41493711
Car Plate Localization Using Modified PCNN in Complicated Environment
Car plate Localization, which remains a difficult problem under complicated environment, is the key problem in many traffic related applications. In this paper we describe a new method based on modified Pulse Coupled Neural Network (PCNN) with adaptive threshold, which can capture relatively complete objects in human perception. After inverse filtering, PCNN processing is applied to produce a firing time sequence image. Then car plates' position and rotated angle can be extracted from the firing image. Experiment results show that the correct car plate locating rate reaches 98%, 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.
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.
12523556
Design Patterns of Scalable Cluster System Software
The design pattern of cluster system software has an important influence on scalability of massive cluster system. The paper presents design patterns of scalable cluster system software, including scalable software topologies and optimized communication modes. These design patterns have been widely applied in Dawning series of supercomputers and some results of performance evaluation show their good scalability
32712174
Hailstone Detection Based on Image Mining
Hailstone is one of the main meteorological disasters and it is very difficult to be forecasted effectively. A novel method of automatic recognition hail cloud is proposed base on association rules in this paper. Firstly, the method preprocesses the original reflectivity image. Then the color features and texture features are extracted from the radar echo image. Lastly, the association rules, which are used to distinguish hailstorm from rainstorm and super refract, are constructed, namely hail echo ensemble forecast model based on image mining is constructed. The results show that hail echo ensemble forecast model based on image mining has higher accuracy compared with the PUP products.
12907377
Body Detection and Tracking with Hierarchical Scheme in Dynamic Scenes
In this paper, we propose an approach for body detection and tracking with a hierarchical and salient-half-body-prior scheme and apply it in real dynamic scenes. The human region is firstly located by an SVM classifier with histogram of oriented gradients (HOG). Two image observations, motion foreground and edge distance map, are then applied for body detection. In detail, the torso part is first roughly initialized, and then parts are localized in a salient-prior half-body sampling scheme by maximizing body configuration probability with both parts likelihood and body geometry constraints. For body tracking, the color-texture appearance templates of parts extracted in detection phase are further used in tracking process. The approach has been tested on soccer and skating videos and got good results.
6828357
Study on Traffic Information Fusion Algorithm Based on Support Vector Machines
Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on SVM about freeway incident detection was proposed. A SVM was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. Compared with the multi-layer feed forward neural network (MLF) algorithm trained with the same data, the simulation results showed that the SVM offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance
11099012
A Pattern Matching Algorithm in P2P Mobile QA System Answer Extracting Process
Mobile automatic question and answer (QA) system is more and more popular as a mobile value-added business. But the current mobile QA systems always have low query precision and recall ratio. In this paper, a new algorithm by pattern matching in p2p mobile QA system answer extracting process is designed. It expresses the document as triad and extracts the question and answer sentence from the triad based documents. Simulation experimental analysis results show that under ideal conditions the systempsilas average responding time is less than 58 ms and precision is above 66%, and the recall ratio is upper than 52% when there are no more than 3000 simultaneous query nodes, and so the completeness and feasibility of the systempsilas design is proved.