PCNN Model Research Articles

A lightweight scheme for multi-focus image fusion

The aim of multi-focus image fusion is to fuse the images taken from the same scene with different focuses so that we can obtain a resultant image with all objects in focus. However, the most existing techniques in many cases cannot gain good fusion performance and acceptable complexity simultaneously. In order to improve image fusion efficiency and performance, we propose a lightweight multi-focus image fusion scheme based on Laplacian pyramid transform (LPT) and adaptive pulse coupled neural networks-local spatial frequency (PCNN-LSF), and it only needs to deal with fewer sub-images than common methods. The proposed scheme employs LPT to decompose a source image into the corresponding constituent sub-images. Spatial frequency (SF) is calculated to adjust the linking strength β of PCNN according to the gradient features of the sub-images. Then oscillation frequency graph (OFG) of the sub-images is generated by PCNN model. Local spatial frequency (LSF) of the OFG is calculated as the key step to fuse the sub-images. Incorporating LSF of the OFG into the fusion scheme (LSF of the OFG represents the information of its regional features); it can effectively describe the detailed information of the sub-images. LSF can enhance the features of OFG and makes it easy to extract high quality coefficient of the sub-image. The experiments indicate that the proposed scheme achieves good fusion effect and is more efficient than other commonly used image fusion algorithms.

Transmission line voltage classes identification based on particle swarm optimization algorithm and PCNN

ABSTRACTFor the current problems of the high voltage device of autonomous safety, warning is required manual input of the voltage classes. A method of dividing the insulator image based on the simplified model of PCNN is proposed to extract the number of insulator sheds of a string to independently identify the voltage of high voltage transmission line. Because the image segmentation effect of PCNN model is closely related to its many parameter tuning, however, the PSO algorithm is used to select the optimal PCNN parameter to realize the binary segmentation of the Insulator image in this paper, and then the transform angle of the image region is corrected so that the Insulator horizontal array, and then according to the shape of the Insulator binary image characteristics to extract the number of Insulator Sheds in order to achieve the transmission line voltage classes of self-identification. The experimental results show that the method can identify and extract the number of Insulator Sheds, and has strong practicability.

Saliency motivated improved simplified PCNN model for object segmentation

Abstract Based on the fact that PCNN and saliency detection method all can achieve the better simulation of HVS to locate the objects that have the most interests in an image, a novel approach for object segmentation, termed as saliency motivated improved simplified pulse coupled neural network (SM-ISPCNN) algorithm, is proposed in this paper. Instead of adopting pure gray-scale to activate the ISPCNN neurons, it is better to introduce the saliency feature value to motivate this model. The introduced saliency stimulus applies a sliding window to precisely exploit the distributions of the objects and surroundings, weakens the influence of background while retaining the region of interest; the highlight of this ASLFC saliency feature lies on: (1) the saliency estimation is based on semi-local areas instead of pixel level; (2) the estimations of the conditional distributions is manipulated via integral histogram approach; (3) the adaptive prior probability setting method is employed to achieve more promising saliency map. For improvement of convergence speed of SM-ISPCNN model for object segmentation, at each iteration, we regard top 5 regions as feedback input for next iteration, which can raise the robustness of SM-ISPCNN model against noise and other interferences. We demonstrate the proposed model based on the mammograms from open and common database of MIAS, gray images from Weizmann segmentation evaluation database and color images from public database with ground truth annotations. Compared with five competitive methods, our model has the obvious superiority for segmentation capacity and algorithm robustness, furthermore, it does not requires any training and can be used in various occasions of object segmentation. In addition, this method is verified on the mammograms from Gansu Provincial Cancer Hospital, the detection results reveal that this model has great potential in clinical application.

Target Recognition Using of PCNN Model Based on Grid Search Method

To improve the accuracy of face recognition using pulse coupled neural network（PCNN） model and save the problem that the parameters of PCNN model must be set with experience, the PCNN model based on pulse intensity (QD-PCNN) and the improved grid search method are proposed. In the QD-PCNN, pulse intensity can make the outputs of the model more accurate. When the improved grid search method is used to find the suitable parameters, the parameters are searched in a large space firstly, and then searched accurately around the parameters we have found according to the objects which are to be recognized. In the experimental process, the parameters obtained through improved grid search method is applied to QD-PCNN model to recognize faces, and the results show the efficiency of this method.

Object detection system based on multimodel saliency maps

Detection of visually salient image regions is extensively applied in computer vision and computer graphics, such as object detection, adaptive compression, and object recognition, but any single model always has its limitations to various images, so in our work, we establish a method based on multimodel saliency maps to detect the object, which intelligently absorbs the merits of various individual saliency detection models to achieve promising results. The method can be roughly divided into three steps: in the first step, we propose a decision-making system to evaluate saliency maps obtained by seven competitive methods and merely select the three most valuable saliency maps; in the second step, we introduce heterogeneous PCNN algorithm to obtain three prime foregrounds; and then a self-designed nonlinear fusion method is proposed to merge these saliency maps; at last, the adaptive improved and simplified PCNN model is used to detect the object. Our proposed method can constitute an object detection system for different occasions, which requires no training, is simple, and highly efficient. The proposed saliency fusion technique shows better performance over a broad range of images and enriches the applicability range by fusing different individual saliency models, this proposed system is worthy enough to be called a strong model. Moreover, the proposed adaptive improved SPCNN model is stemmed from the Eckhorn’s neuron model, which is skilled in image segmentation because of its biological background, and in which all the parameters are adaptive to image information. We extensively appraise our algorithm on classical salient object detection database, and the experimental results demonstrate that the aggregation of saliency maps outperforms the best saliency model in all cases, yielding highest precision of 89.90%, better recall rates of 98.20%, greatest F -measure of 91.20%, and lowest mean absolute error value of 0.057, the value of proposed saliency evaluation E H A reaches to 215.287. We deem our method can be wielded to diverse applications in the future.

Vehicle Recognition based on Pseudo invariant Linear Moment Features and ELM

of the problem of slow speed and low accuracy of the vehicle recognition in advanced driver assistance systems, a vehicle recognition method based on pseudo invariant linear moment features and ELM is proposed. Target edge is extracted by the improved PCNN model, according to the characteristic of multiple target features, the pseudo invariant linear moment features are extracted, then ELM model is used to train and recognize the databases. The validity of the model is verified through experiments, compared with other algorithms, the recognition accuracy of pseudo invariant linear moment features and ELM vehicle recognition method is higher and the speed is faster, which provides a new way to identify the vehicle in real-time monitoring system of the vehicle.

The recognition of landed aircrafts based on PCNN model and affine moment invariants

Currently the most research done on the recognition of landed aircrafts based on its shape feature lie in two cases: either the research has focused on different moment invariants algorithms, only using some already processed binary images to verify results, or the research refers to some integrated recognition system which has multi-step preprocessing. The better solution must consider preprocessing, simple computation and step reduction altogether when the high recognition ratio is guaranteed. For this better solution, we propose a landed aircrafts recognition method based on the PCNN model and affine moment invariants (PCNN+AMI). Our method uses the PCNN model to generate binary sequence of grey intensity images, then it extracts affine moment invariants from the sequence to compose features vector. The experimental results illustrate that our method cannot only get a good ability of anti-geometrical distortion but can also have simple computation and step reduction.

PCNN Model Automatic Linking Strength Determination Based on Geometric Moments in Image Fusion

In order to obtain a clear image that contains all relevant objects in all area, this paper proposes a new image fusion algorithm. The proposed method can adaptively determine the linking strengths of PCNN based on the regional image moment invariants. Compared with the traditional methods, the results of experiments show that the new algorithm enhances the fusion image information, effectively retains the source image edges and detail information. The effect of fusion and quantifying indicators are fairly good.

An Image Fusion Method Based on NSCT and Dual-channel PCNN Model

NSCT is one of useful multiscale geometric analysis tools, which takes full advantage of geometric regularity of image intrinsic structures. The dual-channel PCNN is a simplified PCNN model, which can process multiple images by a single PCNN. This saves time in the process of image fusion and cuts down computational complexity. In this paper, we present a new image fusion scheme based on NSCT and dual-channel PCNN. Firstly, the fusion rules of subband coefficients of NSCT are discussed. For the fusion rule of low frequency coefficients, the maximum selection rule (MSR) is used. Then, for the fusion rule of high frequency coefficients, spatial frequency (SF) of each high frequency subband is considered as the gradient features of images to motivate dual-channel PCNN networks and generate pulse of neurons. At last, fused image is obtained by using the inverse NSCT transform. In order to show that the proposed method can deal with image fusion, we used two pairs of images as our experimental subjects. The proposed method is compared with other five methods. The performance of various methods is mathematically evaluated by using four image quality evaluation criteria. Experimental comparisons conducted on different fusion methods prove the effectiveness of the proposed fusion method

Multi-focus image fusion algorithm based on compound PCNN in Surfacelet domain

In order to effectively retain details and suppress noise, a multi-focus image fusion method based on Surfacelet transform and compound PCNN is proposed. Surfacelet transform is a powerful multi-resolution analysis tool which is able to decompose the original image into a number of different frequency band sub-images, compound PCNN model is a combined model of PCNN and dual-channel PCNN which is to select the fusion coefficients from the decomposed coefficients, the Local sum-modified-Laplacian (LSML) is selected as external stimulus of compound PCNN, fusion coefficients are decided by compound PCNN. The experimental results show that the new method has a good performance, fusion image has more texture details and it is more similar to the original images, the objective evaluation indexes show that this method is superior to the traditional image fusion methods.

An Image Segmentation Method Using Image Enhancement and PCNN with Adaptive Parameters

PCNN model is particularly suitable for image segmentation and edge extraction, but its effect depends on the selection of parameters in PCNN model and network iteration settings, which needs for a large number of artificial interaction and has limited PCNN image processing practicality. In this paper, through combining statistical properties of images and PCNN model, we present an adaptive algorithm based on the distribution of pixels to replace the artificial interaction. Experimental results show that image segmentation using image enhancement and PCNN with adaptive parameters is significantly better than the traditional PCNN image segmentation and verify the effectiveness of the method.

An Optimization Control Algorithm for Containers Relocation Based on PCNN Model

摘要: 集装箱的堆存状态与理想发箱顺序很难保持一致, 翻箱操作是不可避免的. 为降低堆场的翻箱率, 提高作业效率, 在获得集装箱的取箱顺序前提下, 将每取一个集装箱所产生的可能状态视为一个状态结点, 所需的翻箱次数加一个基数作为状态结点间的连接权, 把翻箱优化问题转化为最短路径求解问题. 脉冲耦合神经网络(Pulse-coupled neural network, PCNN)具有独特的自动波并行传播的特性, 适用于求解大规模实时问题, 能一次求出源点到其他所有目标点的最短路径, 从而获得最优的翻箱方案. 其所需要的计算量仅正比于最短路径的长度, 与路径图的复杂程度及路径图中的通路总数无关. 这为建立集装箱的智能控制系统奠定了坚实的理论基础. 关键词: 翻箱 / 最短路径 / 堆场 / 智能控制 / 脉冲耦合神经网络

Multi-focus Image Fusion Based on The Nonsubsampled Contourlet Transform and Dual-layer PCNN Model

Multi-focus Image Fusion Based on The Nonsubsampled Contourlet Transform and Dual-layer PCNN Model

Edge Detection of Color Image Using Unit-Linking PCNN

PCNN model has the characteristic of similar group neurons releasing pulse synchronously, which is suitable for image segmentation that performs logarithmic transformation on luminance component of color image in order to accord with human visual characteristics. During the segmentation process of R, G, B three components of color images, choosing neuron key parameters by genetic algorithm, controlling iteration by skewness indicators, achieving edge detection of split graphs from R, G, B three components in the model of Unit-Linking PCNN, and getting final results by the strategy of weighted combination. Simulation results show that the approach can embody more contour details of images, and is more self-adaptive.

A Novel Method of Image Segmentation Based on Parallelized Firing PCNN

摘要: 提出一种并行点火脉冲耦合神经网络(Parallelized firing pulse coupled neural networks, PFPCNN)模型的图像分割方法. 首先用改进的Unit-linking PCNN (ULPCNN)模型对图像进行增强, 便于后续的图像分割. 然后采用PFPCNN新模型对增强后的图像进行分割, 最后用最大香农熵方法判定最佳分割结果. 各种复杂场景下的仿真实验及定量评价表明, 本文提出的图像分割方法, 其效果明显优于常规的PCNN分割方法. 关键词: 脉冲耦合神经网络 / 并行点火模型 / 图像增强 / 最大香农熵 / 图像分割