date: 10-Dec-2017
• DOI: 10.15224/978-1-63248-138-2-10
• Views: 0
• Downloads: 0