The Application Of Pulse Coupled Neural Network In Image Procession

Abstract:Image processing approaches and their applications inspired by human vision mechanism have become one of most active topics in digital image processing field recently. The general framework is to develop a mathematic model for human vision mechanism and apply it to a specific image processing task. Generally, there are three principle categories in recent research:the representation and modeling for human vision perceptual information, modeling for mechanism of human visual neurons and its working mechanism, and modeling for the function mechanism of visual cortex and its information processing mechanism. As one of the most successful computational models, PCNN has become one of the most important accesses to studying image processing based on mechanism of human visual neurons and its working mechanism. However, PCNN's intrinsic characteristics cannot be demonstrated with elegant mathematic methods, which laminate its application in image processing. Therefore, solving the above issue is of great significance to improve the performance of PCNN and motivate new image processing methods based on human vision mechanism.In order to solve the aforementioned issue, we analyze the working mechanism of visual cortex neurons. In this dissertation, the study object is PCNN, and our goal is to solve the aforementioned issue in PCNN and improve its performance in image processing. Our research mainly focuses on the following three aspects. First, we discuss the characteristics of the key parameters in PCNN and their applications in image processing. Second, we study the task dependent PCNN and apply it to image processing. Finally, we introduce the adaptive PCNN and its application in image processing. The key contributions of this thesis lie in three-fold:1. Aiming at the issues that the computation complexity of the standard PCNN is high and numerous parameters have to be set without any regulation when applying to image processing, we propose a novel method for image detection via the updated PCNN, mainly focusing on the key parameters. The proposed method improves the performance of the standard PCNN and simplifies it so that it meets the needs of edge detection in a more rational way. Meanwhile, in edge detection, difference parameter setting strategies are introduced according to the updated key parameters in PCNN. A four step method is proposed to decrease the parameter number of the standard PCNN from9to4:(1) setting the connection coefficient ? according to the local gray level,(2) setting the weight matrix according to the dissimilarity between two neurons,(3) computing the amplification coefficient VE and time attenuation constants aE based on the local gradient,(4) deciding the optima number of iteration N based on the maximum variance ratio. Experimental results show that our method outperforms the standard PCNN and ICM with higher edge detection precision and our detection results meet the needs of human visual perception better.2. Focusing on the ambiguous relationship between the PCNN and image processing tasks, which adversely affects PCNN's application, we present a novel shadow detection method, which combines the discrete coefficients of the image and the lateral inhibition of the PCNN. Meanwhile, a new image fusion method is proposed, where brightness features of human vision and image intensity contrast are considered. The proposed method focuses on the relationship between the mathematic formulation of the specific image processing task and PCNN to guide the running state when applying PCNN into image processing and improve the performance further. In terms of shadow detection based on PCNN, first we introduce the lateral inhibition to improve the discrimination ability to pixels with similar intensity from different regions. Then we introduce the shadow coefficient feature to guide the shadow detection. In terms of the image fusion based on PCNN, we first present Main-Auxiliary PCNN model, and then guide the image fusion via establishing the relationship between PCNN and image fusion by combining the contrast and intensity features.3. We propose an adaptive immune clone PCNN based image segmentation method to address the problem that the standard PCNN cannot be described definitely in mathematic language, which results that the running state of the PCNN cannot be adjusted to the specific image processing task. We formulate the image segmentation problem as an optimization problem of immune clone algorithm to change the PCNN running state dynamically. First, the adaptive operation and gradient are added into the standard immune clone algorithm to accelerate the convergence. Then the standard PCNN is simplified, and its corresponding parameters are viewed as antigen of biological immune system while the entropy of the segmentation result is the antibody. After a series of dynamic clonal variation, we finally obtain the segmentation result.We compare our methods with PSO-PCNN, standard immune clone based PCNN, PCNN, ICM, PSO-ICM and other methods, the experimental results indicate that our method can dynamically adapt to the segmentation task and outperforms the state-of-the arts.