Simplified parameters model of PCNN and its application to image segmentation

Image segmentation is a very important aspect in the field of image processing. According to certain principles, it will be divided into a number of small regional disjoint. Small region is a sense sets of common attributes of the connectivity pixels. The so-called connectivity which refers to the fact that any two points in the pool existing a connectivity path between all belong to the set. Pulse coupled neural networks was proposed based on the brain simultaneously pulsed release phenomenon of the visual cortex of the cats, monkeys, and other animals, the similar input data can be outbreak synchronized through interrelated neurons. The digital image can be processed by pulse coupled neural networks, the pixels of space based approach and brightness can be made up to a group. Pulse coupled neural networks has a good nature in image processing applications, which can effectively for the various aspects of image processing, and it can be used in the image processing system. Experimental results show that the method can achieve better image segmentation and have a common applicability.

In this paper, a review of parameter optimization methods of pulse-coupled neural networks (PCNNs) is presented. Considering that PCNN has been used in image processing for many years, the aim of this paper was to provide an overview of the work that has been done and to serve as a useful reference for those who are looking for PCNN parameter optimization methods and those who are researching PCNN applications for a specific field. This paper first briefly reviews the PCNN model, including the standard PCNN and several variants of PCNN. Then, we emphasize the optimization methods for PCNN’s parameters, describing three types of parameter optimization methods in detail. Next, the paper summarizes the applications of the optimized models of PCNN with adaptive parameters in image segmentation, image fusion, image denoising and edge detection.

Image analysis for airborne reconnaissance and missile applications

In order to overcome the lacking of Shift invariance in Contourlet Transform, enable the image fusion to be in accord with human vision properties, Nonsubsampled Contourlet Transform (NSCT) and Pulse Coupled Neural Networks(PCNN) were used jointly in image fusion algorithms. Original images were decomposed to get the coefficients of low frequency sub bands and high frequency sub bands. The coefficients of low and high frequency sub bands were processed by a modified PCNN. Matching degree of original images is defined and used in fusion rules. Fusion image was obtained by NSCT inverse transformation. Experimental result shows this method is better than Wavelet, Contourlet and traditional PCNN methods, it has bigger mutual information, so the fusion image include more original image's information.

It needs set parameters on image segmentation based on PCNN (Pulse Coupled Neural Network) now. This paper points out the new method for medical image segmentation based on improved PCNN and Tsallis entropy. The new methods can automatically segment the medical images without selecting the PCNN parameters. It gets the best results with combining with the Tsallis entropy. The new method is very useful for PCNN application in the medical images segmentation.

Visual object tracking is a fundamental research topic in computer vision. In this paper, we proposed a novel hybrid tracking method based on Pulse Coupled Neural Network (PCNN) and Multiple Instance Learning (MIL). Most modern trackers may be inaccurate when the training samples are imprecise which causes drift. To resolve these problems, MIL method is introduced into the tracking task, which can alleviate drift to some extent. However, the MIL tracker may detect the positive sample that is less important. PCNN is different from traditional artificial neural networks, which can be applied in many image processing fields, such as image segmentation. So, the PCNN was employed as sample detector which can know the most important sample when training the classifier. Then, a more robust and much faster tracker is proposed to approximately maximize the bag likelihood function. Empirical results on a large set of sequences demonstrate the superior performance of the proposed approach in robustness, stability and efficiency to state-of-the-art methods in the literature.

It is the key step of automatic apple picking and sorting to segment of the apple image in natural environment using image information processing technology. In order to improve the intellectualization of apple picking process and effectively extract the target of image segmentation, a novel image segmentation method of Huaniu apple based on pulse coupled neural network (PCNN) and watershed algorithm is proposed. Firstly, the PCNN model is improved and optimized on the basis of image RGB to L*A*B space conversion. Then, the improved PCNN combined with watershed algorithm was used to segment the apple image on considering the image texture and color feature information. The theoretical analysis and experimental results show that the novel method is effectively able to segment target image, and can preserve the image details and the edges as well. The proposed algorithm has better subjective vision effect and objective quality than the traditional watershed and other related algorithms.

This paper discusses some of the advantages and disadvantages of pulse-coupled neural networks (PCNNs) for performing image segmentation in the realm of medical diagnostics. PCNNs were tested with magnetic resonance imagery (MRI) of the brain and abdominal region and nuclear scintigraphic ventilation/perfusion imagery of the lungs (V/Q scans). Our preliminary results show that PCNNs do well at contrast enhancement. They also do well at image segmentation when each segment is approximately uniform in intensity. However, there are limits to what PCNNs can do. For example, when intensity significantly varies across a single segment, that segment does not properly separate from other objects. Another difficulty is finding the optimum parameter values so that a uniform response is achieved over a set of imagery. Also, a set of parameters that properly segments objects in one image is sometimes unsuccessful on a similar image.

Light field can generate a set of refocused images by integrating rays into any image plane. It is useful to segment focus region from the refocused image because the focus region contains depth clues. However, to the best of our knowledge, focus-region segmentation for light field images has not been reported. We firstly transform the refocused image into the space of neuron ignition sequence with pulse coupled neural networks (PCNN). Then the model of neuron ignition sequence and the classification criteria are established to divide pixels into different regions. Finally, the clearest region is selected as the segmentation result by the sliding window technology. In this paper, we analyze the features of focus/defocus information in the space of neuron ignition sequence, and propose some simple criteria for pixel classification. The proposed algorithm and other two typical segmentation algorithms for low depth-of-field images are tested on the data set of light field images. The experimental results show that the three algorithms have similar error rates of focused-region segmentation, but the computational time of ours is one order of magnitude shorter than that of the other two.

Pulse-coupled neural network (PCNN) is a biologically-inspired algorithm suited for image processing. However, determining a set of parameters involved in the alteration of the neural behaviour remains a prevalent research for further application. To apply the model into image segmentation, this study proposes an extended PCNN model by using a strategy of the decision tree, and establishes links between the parameters and image characteristics. Particularly, the adjustable threshold term, interacted with the estimation of the global neural threshold, enables the proposed model to obtain the better results with the use of the fuzzy set theory. Through iterative computation, the proposed model can be considered as a region growing approach for multilevel image segmentation, thus named as an extended PCNN model. Finally, experiments on synthetic and natural images demonstrate the efficiency of the proposed model. Moreover, comparisons with some existing PCNN-based models, and recently graph-based methods, normalised cuts, show that the proposed model can extract regions with more similarity.

Mega voltage X-ray images are often characterized by low contrast, low resolution and image blurring. We explore efficient image segmentation of different types of mega voltage X-ray images using a specific neural processing technique called pulse coupled neural network (PCNN). We also study the usefulness of PCNN in ambient noise removal for the high voltage X-ray images.

License plate segmentation is a key technology in the process of license plate location and recognition. How to realize automatic segmentation of license plate image under complex illumination conditions has been a hot issue in intelligent transportation system (ITS). This paper deals with license plate image segmentation under a variety of lighting conditions. Based on the adaptive segmentation of license plate images by the Pulse Coupled Neural Network (PCNN), the relationship between the license plate image contrast and the PCNN iteration entropy is analyzed. An adaptive segmentation algorithm for license plate image using Deep Neural Network (DNN) to select the optimal result is proposed, and the selected segmentation image is filtered by the connected domain, which lays a foundation for subsequent license plate location, character segmentation and recognition. Simulation experiments show that the proposed algorithm performs better license plate segmentation and optimal selection for license plate images under various lighting conditions.

Pulse-coupled neural network (PCNN) model is widely used in digital image processing, but it is always a difficult problem to set network parameters and determine the optimal segmentation. By analyzing the firing characteristics and network parameters setting for the non-coupled linking PCNN, we propose an improved non-coupled linking PCNN for image segmentation. The model introduce the coupling effect of neighboring neurons into the dynamic threshold subsystem, and using a combination of DNN network, manual adjusting on step length for setting the dynamic threshold initial value. When the dynamic threshold initial value is adjusted properly, the optimal segmentation for the image can be obtained. Using the proposed algorithm in image segmentation of Lena and mammographic images, the segmentation effect similar to that of the traditional model can be obtained by less iteration, and it shows faster speed and better robustness.

This paper proposes a method of drunkenness detection based on analysis of thermal infrared facial images. The method consists of the following processing stages: (a) thermal infrared image acquisition; (b) Pulse-Coupled Neural Network (PCNN) image segmentation; (c) feature selection using the Principal Component Analysis (PCA) algorithm cascaded with the Linear Discriminant Analysis (LDA) algorithm; (d) Support Vector Machine (SVM) classification. We have built an experimental thermal infrared facial image database of 10 subjects (6 males and 4 females). The thermal images of each subject have been acquired both for sober condition and also for inebriation condition obtained after the person drank a specific amount of alcohol. The parameters of the PCNN have been optimized using a genetic algorithm. Using the proposed thermal image analysis cascade based on PCNN, we have obtained a drunkenness detection score of 97.5%.

Pulse-Coupled Neural Network (PCNN) is a new kind of network, which is constructed by simulating the activities of the mammal's visual cortex neurons. PCNN is suitable for image processing, including segmentation, image fusion, edge extraction. In this paper, a new method is proposed which is the use of Bidirectional search Pulse-Coupled Neural Network (BPCNN) in the color image segmentation. The validity of this method is well verified by the analysis and experiments.