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A software Requirements Specification Framework for Objective pattern recognition: A Set-Theoretic Classification Approach

A Software Requirements Specification Framework for Objectiv...

引用

IEEE international conference on Engineering of Complex Computer Systems

作者： Arbi Ghazarian Mehdi Sagheb Tehrani Arin Ghazarian Department of Engineering Computing Studies Arizona State University Mesa AZ USA College of Business Department of CIS/MIS Bemidji State University Bemidji MN USA Computer Engineering and Information Technology Department Amirkabir University of Technology슠 Tehran Iran

The motivation behind pattern-oriented software development is to decompose complex problems into recognizable sub-problems with predefined solutions, hence promoting both the quality of the resulting software product and the efficiency of the development process through the reuse of optimal solutions (e.g., best practices). Unfortunately, due to the opportunistic (i.e., non-systematic and subjective) element inherent in the process of recognizing conventional patterns in a problem context, the full potential of a pattern-driven problem-solving strategy has not been yet realized. In this paper, we introduce a requirements specification framework, called Problem Decomposition Scheme (PDS), which serves as a foundation to address the challenges of pattern recognition in the context of software requirements. We establish that pattern recognition can be abstracted as a set-theoretic classification problem, and formalize the properties of the resulting classification scheme. In contrast to ill-defined sub-problems in conventional development problem contexts, patterns in PDS-based specifications can be objectively recognized. To evaluate our requirements specification framework, we conducted an empirical user study of software developers performing a requirements pattern recognition task on a PDS-based requirements specification. PDS-based specifications are shown to achieve a high degree of requirements pattern recognition consistency across developers.

关键词： software systems Context Business pattern matching Programming

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soft computing Based Iris recognition Using Contourlet

Soft Computing Based Iris Recognition Using Contourlet

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2011 3rd IEEE international conference on Information Management and Engineering(ICIME 2011)

作者： C.Murukesh A.Karpagameena K.Thanushkodi R.Ganesan Department of Electronics and Instrumentation Velammal Engineering College Embedded Systems Technologies TIFAC-CORE in Pervasive Computing TechnologiesVelammal Engineering College Akshaya college of Engineering Coimbatore TIFAC-CORE in Pervasive Computing Technologies Velammal Engineering College

ISBN: (纸本)9781612842387

Identification of individual is becoming a serious issue now *** systems play a vital role in auto identification of individual based on the unique feature or characteristic possessed by the *** is unique for every person so it can be consider as a most reliable and accurate system of *** serious issue in iris recognition process is the choosing of transform for feature extraction and the selection of subset in the feature space for *** this paper the method of contourlet transform for feature extraction and graylevel cooccurrence matrix(GLCM)technique were used to select feature vector subset for *** transform have the excellent property of capturing the intrinsic geometrical structure of the image which is used for feature extraction of the iris *** multidirectional filter bank decomposes the iris image into different subband levels and provides best resolution of iris in various orientations along with the reduced feature vector dimension due to the characteristic of multiresolution *** feature vector subset selections were done efficiently using GLCM *** classification was also done efficiently with the subset vector of feature obtained while in GLCM *** obtain the accurate classification and result in less processing time with increase performance Support Vector Machine(SVM)is *** Vector Machine(SVM)classifier is used among various soft computing technique for classification of *** provide unique and global solution by whistling Artificial Neural Network(ANN) without depending on the dimensionality of the *** proposed innovative method is efficient as well as reliable for personal identification which yields less computational running time both in the training and recognition *** type of recognition can be used for authentication purpose and security related areas such in airport,banking,and secret *** recognition is

关键词： Iris recognition Biometric Segmentation Localization Normalization Enhancement Contourlet Filter banks GLCM Support Vector Machine.

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Linear Discriminant Analysis and Its Application in Plant Classification

Linear Discriminant Analysis and Its Application in Plant Cl...

引用

international conference on Information and computing Science, ICIC

作者： Minggang Du Xianfeng Wang School of Urban and Environment Science Shanxi Normal University Linfen Shanxi China Xijing University Xi'an China

The most common organisms on Earth are plants, which are crucial to the maintenance of atmospheric composition, nutrient cycling and other ecosystem processes. The classification of plants is vital to the study of plant genetic diversity and ecological sensitivity, which is also helpful for many problems such as feeding the population and fighting disease. In this paper, we firstly discuss the shortages of LDA, then classify the plant by using PCA+LDA. The experiments show that it is effective and feasible for plant classification.

关键词： Feature extraction Classification algorithms Shape Algorithm design and analysis pattern recognition Personal digital assistants Support vector machines

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Training Data Selection for Support Vector Machines Model

Training Data Selection for Support Vector Machines Model

引用

international conference on Information and Electronics Engineering（ICIEE 2011）

作者： Dang Huu Nghi Luong Chi Mai Hanoi University of Mining and Geology Tu LiemHa Noi. Vietnamse Academy of Science and Technology Institute of Information Technology18 Hoang Quoc Viet RoadHa Noi.

ISBN: (纸本)9789810886370

In recent years,Support Vector Machines(SVM) have become a popular tool for pattern recognition and machine *** training a support vector machine we need to select the parameters of the kernel function as well as soft margin parameter C of *** to develop the optimal classifier we need to determine the optimal kernel parameter and the optimal value of *** the optimal classifier is called model *** applied to a large data set,however,it requires a long time for training so the model selection task and its performance can be degraded a long *** speed up training thereby shortening the time for model selection,several methods have been proposed,one of which is to reduce the training set size called training data ***,there has been considerable research on data selection for SVM *** main idea is to select only the patterns that are likely to be located near the decision *** this paper we propose a methods that select a subset of data for SVM *** experimental results show that a significant amount of training data can be removed by our proposed method without degrading the performance of the resulting SVM classifiers.

关键词： Support vector machines,model selection,training data selection.

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A Novel Depth Map Generation Method Based on K-Means Clustering and Depth pattern recognition

A Novel Depth Map Generation Method Based on K-Means Cluster...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Hao Jiang Shuxu Guo Siming Meng Xiaonan Luo College of Electronic Science & Engineering Jilin University Changchun China Institute of Information Science and Technology Sun Yat-sen University Guangzhou China

In this paper, we propose a novel depth map generation method. After a series of pre-treatment process, image quality capture and bilateral filtering, K-means clustering method has been used for classification of background and front objects. Then the depth map could be generated directly depend on the predeterminate model which is given a forehand, finally the correct depth map can be vividly created base on the layer Stratifying. The experiment result shows that the depth map directly represent the depth information and also earn good subjective evaluation.

关键词： Three dimensional displays Streaming media Information filters Image edge detection Rendering (computer graphics)

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Requirements for a behaviour pattern based assistant for early detection and management of neurodegenerative diseases

Requirements for a behaviour pattern based assistant for ear...

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international conference on Pervasive computing Technologies for Healthcare, PervasiveHealth

作者： Andreas Hochgatterer Lukas Roedl Alberto Martínez Igone Etxeberria Erkuden Aldaz Bernhard Wöckl Jürgen Bund Health & Environment Department AIT Austrian Institute of Technology GmbH Wiener Neustadt Austria Health & Quality of Life TECNALIA Research and Innovation Zamudio Spain INGEMA Instituto Gerontolózico Matia San Sebastián Spain CURE Center for Usability Research & Enzineering Vienna Austria Meticube-Software Coimbra Portugal

This paper describes the various aspects of the BEDMOND AAL Joint Programme project, which aims at the development of an ICT-based system for the early detection of Alzheimer's disease and other neurodegenerative diseases, with focus on the identified applied reasoning layers. First the requirements of such a behaviour pattern based assistant are discussed, then the used system architecture is introduced and finally the applied reasoning layers for the situation recognition, interpretation and data representation layers are also detailed.

关键词： Senior citizens Intelligent sensors Dementia Cognition

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soft subspace clustering with competitive agglomeration

Soft subspace clustering with competitive agglomeration

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE)

作者： Lin Zhu Longbing Cao Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Faculty of Engineering and IT QCIS Sydney University of Technology Sydney Australia

In this paper, two novel soft subspace clustering algorithms, namely fuzzy weighting subspace clustering with competitive agglomeration (FWSCA) and entropy weighting subspace clustering with competitive agglomeration (EWSCA), are proposed to overcome the problems of the unknown number of clusters and the initialization of prototypes for soft subspace clustering. The main advantage of FWSCA and EWSCA lies in the fact that they effectively integrate the merits of soft subspace clustering and the good properties of fuzzy clustering with competitive agglomeration. This makes it possible to obtain the appropriate number of clusters during the clustering progress. Moreover, FWSCA and EWSCA algorithms can converge regardless of the initial number of clusters and initialization. Substantial experimental results on both synthetic and real data sets demonstrate the effectiveness of FWSCA and EWSCA in addressing the two problems.

关键词： Clustering algorithms Entropy Partitioning algorithms Equations Prototypes Electronic mail Indexes

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Fingerprints as Spatial Graphs: Nodes and Edges

Fingerprints as Spatial Graphs: Nodes and Edges

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： K. J. Horadam S. A. Davis A. Arakala J. Jeffers School of Mathematical and Geospatial Sciences RMIT University Melbourne Australia

Point-pattern matching of minutiae is the most common method used in fingerprint biometrics, but it is generally insufficient by itself. It has particular limitations in matching partial prints or in secure (biocryptographic) matching. Here, we add structure with a new spatial graph represention of a fingerprint, with minutiae as nodes. Using a sample of fingerprint graphs extracted from the FVC2002 database, we show that matching fingerprints using only the edges of the graphs performs almost as well as using only the nodes. Combinations of edges and nodes have superior performance to either individual score.

关键词： Image edge detection Feature extraction Databases Manuals pattern matching Fingerprint recognition Bifurcation

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A new algorithm for shape matching and pattern recognition using dynamic programming

A new algorithm for shape matching and pattern recognition u...

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IEEE international conference on Multimedia computing and Systems (ICMCS)

作者： Noreddine Gherabi Abdellatif Gherabi Mohamed Bahaj Department of Mathematics and Computer Science LABO LITEN University Hassan 1er Settat Morocco Department of Physics Laval University Canada Department of Mathematics and Computer Science Labo Liten University Hassan 1er Settat Morocco

We propose a new method for shape recognition and retrieval based on dynamic programming. Our approach uses the dynamic programming algorithm to compute the optimal score and to find the optimal alignment between two strings. First, each contour of shape is represented by a set of points. After alignment and matching between two shapes, the contours are transformed into a string of symbols and numbers. Finally we find the best alignment of two complete strings and compute the optimal cost of similarity. In general, dynamic programming has two phases - the forward phase and the backward phase. In the forward phase, we compute the optimal cost for each subproblem. In the backward phase, we reconstruct the solution that gives the optimal cost. Our algorithm is tested in a database that contains various shapes such as MPEG-7.

关键词： Shape Dynamic programming Context Heuristic algorithms Databases Histograms Gravity

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Palmprint verification using competitive index with PCA

Palmprint verification using competitive index with PCA

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international conference on Signal Processing, Communication, computing and Networking Technologies

作者： Deepti Tamrakar Pritee Khanna PDPM Indian Institute of Information Technology Design and Manufacture Jabalpur India

Among the various palmprint recognition techniques, competitive coding technique is very effective and gives efficient results. In the competitive coding method, gabor filters of six different orientation convolve with the palmprint image to extract the orientation information from the image. The winner-take-all strategy is used to select one orientation for each pixel, while each competitve index is coded into 3 bits, but in this study competitive index is not coded into bits. An approximation band of discrete wavelet transform of the image is used to compress the image, which has important information and makes the system illumination and translation invariant. Competitve index scheme is applied in the approximate band. Principal component analysis (PCA) is used to reduce the size of the feature vector and project the features in eigen space. Two different palmprints are matched using Euclidean distance metrics. This gives the equal error rate of .015% for palmprint verification.

关键词： Indexes Feature extraction pattern recognition Principal component analysis Discrete wavelet transforms Covariance matrix

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