Fast Grain Shape Determination Algorithm Based on Parallel Processing
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1 39 3 Vol.39, No.3 3 Opto-Electronic Engineering March, 3 5X(3 66 6,,,, (. ( Britton Chance (GPU (CUDA GPU (CPU 4 TP39 A.3969/j.issn.3-5X..3. Fast Grain Shape Determination Algorithm Based on Parallel Processing JIANG Ni, DUAN Ling-feng, YANG Wan-neng, LIU Qian, (. Brittion Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 4374, China;. Key Laboratory of Biomedical Photonics of Ministry of Education; College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 4374, China Abstract Grain shape is an important feature, which determines the grain quality and yield. Traditional manual determination of grain shape is time-consuming, laborious and subjective. A system for automatically determining the grain shape is presented based on a line-scan camera and a coveyor belt. In order to improve the grain shape determination efficiency, parallel processing technique based on Graphics Processing Unit (GPU is used. On the basis of Compute Unified Device Architecture (CUDA, the grain shape determination algorithm is optimized. The experiment result shows that the GPU-based accelerated algorithm can achieve a good effect on the measurement efficiency. When the number of grain in an image is approximately, the optimized algorithm gets a speedup of more than 4 times. Moreover, as the quantity of kernels in the images gets larger, the performance of the grain shape determination algorithm is improved more significantly. Key words grain shape; graphics processing unit; parallel processing; acceleration algorithm [-] [3] ( GB CDA jiangni_hust@6.com
2 [4] [5-7] [7-9] X [] (GPU [] [] [3] GPU [] GPU (CPU LCCD camera Grain Grain image Image pre-processing Calculate grain shape Save results Fig. The overall process of the measurement f(x y y l P i (x i y i num P 3(x 3, y 3 P (x y P (x y d P (x, y P P l P P L d ij ( xi - x j ( yi - y j ( L max( d ij ( d ij P i (x i y i P j (x j y j i j<num i j o P (x, y Fig. d P 4(x 4, y 4 f (x,y Computation of grain length and width x
3 68 3 l f(x y l P 3 (x 3 y 3 P 4 (x 4 y 4 l d l d l w d l d l w y - y l : y ( x - x y (3 x - x ( y - y( x - x - ( x - x( y - y d li (4 ( x - x ( y - y w max( d li - min( d (5 d li P i (x i y i l i<num GPU 3 li CPU Grain image x[i-][.], y[i-][.] num[i-], N GPU Calculate grain shape Calibration Per thread process one contour point, while per thread block process one grain Binaryzation Block Block Removing small particle Watershed segmentation Block i Block N- Block N Contour labeling Save results Results 3 Fig.3 The flowchart of the fast grain shape determination algorithm I b I b I e I b I e I c I c i x[i-][.] y[i-][.]
4 num[i] N 6 x[i-][.] y[i-][.] num[i] GPU (CPU 4 a b ( di max( di, di,, di( num- (6 L max( d i (7 d i i d i c d li d [4] 4 Calculate d Calculate d i Calculate dnum[k]- Calculate L Calculate d l Calculate d li Calculate dlnum[k]- Kernel Calculate w Get coordinates Calculate d Calculate d i Calculate dnum[k]- Calculate L Calculate d l Calculate d li Calculate dlnum[k]- Kernel N Calculate w 4 Fig.4 The flowchart of the fast grain shape determination algorithm (CUDA GPU GPU GeForce 98 GT N N i i d i L l
5 7 3 d li w CUDA f(x y f(x y L w d i d li 3 ( GPU CPU GPU CPU Intel (R Xeon(R GHz 3 GB GPU GeForce 98 GT Table Time cost and speedup of the optimized method No. CPU time/ms GPU time/ms Speedup No. CPU time/ms GPU time/ms Speedup GPU GPU GPU 45 GPU GPU Speedup The grain counts 5 Fig.5 The speedup of images with different grain counts
6 GPU 4 []. [J]. 4 8( LUO Yu-kun ZHU Zhi-wei CHEN Neng et al. Grain Types and Relatede Quality Characteristics of Rice in China [J]. Chinese Journal of Rice Science 4 8( [] LI Ji-ming Thomson Michael McCouch Susan R. Fine Mapping of a Grain-Weight Quantitative Trait Locus in the Pericentromeric Region of Rice Chromosome 3 [J]. Genetics (S [3] JIANG Gong-hao HONG Xue-yu XU Cai-guo et al. Identification of Quantitative Trait Loci for Grain Appearance and Milling Quality Using a Doubled-Haploid Rice Population [J]. Journal of Integrative Plant Biology (S ( [4] Brosnan Tadhg SUN Da-wen. Inspection and Grading of Agricultural and Food Products by Computer Vision Systems-a Review [J]. Computers and Electronics in Agriculture (S [5] Dalen G van. Determination of the Size Distribution and Percentage of Broken Kernels of Rice Using Flatbed Scanning and Image Analysis [J]. Food Research International (S [6] Emadzadeh B Razavi S M A Farahmandfar R. Monitoring Geometric Characteristics of Rice During Processing by Image Analysis System and Micrometer Measurement [J]. International Agrophysics (S [7] Yadav B K Jindal V K. Monitoring Milling Quality of Rice by Image Analysis [J]. Computers and Electronics in Agriculture (S [8] Yadav B K Jindal V K. Dimensional Changes in Milled Rice (Oryza sativa L. Kernel During Cooking in Relation to Its Physicochemical Properties by Image Analysis [J]. Journal of Food Engineering (S [9] WAN Y N LIN C M Chiou J F. Rice Quality Classification Using an Automatic Grain Quality Inspection System [J]. Transaction of the ASAE (S-35 45( [] DUAN Ling-feng YANG Wan-neng BI Kun et al. Fast Discrimination and Counting of Filled/Unfilled Rice Spikelets Based on Bi-modal Imaging [J]. Computers and Electronics in Agriculture (S [] Fialka O Cadik M. FFT and Convolution Performance in Image Filtering on GPU [C]// Information Visualization London July DC IEEE Computer Society []. [J]. 9 7( YANG Zhi-yi ZHU Ya-ting PU Yong. Study on Parallel Image Processing Based on Compute Unified Device Architecture [J]. Computer Measurement and Control 9 7( [3]. GPU Sobel [J]. 9 36( 8. ZUO Hao-rui ZHANG Qi-heng XU Yong et al. Fast Sobel Edge Detection Algorithm Based on GPU [J]. Opto-Electronic Engineering 9 36( 8. [4] Mark Harris. Optimizing Parallel Reduction in CUDA [EB/OL]. http //developer.download.nvidia.com/compute/cuda/_/ Website/projects/reduction/doc/reduction.pdf 7-.
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