Pulse Coupled Neural Network Research Articles

Random-Coupled Neural Network

Improving the efficiency of current neural networks and modeling them on biological neural systems have become prominent research directions in recent years. The pulse-coupled neural network (PCNN) is widely used to mimic the computational characteristics of the human brain in computer vision and neural network fields. However, PCNN faces limitations such as limited neural connections, high computational costs, and a lack of stochastic properties. This study proposes a random-coupled neural network (RCNN) to address these limitations. RCNN employs a stochastic inactivation process, selectively inactivating neural connections using a random inactivation weight matrix. This method reduces the computational burden and allows for extensive neural connections. RCNN encodes constant stimuli as periodic spike trains and periodic stimuli as chaotic spike trains, reflecting the information encoding characteristics of biological neural systems. Our experiments applied RCNN to image segmentation and fusion tasks, demonstrating its robustness, efficiency, and high noise resistance. Results indicate that RCNN surpasses traditional methods in performance across these applications.

Research on fractional wavelet transform combined with parameter adaptive PCNN for infrared and visible image fusion algorithm

Infrared and visible image fusion holds significant value across various fields due to its ability to provide complementary information. However, existing fusion algorithms often suffer from susceptibility to external physical conditions of source images, leading to inconsistent fusion quality and limited adaptive capability. In this paper, we propose a fractional wavelet transform fusion algorithm for infrared and visible images. This algorithm uses fractional wavelet domain image decomposition to more accurately locate the image structures of different scales. Initially, we perform image decomposition using a non-subsampled shearlet transform (NSST) with multi-scale and multi-directional decomposition. Subsequently, to effectively fuse detailed information, we employ discrete fractional wavelet transform (DFRWT) to decompose low-frequency subbands while selecting an appropriate p-value. High-frequency subbands are fused using a parameterized adaptive pulse-coupled neural network (PCNN) model. To validate the superiority of our algorithm, we conduct visual and quantitative comparative analyses against several existing algorithms. The results confirm that our proposed algorithm exhibits robustness against external physical conditions and surpasses these existing algorithms, making it highly applicable for infrared–visible fusion tasks.

Alzheimer's disease stage recognition from MRI and PET imaging data using Pareto-optimal quantum dynamic optimization

The threat posed by Alzheimer's disease (AD) to human health has grown significantly. However, the precise diagnosis and classification of AD stages remain a challenge. Neuroimaging methods such as structural magnetic resonance imaging (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to diagnose and categorize AD. However, feature selection approaches that are frequently used to extract additional data from multimodal imaging are prone to errors. This paper suggests using a static pulse-coupled neural network and a Laplacian pyramid to combine sMRI and FDG-PET data. After that, the fused images are used to train the Mobile Vision Transformer (MViT), optimized with Pareto-Optimal Quantum Dynamic Optimization for Neural Architecture Search, while the fused images are augmented to avoid overfitting and then classify unfused MRI and FDG-PET images obtained from the AD Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets into various stages of AD. The architectural hyperparameters of MViT are optimized using Quantum Dynamic Optimization, which ensures a Pareto-optimal solution. The Peak Signal-to-Noise Ratio (PSNR), the Mean Squared Error (MSE), and the Structured Similarity Indexing Method (SSIM) are used to measure the quality of the fused image. We found that the fused image was consistent in all metrics, having 0.64 SIMM, 35.60 PSNR, and 0.21 MSE for the FDG-PET image. In the classification of AD vs. cognitive normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, the precision of the proposed method is 94.73%, 92.98% and 89.36%, respectively. The sensitivity is 90. 70%, 90. 70%, and 90. 91% while the specificity is 100%, 100%, and 85. 71%, respectively, in the ADNI MRI test data.

Self-supervised change detection of heterogeneous images based on difference algorithms

ABSTRACT The presence of heterogeneous image disparities often leads to inferior quality in the generated difference images during change detection. This paper proposes a self-supervised change detection of heterogeneous images based on a difference algorithm. Firstly, a combination of phase consistency and a simplified pulse-coupled neural network (PC-SPCNN) is used to fuse the heterogeneous images, and the result is used to compute the difference image (DI). The new DI generation method can generate the standard and exponential difference images. Secondly, the hierarchical FCM clustering algorithm is improved to extract stable and correct self-supervised samples by difference images so that the clustering process is not overly dependent on thresholds. Then, the support vector machine classifier is trained based on the heterogeneous images, the fused images, and self-supervised sample sets, and the information from the fused images is utilized to increase the feature dimension for better detection of changes. Finally, the support vector machine classifier automatically detects whether the intermediate pixels are changed and produces the change detection results. The experimental results confirm the improvements made by the proposed method in difference image extraction, training sample selection, and clustering algorithm, and the stability of the method exceeds that of the state-of-the-art change detection methods.

Revolutionizing the Application of Automatic Inspection System for Industrial Parts Using AI Machine Vision Technology

This paper addresses the challenges of large data management, prolonged operation times, and low detectionefficiency encountered in automatic detection systems. To overcome these issues, we propose a novel research and application method utilizing AI machine vision technology. The methodology employs the Pulse-Coupled Neural Network (PCNN) algorithm for analyzing machine control points, enhancing system reliability and detection efficiency. Furthermore, a three-stage sliding table mechanism is implemented to facilitate seamless operation restarts. Notably, our approach significantly reduces the time required for key operations, such as feeding, imaging, decision-making, on-site inspection, and re-inspection, all within 5 seconds and 1 meter distance. It supports high-precision dynamic identification, detection, and correction of errors during high-speed movement, thereby enhancing overall system performance. The experimental results demonstrate exceptional accuracy, particularly in detecting small parts measuring 28.87 mm in length and 12.36 mm in width, achieving an impressive precision of 0.04 mm. Additionally, our system boasts meticulous hardware selection, robust software stability, and high-performance capabilities, culminating in improved detection efficiency and accuracy. This research not only contributes novel ideas and results but also holds significant commercial value in industrial applications. Overall, our proposed methodology represents a noteworthy advancement in automatic inspection systems, offering superior performance and reliability compared to existing approaches.

Fusion of Infrared and Visible Light Images Based on Improved Adaptive Dual-Channel Pulse Coupled Neural Network

Fusion of Infrared and Visible Light Images Based on Improved Adaptive Dual-Channel Pulse Coupled Neural Network

A Comprehensive Study on Image Segmentation Using Pulse-Coupled Neural Networks

Pulse-Coupled Neural Network (PCNN) operates as a matrix of neurons, each uniquely corresponding to a pixel in the image being processed. Unlike traditional neural networks, PCNN does not require any training, making it highly suitable for image segmentation tasks. However, the complexity of PCNN arises from its numerous parameters, making parameter selection a challenging endeavor. In this work, we introduce a simplified PCNN architecture that includes an automatic parameter determination method. This approach is specifically designed for binary image segmentation and has been tested on various images, yielding results with distinct and desirable features. The proposed network iterates only four times, enhancing its efficiency. During pre-processing, RGB images are converted to HSV color space, and the V component undergoes further processing. This component is first filtered using an averaging filter, followed by a sharpening filter. The parameters for the PCNN are then generated automatically, eliminating the need for manual selection and making the network highly suitable for real-time image processing. The performance of the proposed network has been verified through tests on a variety of images. Key Words: Neural Networks, Pulse Coupled Neural Networks, Image Processing

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Traffic congestion is prevalent in many major and medium-sized cities throughout different countries in contemporary society. In traffic images, various multi-sized vehicles are tightly clustered together and obstructed from one another. Identifying vehicles in such instances is crucial for urban traffic surveillance, safety monitoring, and legal concerns but it also presents major challenges. The remarkable detection accuracy and efficiency of deep learning-based systems have led to their recent and extensive use in vehicle identification. There are significant advanced YOLO models with different backbone architectures and frameworks developed for vehicle detection. Yet, the performance of YOLO variants are facing the challenges of handling false detection against occluded and densely sophisticated scenarios. The proposed model is developed to address such types of limitations, for example; dynamic illumination, noisy images, and scale sensitivity to improve the vehicle detection rate in different traffic scenarios and varying weather conditions. The proposed study employs an improved YOLOv4 to identify moving vehicles in different lighting conditions including daylight, cloudy, rainy, and night. For hybridization, three techniques are utilized such as the Multiscale Retinex, Dual tree complex wavelet transform (DTCWT), and Pulse Coupled Neural Networks (PCNN). The DTCWT is employed for multiscale decomposition and to denoise the complex high frequency subband information, then the denoised subbands are reconstructed into a denoised image. The Multiscale retinex is utilized to reduce the halo artifacts on high-contrast edges and maintain the balance with dynamic range compression and color reproduction. The synchronizing pulse burst property of PCNN is used to detect the isolated noisy pixels and modify the detected noisy pixels. From the results it is worth noting that the developed model surpasses state-of-the-art methods in sunny, night, cloudy, and rainy modes. The proposed method using the DTCWT technique can detect the vehicles with mAP of 91.09% and 35FPS.

A painting authentication method based on multi-scale spatial-spectral feature fusion and convolutional neural network

The scientific authentication of paintings holds significant importance within the realm of art collection. Employing convolutional neural networks for the classification of authentic and counterfeit painting images based on color images is a viable but suboptimal choice. This study investigates the potential for authenticating paintings by incorporating high-spectral images alongside high-resolution spatial images. High-resolution and high-spectral images of genuine and counterfeit paintings were acquired using a push-broom digital scanning system. The processing methods presented in this approach for the acquired images are: 1) The study utilized the circular local binary pattern (LBP) and principal component analysis (PCA) to extract surface texture and spectral data from Chinese character images in paintings, encompassing both spatial and spectral dimensions. 2) The technique utilizing non-subsampling Shearlet transform (NSST) and pulse-coupled neural network (PCNN) was employed to integrate the spatial and spectral characteristics of the images into a pseudo-color image, producing a dataset of feature data for genuine and counterfeit paintings. 3) The experiments aimed to achieve the authenticity of artworks using EfficientNet v2-s, the hyperparameters of which were fine-tuned accordingly. The experimental findings demonstrate that this approach attained a 90.8 % accuracy on the test dataset, representing a 3.5 % enhancement over the existing top-performing three-dimensional convolutional neural network (3D-CNN).

A Deep Learning-Based Fault Diagnosis Approach for Power System Equipment via Infrared Image Sensing

Automatic fault diagnosis for power system equipment has always been an essential concern in this industry. Conventionally, such works are conducted by manual patrol inspection, which consumes much human labor and expert knowledge. Fortunately, infrared images can present diagnosis areas inside the equipment via the thermal sensing function. In such context, this work utilizes deep neural network to construct a specific infrared image processing framework that can realize automatic fault diagnosis. Thus in this paper, a deep learning-based fault diagnosis approach for power system equipment via infrared image sensing is proposed. First, a pulse-coupled neural network structure is employed to enhance feature representation for infrared images of the equipment. Next, a fuzzy [Formula: see text]-means (FCM)-based segmentation method is developed to filter diagnosis areas from the infrared images. Finally, a convolution operation-based fault diagnosis operator is adopted to identify the diagnosis types. After that, some simulation experiments are conducted on real-world infrared images on the power system equipment, in order to make the performance evaluation of the proposed approach. The proposal realizes the end-to-end process of feature extraction and fault detection and identification, and avoids the problems of single feature. It is due to manual extraction of fault features, and the inability to detect and identify faults effectively in specific situations and scenarios.

Image Fusion Method Based on Snake Visual Imaging Mechanism and PCNN.

The process of image fusion is the process of enriching an image and improving the image's quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today's deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes.

Multi-modal medical image fusion using improved dual-channel PCNN.

This paper proposes a medical image fusion method in the non-subsampled shearlet transform (NSST) domain to combine a gray-scale image with the respective pseudo-color image obtained through different imaging modalities. The proposed method applies a novel improved dual-channel pulse-coupled neural network (IDPCNN) model to fuse the high-pass sub-images, whereas the Prewitt operator is combined with maximum regional energy (MRE) to construct the fused low-pass sub-image. First, the gray-scale image and luminance of the pseudo-color image are decomposed using NSST to find the respective sub-images. Second, the low-pass sub-images are fused by the Prewitt operator and MRE-based rule. Third, the proposed IDPCNN is utilized to get the fused high-pass sub-images from the respective high-pass sub-images. Fourth, the luminance of the fused image is obtained by applying inverse NSST on the fused sub-images, which is combined with the chrominance components of the pseudo-color image to construct the fused image. A total of 28 diverse medical image pairs, 11 existing methods, and nine objective metrics are used in the experiment. Qualitative and quantitative fusion results show that the proposed method is competitive with and even outpaces some of the existing medical fusion approaches. It is also shown that the proposed method efficiently combines two gray-scale images.

Brain Tumor Detection Using Neural Classification in Machine Learning

Brain cancer classification is a difficult task due to the variety and complexity of tumors shown in magnetic resonance imaging (MRI) pictures. This research presents two neural network approaches for categorizing MRI brain images. The proposed neural network method consists of three steps: feature extraction, dimensionality reduction, and classification. First, we extracted features from MRI images using discrete wavelet transformation (DWT). In this second stage, we reduce the salient features of MRIs using principal component analysis (PCA). For the classification step, two supervised machine learning classifiers have been developed. Artificial neural networks are used by both classifiers; however, the second one employs back propagation (BPN) while the first one uses feed-forward (FF-ANN). Using the classifiers, MRI brain images of the subjects were classified as normal or abnormal. Artificial neural networks have numerous applications, including function approximation, feature extraction, optimization, and classification (ANNs). They are specifically intended to enhance photos, distinguish and categorize items, separate and register objects, and extract features. Among these, object and picture recognition is the most important for complex processing tasks such as classifying brain tumors. Radial basis function (RBF), cellular, multi-layer perceptron (MLP), hop field, and pulse-coupled neural networks have all been used in image segmentation. These networks can be categorized as feed-forward (associative) or feedback (auto-associative).

Underwater Terrain Matching Method Based on Pulse-Coupled Neural Network for Unmanned Underwater Vehicles

The positioning results of terrain matching in flat terrain areas will significantly deteriorate due to the influence of terrain nonlinearity and multibeam measurement noise. To tackle this problem, this study presents the Pulse-Coupled Neural Network (PCNN), which has been effectively utilized for image denoising. The interconnection of surface terrain data nodes is achieved through PCNN ignition, which serves to alleviate the reduction in terrain similarity caused by measurement error. This enables the efficient selection of terrain data, ensuring that points with high measurement accuracy are preserved for terrain matching and positioning operations. The simulation results illustrate that the suggested methodology effectively removes terrain data points with low measurement accuracy, thereby improving the performance of terrain matching and positioning.

Pulse-coupled neural network based on an adaptive Gabor filter for pavement crack segmentation

This article proposes a Pulse-Coupled Neural Network based on an adaptive Gabor filter for the segmentation of cracks in the pavement in digital images. By estimating the noise in the image, the parameters of the filter that convolves the neurons of the model are adjusted. As a result iterations were reduced to 2%with ? 90% precision. The algorithm was parallelized on the GPU and the processing time was reduced to n/NM regardless of the M and N dimensions of theimage.

Quantitative analysis of the effect of illumination variations on image processing in machine vision inspection

Abstract When unavoidable light, age, expression, and gesture changes occur, the machine vision detection accuracy will be greatly reduced, especially the light change impact on image processing. Combining the reflection equation and frequency domain analysis to construct a light change model based on spherical harmonic function and process the image from four directions, namely, digital image conversion, median filtering, sharpening processing, and image global segmentation. Aiming at the shortcomings of traditional image processing algorithms, the light compensation based on the improved pulse-coupled neural network model is proposed, and the model is used to analyze the impact of light changes on image processing. Despite the changes in light conditions, this algorithm maintains above 0.95 in each subset and has the best overall performance, as shown by the results. When the light intensity is less than 41.88kLux, using the optimal threshold for background segmentation of adult green peppers with respect to a fixed threshold T=0.3 results in a significant reduction of the background segmentation error. When the light intensity is greater than 41.88kLux, the superiority of the optimal threshold is not obvious. The results of this research not only have a significant role in promoting the development of the field of computer vision but also can provide people with convenient and efficient services in all aspects of social life.

PCNN orchard heterologous image fusion with semantic segmentation of significance regions

The apple fruits captured from red green and blue (RGB) color camera cannot be accurately identified and localized under natural scenes of orchards due to lighting and shading. We propose a pulse coupled neural network (PCNN) orchard heterogeneous image fusion model for semantic segmentation of saliency regions. The purpose of the model is to improve the accuracy of image segmentation and the quality of heterogeneous source image fusion in complex orchard scenes. The model fuses the time of flight (ToF) and RGB images acquired from orchard scenes. This model consists of an orchard image semantic segmentation module, a parameter optimization module, and a dual PCNN image fusion module. Image semantic segmentation module extracts the significant regions of fruit targets in orchard images. And then, in parameter optimization module, we introduce tent chaos and ranking strategies into human mental search algorithm to optimize the parameters of PCNN model. Finally, we complete the fusion through a dual PCNN module. Six fusion algorithms were used to evaluate the fusion quality of public and self-built datasets. The results showed that the chaotic hierarchical mental search algorithm could better solve the problem of difficult-to-determine PCNN parameters. The improved DeeplabV3 + could also segment clearer and more complete apple regions. Compared with other six fusion algorithms, the fusion experimental results show that the fused images with clearer targets and more prominent textures could be obtained in the proposed model. The proposed model reduces the influence of light and other disturbing factors in fruit recognition. And the fused image contains rich and accurate image information, which is more in line with the perception habit of human eyes.

Fuzzy Genetic Particle Swarm Optimization Convolution Neural Network Based On Oral Cancer Identification System

Oral cancer is the eighth most common type of cancer in the world. Every year, 130,000 people in India die from mouth cancer. Getting a diagnosis from a clinical exam by skilled doctors and a biopsy takes time. When a problem is found early, it is always easier to treat. The primary goal of this work is to recognise disease-affected oral regions in a given oral image and classify the oral cancer disorder. This study employs unique Deep Learning algorithms to detect the location of disease-affected oral areas. This work employs the most effective feature extraction techniques, including appearance and patter-based features. Following feature extraction, the Bee Pulse Couple Neural Network (BeePCNN) algorithm is used to choose the best feature. Finally, Deep Learning is used to classify these attributes. An innovative FGPSOCNN reduces the computational complexity of CNN. On an additional real-time data set from Arthi Scan Hospital, a secondary evaluation is conducted. The experimental results indicate that the innovative FGPSOCNN performs better than existing methods.

An image fusion-based method for recovering the 3D shape of roll surface defects

Most of the existing studies on roll surface defects focus on qualitative detection and lack quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are the stylus method, laser scanning method, and structured light scanning method, but these methods are difficult to accurately measure the complex defect variations on the roll surface. In this paper, we propose a method for recovering the 3D shape of roll surface defects based on image fusion. The traditional 3D reconstruction problem is transformed into a 2D image fusion problem using a focusing method. The non-subsampled shear wave transform is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called modified multi-state pulse-coupled neural network to obtain a fully focused image. The method achieves 3D shape recovery of defects by modeling the relationship between the defect depth, the fully focused image, and the original image. To evaluate the performance of the method, experiments were carried out using data involving craters and scratches on the roll surface. This method significantly improves the quality of defect detection images, with a 98% better gradient and a 28% increase in overall image quality. Additionally, it keeps 3D reconstruction errors under 4%, ensuring high accuracy and noise resistance.

A theoretical analysis of continuous firing condition for pulse-coupled neural networks with its applications

The pulse-coupled neural network (PCNN) has become a popular biology-inspired model because of its remarkable performance, such as image fusion, segmentation, and recognition. Although the PCNN is an unsupervised neural network model, its parameters are often manually adjusted, which leads to the shortcomings of being time-consuming, verbose, and with bad consistency. Researchers have conducted work on the PCNN's neurodynamic analysis and parameter settings. However, most of these works were proposed for specific application areas, and there are no widely recognized and accepted methods or theories for PCNN parameter setting at present. Thus, how to reduce the difficulty of parameter setting based on neurodynamic analysis is a very important research area for applying PCNN. In this work, we proposed a general formulae of a continuous firing condition based on the dynamic analysis of PCNN neurons to avoid constructing an invalid model and to learn the neurons' firing characteristics. The results obtained from the proposed formulae were used to explore the usability of the PCNN parameter setting theory. We first utilized the pre-existing theory of the PCNN firing period to examine the rationality of our theory, and then we also introduce an image fusion method using our theory and whale optimization algorithm to verify our method and theory, besides, numerical tests and experiments on common images were also performed to verify our theory. Experimental results show that our method and theory is effective.