Performance of CPU-GPU parallel architecture on segmentation and geometrical features extraction of Malaysian Herb Leaves

Abstract

Image recognition includes the segmentation of image boundary, geometrical features extraction, and classification is used in the particular image database development. The ultimate challenge in this task is it is computationally expensive. This paper highlighted a CPU-GPU architecture for image segmentation and features extraction processes of 125 images of Malaysian Herb Leaves. Two (2) GPUs and three (3) kernels are utilized in the CPU-GPU platform using MATLAB software. Each of herb image has pixel dimensions 16161080. The segmentation process uses the Sobel operator, which is then used to extract the boundary points. Finally, seven (7) geometrical features are extracted for each image. Both processes are first executed on the CPU alone before bringing it onto a CPU-GPU platform to accelerate the computational performance. The results show that the developed CPU-GPU platformhas accelerated the computation process by a factor of 4.13. However, the efficiency shows a decline, which suggests that the processors utilization must be improved in the future to balance the load distribution.