26132055
Car Plate Localization Using Pulse Coupled Neural Network in Complicated Environment
Car plate recognition is an important problem in many traffic related applications. In this paper, we focus on car plate localization-the first step of car plate recognition. We propose a hybrid method based on Pulse Coupled Neural Network (PCNN) and wavelet analysis. First of all, we use PCNN to enhance the image. Then, regions of interest (ROIs) will be get through wavelet analysis. After that, PCNN enhancement is applied again in ROIs, followed by a training and classification process for final labelling ROIs as car plate regions or not. Experiment results show that the precision can get 96%, which is higher than other localization methods on the same image database.
23743946
Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model
Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.
41889531
Research on Pairwise Key Establishment Model and Algorithm for Sensor Networks
Security schemes of pairwise key establishment, which enable sensors to communicate with each other securely, play a fundamental role in research on security issue in wireless sensor networks. A new kind of pairwise key pre-distribution model for sensor networks is proposed. And in addition, based on which, an efficient dynamic key path establishment algorithm is designed. Theoretic analysis and experimental figures show that the new algorithm has better performance than those previous related works.
2370296
Regularized Discriminative Direction for Shape Difference Analysis
The "discriminative direction" has been proven useful to reveal the subtle difference between two anatomical shape classes. When a shape moves along this direction, its deformation will best manifest the class difference detected by a kernel classifier. However, we observe that such a direction cannot maintain a shape's "anatomical" correctness, introducing spurious difference. To overcome this drawback, we develop a regularized discriminative direction by requiring a shape to conform to its population distribution when it deforms along the discriminative direction. Instead of iterative optimization, an analytic solution is provided to directly work out this direction. Experimental study shows its superior performance in detecting and localizing the difference of hippocampal shapes for sex. The result is supported by other independent research in the same domain.
31999867
A Novel Classifier with the Immune-Training Based Wavelet Neural Network
After analyzing the classification and training ability of a wavelet neural network (WNN), a novel WNN learning scheme integrating immunity based evolutionary algorithm (IDEA) is proposed, in which, IDEA is an evolutionary algorithm with an embedded immune mechanism. When WNN is used as a classifier, the process of seeking the least mean-square error (LMS) of an optimal problem is equivalent to that of finding the wavelet feature with maximal separability, namely, maximizing its separable division. On the other hand, with the capability of robust learning of its evolutionary process, IDEA is able to eliminate local degenerative phenomenon due to blindfold behaviors of original operators in the existing evolutionary algorithms. In the case of the twin-spiral problem, experimental simulation shows the feasibility of WNN training with the IDEA based learning algorithm.
5903999
Texture classification using multiresolution Markov random field models
Abstract Texture classification is an important topic in texture analysis. Texture classification has wide applications in remote sensing, computer vision, and image analysis. During the past years, several authors discussed to use multiresolution stochastic approaches to model textures. However, in these approaches, the highpass components which contain rich detailed information are lost. In this paper, we propose multiresolution MRF (MRMRF) modeling to describe textures. MRMRF modeling is a method trying to fuse filtering theory and MRF models. In the MRMRF modeling, highpass components are considered as well as lowpass components. “Brodatz texture database” is used in this paper for the experiments and Nearest Linear Combination (NLC) is used as measurement of distance to improve the recognition rate. The experimental results show that NLC has much better performance than Nearest Neighbor (NN) as the measurement in MRMRF modeling.
195719977
Improved Hierarchical Network Model Based on Pastry Scheme
The traditional Pastry design is a flat overlay network and limited-scope searching, in which all nodes have identical capabilities and responsibilities and all communication is symmetric. But in practice, because of a variety of available bandwidth and delay, each node has different capabilities. in this paper, a hierarchical Pastry network model is presented to address this limitation. Furthermore, the routing procedure and handling algorithm of the arrival and departure of nodes will be carefully discussed. Finally, some elementary experimental results obtained with a prototype implementation on JXTA platform indicate that the improved Pastry network model is superior in routing performance.