Image Segmentation using Two-Layer Pulse Coupled Neural Network with Inhibitory Linking Field

For over a decade, the Pulse Coupled Neural Network(PCNN) based algorithms have been used for imagesegmentation. Though there are several versions of the PCNNbased image segmentation methods, almost all of them use singlelayerPCNN with excitatory linking inputs. There are fourmajor issues associated with the single-burst PCNN which needattention. Often, the PCNN parameters including the linkingcoefficient are determined by trial and error. The segmentationaccuracy of the single-layer PCNN is highly sensitive to the valueof the linking coefficient. Finally, in the single-burst mode,neurons corresponding to background pixels do not participatein the segmentation process. This paper presents a new 2-layernetwork organization of PCNN in which excitatory andinhibitory linking inputs exist. The value of the linkingcoefficient and the threshold signal at which primary firing ofneurons start are determined directly from the image statistics.Simulation results show that the new PCNN achieves significantimprovement in the segmentation accuracy over the widelyknown Kuntimad’s single burst image segmentation approach.The two-layer PCNN based image segmentation methodovercomes all three drawbacks of the single-layer PCNN

Application of Antagonism Neural Network in Data Processing of Graph Calculation

With the development of science and technology, especially information technology, image processing technology has become an indispensable and powerful tool in scientific research. Traditional image processing methods cannot meet the needs. Researchers began to explore new and more effective methods. Among them, neural network image processing is the most active direction. Compared with traditional algorithms, neural network algorithm has the advantages of strong parallel computing ability, strong nonlinear imaging ability and strong adaptability. With the in-depth study of neural network theory, people have fully realized the advantages of neural network technology in parallel computing ability, nonlinear mapping and adaptability. Different neural network models are widely used in the field of image processing. Neural network technology has also become a hot research topic

License Plate Recognition Technology Development Research and Improvement

License plate recognition technology is an important part of an intelligent transport system, widely used in highway tolls, unregistered vehicle monitoring, vehicle parking management, and other important occasions. Typical of the license plate recognition, algorithm is divided into three components, license plate localization, character segmentation, and character recognition. This paper summarizes the key technology of license plate recognition algorithm, and analyses the difficulties of improving the recognition rate. According to features of license plates, license plate character recognition methods in recent years were summarized and put forward, on the basis of the existing methods, improving system performance and accuracy

Segmentation and classification of leukocytes using neural networks: a generalization direction

In image digital processing, as in other fields, it is commonly difficult to simultaneously achieve a generalizing system and a specialistic system. The segmentation and classification of leukocytes is an application where this fact is evident. First an exclusively supervised approach to segmentation and classification of blood white cells images is shown. As this method produces some drawbacks related to the specialistic/generalized problems, another process formed by two neural networks is proposed. One is an unsupervised network and the other one is a supervised neural network. The goal is to achieve a better generalizing system while still doing well the role of a specialistic system. We will compare the performance of the two approaches

NOVA INFORMACIJSKA TEHNOLOGIJA PROCJENE KORISTI IZDVAJANJA CESTA POMOĆU SATELITSKIH SNIMKI VISOKE REZOLUCIJE TEMELJENE NA PCNN I C-V MODELU

Road extraction from high resolution satellite images has been an important research topic for analysis of urban areas. In this paper road extraction based on PCNN and Chan-Vese active contour model are compared. It is difficult and computationally expensive to extract roads from the original image due to presences of other road-like features with straight edges. The image is pre-processed using median filter to reduce the noise. Then road extraction is performed using PCNN and Chan-Vese active contour model. Nonlinear segments are removed using morphological operations. Finally the accuracy for the road extracted images is evaluated based on quality measures.Izdvajanje cesta pomoću satelitskih slika visoke rezolucije je važna istraživačka tema za analizu urbanih područja. U ovom radu ekstrakcije ceste se uspoređuju na PCNN i Chan-Vese aktivnom modelu. Teško je i računalno skupo izdvojiti ceste iz originalne slike zbog prisutnosti drugih elemenata ravnih rubova sličnih cestama. Slika je prethodno obrađena korištenjem filtera za smanjenje smetnji. Zatim se ekstrakcija ceste izvodi pomoću PCNN i Chan-Vese aktivnog modela konture. Nelinearni segmenti su uklonjeni primjenom morfoloških operacija. Konačno, točnost za ceste izdvojene iz slika se ocjenjuje na temelju kvalitativnih mjera

Integrated 2-D Optical Flow Sensor

I present a new focal-plane analog VLSI sensor that estimates optical flow in two visual dimensions. The chip significantly improves previous approaches both with respect to the applied model of optical flow estimation as well as the actual hardware implementation. Its distributed computational architecture consists of an array of locally connected motion units that collectively solve for the unique optimal optical flow estimate. The novel gradient-based motion model assumes visual motion to be translational, smooth and biased. The model guarantees that the estimation problem is computationally well-posed regardless of the visual input. Model parameters can be globally adjusted, leading to a rich output behavior. Varying the smoothness strength, for example, can provide a continuous spectrum of motion estimates, ranging from normal to global optical flow. Unlike approaches that rely on the explicit matching of brightness edges in space or time, the applied gradient-based model assures spatiotemporal continuity on visual information. The non-linear coupling of the individual motion units improves the resulting optical flow estimate because it reduces spatial smoothing across large velocity differences. Extended measurements of a 30x30 array prototype sensor under real-world conditions demonstrate the validity of the model and the robustness and functionality of the implementation

Intelligent Foreign Particle Inspection Machine for Injection Liquid Examination Based on Modified Pulse-Coupled Neural Networks

A biologically inspired spiking neural network model, called pulse-coupled neural networks (PCNN), has been applied in an automatic inspection machine to detect visible foreign particles intermingled in glucose or sodium chloride injection liquids. Proper mechanisms and improved spin/stop techniques are proposed to avoid the appearance of air bubbles, which increases the algorithms' complexity. Modified PCNN is adopted to segment the difference images, judging the existence of foreign particles according to the continuity and smoothness properties of their moving traces. Preliminarily experimental results indicate that the inspection machine can detect the visible foreign particles effectively and the detection speed, accuracy and correct detection rate also satisfying the needs of medicine preparation