Applied Mechanics and Materials Online: 2013-02-13 ISSN: 1662-7482, Vols. 291-294, pp 2936-2940 doi:10.4028/www.scientific.net/amm.291-294.2936 2013 Trans Tech Publications, Switzerland The parallel design and implementation of the PCNN algorithm based on the visual perception information Yanming Zhao 1, a, Yong Wang 2, b 1 Department of Mathematics and Computer, Hebei Normal University for Nationalities, Hebei 067000, China a Zhaoyanming007@163.com Keywords: pulse coupled neural network, Gabor, visual perception information, Computer cluster, Parallel Algorithm Abstract. The PCNN algorithm based on the visual perception of information can better analyze and understand the natural essence of image. But the multi-parameter settings and high computational complexity restrict the application of the algorithm in the industrial real-time image processing. Based on this, The parallel design and implementation of the PCNN algorithm based on the visual perception information is proposed. The PCNN algorithm is improved by the visual perception method, through analyzing parallelization of the feasibility of the algorithm, the parallel algorithm running at the cluster and experimental environment are developed. The performance of the parallel algorithm is verified by industrial image on the cluster, the experimental results show that the parallelized the PCNN algorithm has a better scalability and speedup. Introduction In 1990, Eckhorn et al proposed a neural network model based on the cat's visual cortex nerve signal conduction characteristics [1]; 1999, Johnson improved the model as suitable for image processing, which was called the PCNN [2]. The model can be successfully applied to Digital image processing, analysis and understanding. And widely used in the field of image segmentation, edge extraction, coding, enhancement, integration, and target recognition [3,4,5]. But the model of multi-parameter settings and high computational complexity restrict applications in industrial image processing. The perception computing of visual information reveals that biological visual perception can understand and express image of the natural essential characteristics of the pixel-level. Biological visual perception of information has a strict the Gabor mathematical model, and can realize algorithmization. Therefore, the network parameters are determined adaptively by biological visual perception. The proposed can reveal image of the natural essential characteristics. The improved PCNN model has a very high computational complexity, which restricts the application of the algorithm in industrial image processing. Parallel computing theory has been widely applied to parallel transformation of the image processing algorithms, and to provide solutions for the high computational complexity of image algorithm practical [6,7,8,9]. Based on this, Design and implementation of parallelization of the improved algorithm is proposed. First the paper analyzes the parallelization of PCNN network model, which based on visual perception information. Then parallel algorithm is designed by the cluster computing theory and message passing programming model MPI. Aiming to parallel processing of industrial image, the parallel algorithms have achieved good speedup and scalability. All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, www.ttp.net. (ID: 130.203.136.75, Pennsylvania State University, University Park, USA-18/05/16,02:53:33)
Applied Mechanics and Materials Vols. 291-294 2937 The PCNN algorithm based on the visual perception Fig.1 shows neuron model which Eckhorn proposed [1]. Neurons cell is made of the feedback input field, the coupling of the input field and the pulse generator. Fig. 1.The neurons structure of traditional PCNN The following discrete system model is described by the iterative difference equation style. F F ( n) e F ( n 1) V M Y ( n 1) S F L L ( n) e L ( n 1) V W Y ( n 1) L (1) (2) U ( n) F ( n)(1 L ( n)) (3) Y 1 U ( n) E 0 others ( n 1) (4) E E ( n) e E ( n 1) V Yi ( n) E j (5) Where s is the value of the gray value of the input image or the transformation. F, L, U and Y correspond to the feedback input, coupled input, internal activity item and neuronal cell output of (i, j) neurons cell. M, W is the matrix of connection coefficient. is connection strength coefficient of the nerve synapse.αf and VF, αl, and the VL, αe and VE correspond to the amplification factor and the decay time of the feedback domain, the amplification factor of the coupling domain and the decay time, decay coefficient of E and the dynamic threshold inherent EMF. The equations (1)(2)(3)(4) reveal the window function W and M determines the ignition timing and algorithm performance.therefore the window functions are determined adaptively by visual perception of information of image and Gabor function. This method should improve the analysis and understanding capacity of image. Receptive field and Gabor function Receptive field is the region reflected the stimulation of visual neurons cell. The two-dimensional Gabor function, which proposed by John Daugman, be adjusted by the scale and direction to describe the receptive fields of the spatial properties of primary cortex simple and complex cell. Two-dimensional Gabor function is shown in equation (6).
2938 Advances in Energy Science and Technology h x, y) g( x', y')[cos(2 f x') jsin(2 f ')] (6) ( 0 0y The parameter θ is the orientation of the Gabor filter, f0is center frequency, x and y is the Gaussian variance in the airspace x 'and y' direction. Utilizing the filters of Gabor, the output of the point (x0, y0) at the local Area is shown in equation (7). R( x, y) I( x, y) h( x x0, y y0) (7) Where X0, Y0 is equivalent to receptive field center position, * denotes the convolution operation. The equations (6) (7) reveal the Gabor function can effectively extract the visual information of regional image by Changing the parameter of θ, x and y. The different and same cortical receptive fields of the mathematical model are achieved. Through the convergence role of the neural network, the senior cortical cell receptive fields of mathematical models are realized by simple visual cells receptive fields. Therefore, the window W, M, of the PCNN is settled adaptively by optimal scaling and optimum direction of the Gabor function. The principle of optimization is the energy maximum response method. Parallel algorithm design The Multi-scale and multi-directional Gabor and PCNN algorithm is an independent dataset; each pixel has the same mathematical model, and has the same algorithm. The datasets have relative independence; the algorithm is suit for the Parallel design of SPMD. Appropriate segmentation method of dataset can effectively reduce the number of communications in the inter-nodes, and improve processing speed. Therefore, the parallel algorithm, which runs at the cluster of computers, is as follows: Step1: Initialize computing environment. The control computer counts computing nodes and node resource allocation of the cluster, generates the resource table of cluster compute nodes. Data set is divided into effectively a subset by the number of available nodes in the table.and a list of computable resources is created. Based on the upper the upper tables, the first batch of computing tasks assign to computable nodes, and create the computing dynamic table, and record the assigned start time in the computing dynamic table. Initialize Computable task request queue Step2:The improved the PCNN algorithm analysis and understand the data set, which is assigned by step 1.when calculations finished, the processing results and request of the next calculation are submitted to the control computer. Step3:The control computer seek computable task request queue, and modify the time item of the computing dynamic table and the node state of the resource table of cluster compute nodes. The computable task allocation object is decided by the performance of the requested node (end time and distribution of the time difference) and a list of computable resources. When there are no computing request tasks at the computable task request queue, the control computer send check signal of scalability to all node. Modify the resource table of cluster compute nodes; the method is advantage to check the increasing dynamically node and Faulty node. When faulty nodes are in the cluster, computable dusks will be assigned again. Step4:When the list of computable resources is empty, and all notes are idle. The control computer Synthesize the final result of the processing.
Applied Mechanics and Materials Vols. 291-294 2939 Experiment and analysis The proposed of experimental environment include hardware, software environment and dataset. Experimental environment configuration is shown in Table 1. Table 1 Experimental environment and Data sets. Hardware configuration of cluster Software configuration Data set[pixels] CPU MEMORY Intel(R) Core(TM) i5-2300,2.8ghz DDR3 4GB OS Linux(fedora 14) DS1 520M(21000*13000) Compilation environment GCC4.5.1 DS2 145MB(18020*9010) HARDDISK 7200 SATA 500G IDE Eclipse DS3 68.7MB(6000*6000) Parallel algorithm performance is tested by the speedup and scalability at the cluster. The experimental results are shown in the Fig.2, Fig.3 and Table 2. Fig.2 Relation of the speedup and thdata subset. Fig.3 Relation of the data subsets and nodes. Fig.2 shows that, when the size of window function is not variable, the speedup increase gradually with the size of data sets. The reason is the effect of redundant data to communication time is getting smaller and smaller. The redundant data use Gabor transform of Boundary points. Fig.3 shows that, when the number of nodes is not variable, the speedup increase gradually with the number of data subset. DS1 DS2 DS3 Processing efficiency Table 2 Relation of scalability and increasing nodes The number of node 2 3 5 7 9 11 Speedup 0.99 1.96 2.97 3.80 4.75 5.80 Parallel efficiency 0.99 0.98 0.96 0.97 0.92 0.89 Speedup 0.99 1.86 2.92 3.70 4.55 5.76 Parallel efficiency 0.97 0.94 0.92 0.90 0.89 0.87 Speedup 0.99 1.91 2.89 3.85 4.65 5.56 Parallel efficiency 0.95 0.92 0.91 0.87 0.88 0.82 Table.2 shows that the speedup increases gradually with the computable nodes, which derive from the node finishing other computing dusks and the Repairing error node.
2940 Advances in Energy Science and Technology Conclusion The PCNN network based on visual perception can analyzes and understands the nature of image from the receptive field. But Multi-parameter settings and high computational complexity restrict algorithm to use at the field of industrial image processing. Based on this, Design and implementation of parallelization of the improved algorithm is proposed. First the paper analyzes the parallelization of PCNN network model, which based on visual perception information. Then parallel algorithm is designed by the cluster computing theory and message passing programming model MPI. Aiming to parallel processing of industrial image, the parallel algorithms have achieved good speedup and scalability. Acknowledgments This work has been supported by the Scientific Research Project of Hebei Colleges and Universities in 2012(Grant No.Z2012127), the Hebei Education Science planning during the Twelfth Five-year Plan Period (Grant No. 11100053). References [1] Dj.M. Maric, P.F. Meier and S.K. Estreicher: Mater. Sci. Forum Vol. 83-87 (1992), p. 119 [1] Eckhorn R, Reitboeck H J, Arndt M, Dicke P. Neural Computation. Papers 2 (3) (1997),P293. [2] Johnson J L, Padgett M L.PCNN Models and Application. IEEE Trans on Neural Net Works, Papers 10(3) (1999), P554. [3] Lingbo Deng. Journal of Projectiles, Rockets, Missiles and Guidance. Papers 28(3) (2008),P237. [4] Yinmao Song, Guole Liu. Journal of Circuits and Systems. Papers 15(1) (2010),P151. [5] Kuntimad G, Ranganath H S. Perfect Image Segmentation Using Pulse Coupled Neural Networks.IEEE Trans. on Neural Networks. Papers 10(3) (1999),P591. [6] Zhi Zeng, Renyi Liu, Xiantao Li, Feng Zhang, Weizheng Bao. Journal of Zhejiang University (Science Edition), Vol.39 (2), 2012, P226. [7] GuiYan Jiang,Guiling Zhang,Dakun Zhang.Journal of Computer Research and Developme -nt.vol49(5),2012,p1130. [8] Siqian Zhang,Guo Cheng,Luo Cheng,Xiong Wei.Computer Science.Vol.39(1),2012,P295. [9] Qingkun Liu, Mingwei Ma, Weichun Yan. Computer Applications.Vol.31(12),2011,P3328.
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