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共 257 条记录，以下是161-170 订阅

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Detection and recognition of Human in Videos Using Adaptive Method and Neural Net

Detection and Recognition of Human in Videos Using Adaptive ...

引用

international conference of soft computing and pattern recognition (socpar)

作者： Syed Sohaib Ali M.F. Zafar Moeen Tayyab Department of EE International Islamic University Islamabad Pakistan International Islamic University Islamabad PK

Detection and recognition of the moving objects in dynamic environment is difficult task. This paper presents a modified framework for the detection and recognition of moving people in videos. Detection part of the proposed method consists of average background model with supportive secondary model and an adaptive threshold selection model based on Gaussian distribution. The background model used for background modelling and adaptive threshold method is used to simultaneously update the system according to environment. Then feature extraction is performed by an established human model. This human model consists of five parts with robust features to facilitate recognition process. For recognition purpose, back propagation neural network has been used as a classifier. Experimental results show the effectiveness of proposed system.

关键词： Humans Videos Neural networks Design optimization Printing Integer linear programming Constraint optimization Testing Containers pattern recognition

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Development of Kernel Fisher Discriminant Model Using the Cross-Entropy Method

Development of Kernel Fisher Discriminant Model Using the Cr...

引用

international conference of soft computing and pattern recognition (socpar)

作者： Budi Santosa Andiek Sunarto Department of Industrial Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

In this paper, the cross-entropy (CE) method is proposed to solve non-linear discriminant analysis or kernel Fisher discriminant (CE-KFD) analysis. CE through certain steps can find the optimal or near optimal solution with a fast rate of convergence for optimization problem. While, KFD is to solve problem of Fisher's linear discriminant in a kernel feature space F by maximizing between class variance and minimizing within class variance. Through the numerical experiments, we found that CE-KFD demonstrates the high accuracy of the results compared to the traditional methods, Fisher LDA and kernel Fisher (KFD) with eigen decomposition method.

关键词： Kernel Shape Curve fitting Phase detection Image segmentation Iterative algorithms pattern recognition Information science Data mining Computer graphics

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Exploiting Routing Information Encoded into Backlinks to Improve Topical Crawling

Exploiting Routing Information Encoded into Backlinks to Imp...

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international conference of soft computing and pattern recognition (socpar)

作者： Alban Mouton Pierre-Francois Marteau Valoria European University of Brittany Vannes France

Local link analysis of topical graphs on the Web allows to experiment focused crawling strategies in a detailed way. In this scope, models, parameters and metrics used to orientate the crawler can be better understood, tuned and evaluated. We develop a methodological and experimental approach that exploits link analysis in order to determine what constitutes a good content analysis metric able to guide efficiently topical crawlers toward highly relevant areas of the Web. Our experimentations show that partial knowledge of the local topology of topical graph highlights our understanding of routing capabilities of various metrics. Furthermore, our experimentations demonstrate that significant crawling efficiency improvement can be reached.

关键词： Routing Design optimization Printing Integer linear programming Constraint optimization Testing Containers pattern recognition Computer applications Laboratories

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Automated Grading of Palm Oil Fresh Fruit Bunches (FFB) Using Neuro-fuzzy Technique

Automated Grading of Palm Oil Fresh Fruit Bunches (FFB) Usin...

引用

international conference of soft computing and pattern recognition (socpar)

作者： Nursuriati Jamil Azlinah Mohamed Syazwani Abdullah Faculty of Computer & Mathematical Sciences Universiti Teknologi MARA Shah Alam Selangor Darul Ehsan Malaysia

Automated fruit grading in local fruit industries are gradually receiving attention as the use of technology in upgrading the quality of food products are now acknowledged. In this paper, outer surface colors of palm oil fresh fruit bunches (FFB) are analyzed to automatically grade the fruits into over ripe, ripe and unripe. We compared two methods of color grading: (1) using RGB digital numbers and (2) colors classifications trained using a supervised learning Hebb technique and graded using fuzzy logic. A total of 90 images are used as the training images and 45 images are tested in the grading process. Overall, automated grading using RGB digital numbers produced an average of 49% success rate, while the neuro-fuzzy approach achieved an accuracy level of 73.3%.

关键词： Petroleum Design optimization Printing Integer linear programming Constraint optimization Testing Containers pattern recognition Computer applications Laboratories

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A New Clustering Method Based on Weighted Kernel K-Means for Non-linear Data

A New Clustering Method Based on Weighted Kernel K-Means for...

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international conference of soft computing and pattern recognition (socpar)

作者： Abdolreza Rasouli Mohd Aizaini Bin Maarof Mahboubeh Shamsi Faculty of Computer Science and Information System Universiti Teknologi Malaysia Skudai Malaysia Faculty of Computer Science and Information System Islamic Azad University Bardsir Iran

Clustering is the process of gathering objects into groups based on their feature's similarity. In this paper, we concentrate on Weighted Kernel K-Means method for its capability to manage nonlinear separability and high dimensionality in the data. A new slight modification of WKM algorithm has been proposed and tested on real Rice data. The results show that the accuracy of proposed algorithm is higher than other famous clustering algorithm and ensures that the WKM is a good solution for real world problems.

关键词： Clustering methods Kernel Clustering algorithms Machine learning algorithms Data mining Atmospheric modeling Vegetation mapping pattern recognition Computer science Management information systems

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Accelerating Multi-scale Image Fusion Algorithms Using CUDA

Accelerating Multi-scale Image Fusion Algorithms Using CUDA

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international conference of soft computing and pattern recognition (socpar)

作者： Seung-Hun Yoo Jin-Hyung Park Chang-Sung Jeong Department Electronics Engineering Korea University Seoul South Korea

Recently, fusion speed has emerged as an important factor in the image fusion and a substantial amount of memory and computing power are required for a high-speed fusion. This paper shows approaches to accelerate multi-scale image fusion speed on GPU (graphics processing unit) using CUDA (compute unified device architecture). The GPU has evolved into a very powerful and flexible streaming processor, which provides a good computational power and memory bandwidth. We implement the multi-scale image fusion algorithms using CUDA software platform of the latest version of GPU and theirs fusion speeds are compared and evaluated with implementation in Core2 Quad processor with 2.4 GHz. The GPU version achieved a speedup of 6x-8x over the CPU version.

关键词： Acceleration Image fusion Design optimization Printing Integer linear programming Constraint optimization Testing Containers pattern recognition Computer applications

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Correlation Research of Association Rules and Application in the Data about Coronary Heart Disease

Correlation Research of Association Rules and Application in...

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international conference of soft computing and pattern recognition (socpar)

作者： Zheng-kui Lin Wei-guo Yi Ming-yu Lu Zhi Liu Hao Xu Information Science and Technology Dalian Maritime University Dalian China Traditional and Western Medicine National Cardiovascular Center China-Japan Friendship Hospital Beijing China

The mining association rule is an important research field in data mining. The mining association rule usually adopts this model: support, confidence, interestingness. But this model can't measure the correlative degree between the antecedent and the consequent of the rule by ration. So we proposed a new mining model of association rules: support, coincidence, interestingness and analyzed the meaning of coincidence by instance. At last, we used this model in the data about coronary heart disease and obtained a lot of meaningful rules.

关键词： Association rules Cardiac disease Data mining Itemsets pattern recognition Hospitals Cardiology Information science Transaction databases Mathematics

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Automatic Multilevel Thresholding Using Binary Particle Swarm Optimization for Image Segmentation

Automatic Multilevel Thresholding Using Binary Particle Swar...

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international conference of soft computing and pattern recognition (socpar)

作者： Leila Djerou Nacer Khelil Houssem Eddine Dehimi Mohamed Batouche Department Computer Science Med Khider University Biskra Algeria Department Mathematics Med Khider University Biskra Algeria Department Computer Science CCIS King Saud University Riyadh Saudi Arabia

In this paper an automatic multilevel thresholding approach, based on binary particle swarm optimization, is proposed. The proposed approach automatically determines the "optimum" number of the thresholds and simultaneously searches the optimal thresholds, by optimizing a function which uses the gray level thresholds as parameters. The algorithm starts with large number initial thresholds, then, these thresholds are dynamically refined to improve the value of the objective function. The proposed method is validated by illustrative examples; comparison with the exhaustive search Otsu's and Kapur's methods shows its efficiency.

关键词： Particle swarm optimization Image segmentation Computer science Pixel Iterative algorithms Ant colony optimization Entropy Genetic algorithms pattern recognition Mathematics

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Cat Swarm Optimization for Clustering

Cat Swarm Optimization for Clustering

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international conference of soft computing and pattern recognition (socpar)

作者： Budi Santosa Mirsa Kencana Ningrum Department of Industrial Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Cat swarm optimization (CSO) is one of the new heuristic optimization algorithm which based on swarm intelligence. Previous research shows that this algorithm has better performance compared to the other heuristic optimization algorithms: Particle swarm optimization (PSO) and weighted-PSO in the cases of function minimization. In this research a new CSO algorithm for clustering problem is proposed. The new CSO clustering algorithm was tested on four different datasets. The modification is made on the CSO formula to obtain better results. Then, the accuracy level of poposed algorith was compared to those of K-means and PSO clustering. The modification of CSO formula can improve the performance of CSO clustering. The comparison indicates that CSO clustering can be considered as a sufficiently accurate clustering method.

关键词： Particle swarm optimization Clustering algorithms Cats Ant colony optimization Industrial engineering Probability Heuristic algorithms Informatics Computational intelligence pattern recognition

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DTM - Extracting Data Records from Search Engine Results Page Using Tree Matching Algorithm

DTM - Extracting Data Records from Search Engine Results Pag...

引用

international conference of soft computing and pattern recognition (socpar)

作者： Jer Lang Hong Eugene Siew Simon Egerton Monash University Selangor Darul Ehsan Malaysia

In this paper, we develop a non-visual automatic wrapper for extracting data records from search engine results page. The novel techniques for our wrapper are (1) filtering rules to detect and filter out irrelevant data records, (2) a tree matching algorithm using frequency measures to increase the speed of data extraction (3) an algorithm to calculate the number and size of the components of data records to detect the correct data region. Results show that our wrapper is as robust and in many cases outperforms the state of the art wrappers such as ViNT and DEPTA. This wrapper could have significant speed advantages when processing large volumes of web sites data, which could be helpful in meta search engine development.

关键词： Data mining Search engines Tree data structures Matched filters Filtering algorithms Frequency measurement Metasearch pattern recognition pattern matching Velocity measurement

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