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Geometric Modeling and Imaging--New Trends (GMAI'06)最新文献

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Fuzzy Neural Networks and Genetic Algorithms for Medical Images Interpretation 模糊神经网络和遗传算法在医学图像解释中的应用
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.20
Nacéra Benamrane, A. Aribi, L. Kraoula
{"title":"Fuzzy Neural Networks and Genetic Algorithms for Medical Images Interpretation","authors":"Nacéra Benamrane, A. Aribi, L. Kraoula","doi":"10.1109/GMAI.2006.20","DOIUrl":"https://doi.org/10.1109/GMAI.2006.20","url":null,"abstract":"In this paper, we propose an approach for detection and specification of anomalies present in medical images. The idea is to combine three metaphors: neural networks, fuzzy logic and genetic algorithms in a hybrid system. The neural networks and fuzzy logic metaphors are coupled in one system called fuzzy neural networks. The genetic algorithm adds to this hybridizing the property of total research like an initialization of the fuzzy neural networks training algorithm witch is based on an adapted version of the back propagation algorithm. After applying the growing region algorithm to extract regions, the fuzzy neural network detect the suspect regions, which are interpreted by the fuzzy neural network of specification. Some of experimental results on brain images show the feasibility of the proposed approach","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128697651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 31
Clustering Approach to Content Based Image Retrieval 基于内容的图像检索聚类方法
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.12
P. Dutta, D. Bhattacharyya, J. Kalita, M. Dutta
{"title":"Clustering Approach to Content Based Image Retrieval","authors":"P. Dutta, D. Bhattacharyya, J. Kalita, M. Dutta","doi":"10.1109/GMAI.2006.12","DOIUrl":"https://doi.org/10.1109/GMAI.2006.12","url":null,"abstract":"This paper presents an efficient spatial indexing technique based on Silhouette moments that makes the index robust subject to the three basic transformations for CBIR. Spatial index is generated based upon a fast and robust clustering technique, which can recognize color clusters of any shape. The new clustering technique has been found to be efficient in terms of time complexity and cluster quality than many of its counterparts. A matching engine has been devised to retrieve images from the image database, which has the capacity for global and regional similarity search","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115184117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Toolpath Pattern Comparison: Contour-Parallel with Direction-Parallel 刀具路径模式比较:轮廓平行与方向平行
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.45
T. El-Midany, A. Elkeran, H. Tawfik
{"title":"Toolpath Pattern Comparison: Contour-Parallel with Direction-Parallel","authors":"T. El-Midany, A. Elkeran, H. Tawfik","doi":"10.1109/GMAI.2006.45","DOIUrl":"https://doi.org/10.1109/GMAI.2006.45","url":null,"abstract":"During rough cut machining of sculptured parts, appropriate selection of a tool path pattern in each cutting plane can significantly improve productivity. In this paper, various feasible toolpath patterns are investigated. A feedrate machining time model that takes into account CNC machine acceleration and deceleration for automatically identifying the most productive toolpath pattern is developed. This model is then used to compare the total machining time for common five types of toolpath patterns. The compared toolpath patterns are normal zigzag, smooth zigzag, normal spiral, smooth spiral and fishtail spiral. The results show that the optimal toolpath pattern is dependent on part geometry, physical characteristic of used CNC machine tool (accelerator and decelerator, continuous path, look ahead, and etc.) and cutting conditions (tool diameter, feedrate, and etc.). The developed comparing method has been implemented in Visual C++. Many examples are used to illustrate the developed method","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128630847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 32
Recognition of Off-Line Handwritten Arabic Words Using Neural Network 基于神经网络的离线手写阿拉伯语单词识别
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.43
S. Al-Maadeed
{"title":"Recognition of Off-Line Handwritten Arabic Words Using Neural Network","authors":"S. Al-Maadeed","doi":"10.1109/GMAI.2006.43","DOIUrl":"https://doi.org/10.1109/GMAI.2006.43","url":null,"abstract":"Neural network (NN) has been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for unconstrained Arabic handwritten word recognition based on a neural network is proposed and discussed. The overall engine of this combination of a global feature scheme with a NN is a system able to classify Arabic-handwritten words of one hundred different writers. The system first attempts to remove some of the variation in the images that do not affect the identity of the handwritten word. Next, the system codes the skeleton and edge of the word so that feature information about the strokes in the skeleton is extracted. Then, a classification process based on the artificial NN classifier is used as global recognition engine, to classify the Arabic words. The output is a word in the dictionary. A detailed experiment is carried out, and successful recognition results are reported","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125992231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 39
Multi-Class SVMs Based on Fuzzy Integral Mixture for Handwritten Digit Recognition 基于模糊积分混合的多类支持向量机手写数字识别
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.37
H. Nemmour, Y. Chibani
{"title":"Multi-Class SVMs Based on Fuzzy Integral Mixture for Handwritten Digit Recognition","authors":"H. Nemmour, Y. Chibani","doi":"10.1109/GMAI.2006.37","DOIUrl":"https://doi.org/10.1109/GMAI.2006.37","url":null,"abstract":"The major drawback of support vector machines (SVMs) is that the training time grows fastly with respect to the number of training samples. This issue becomes more critical for multi-class problems where a set of binary SVMs must be performed. This is the case of the one-against-all (OAA) approach, which is the most widely used implementation of multi-class SVMs. In this paper, we propose a new divide-and-conquer method to reduce the training time of OAA-based SVMs. Experimental analysis is conducted on handwritten digit recognition task. The results obtained indicate that the proposed scheme allows a significant training and testing time improvement. In addition, a significant improvement in generalization performance was obtained","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130728581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
A Tabu Search Meta-Heuristic for Image Semi-Supervised Classification 基于禁忌搜索的图像半监督分类元启发式算法
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.4
M. Zennaki, A. Ech-Cherif, Jean-Charles Lamirel
{"title":"A Tabu Search Meta-Heuristic for Image Semi-Supervised Classification","authors":"M. Zennaki, A. Ech-Cherif, Jean-Charles Lamirel","doi":"10.1109/GMAI.2006.4","DOIUrl":"https://doi.org/10.1109/GMAI.2006.4","url":null,"abstract":"We investigate the utility of tabu search (TS) meta-heuristics for semi-supervised image classification tasks. The proposed heuristic solves the integer programming transductive support vector machine (MIP-TSVM) formulation considered. Preliminary results, with a linear kernel show that our TS implementation can effectively find optimal global solutions for TSVM with relatively large problem dimensions and is competitive, in terms of generalization performance, with transductive SVMlight package on LIBSVM benchmarks. However on corel image database, TSVMlight demonstrates superior performance. As a result, the usefulness of such MIP-TSVM formulation may be application dependant","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"439 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134178211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Arabic OCR Based on Image Invariants 基于图像不变量的阿拉伯语OCR
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.9
A. Al-Shoshan
{"title":"Arabic OCR Based on Image Invariants","authors":"A. Al-Shoshan","doi":"10.1109/GMAI.2006.9","DOIUrl":"https://doi.org/10.1109/GMAI.2006.9","url":null,"abstract":"Optical character recognition (OCR) is an area of interest for converting hard copies of written information, as in faxed or scanned documents. In this paper, we compare two common image invariant methods in OCR, the invariant central moments (NCM) and the normalized Fourier descriptors (NFD) of the character. These methods are insensitive to image variances such as shifting, scaling, or rotation. To ensure the efficiency of the proposed algorithms, some examples and simulation are done. We also show by means of examples that when using the NCM the recognition rate decreases when the image coordinates ratio is changed","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134315314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Knowledge Discovery of Traffic/People Behaviors Based on Image Mining Approach 基于图像挖掘方法的交通/人员行为知识发现
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.31
F. Safara, A. Eftekhari-Moghadam
{"title":"Knowledge Discovery of Traffic/People Behaviors Based on Image Mining Approach","authors":"F. Safara, A. Eftekhari-Moghadam","doi":"10.1109/GMAI.2006.31","DOIUrl":"https://doi.org/10.1109/GMAI.2006.31","url":null,"abstract":"The increasing number of image archives has made image mining an important task because of its potential to discover useful image patterns and relationships from a large set of images. We proposed a framework for extracting knowledge from a sequence of images. The structure of the framework composed of two modules: image analysis and knowledge processing. In this paper, we customized the knowledge-processing module for checking normality/abnormality of vehicles/people behaviors in each image sequence","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131071343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
An Efficient Feature Based Matching Algorithm for Stereo Images 一种基于特征的立体图像匹配算法
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.6
Bo Tang, D. Ait-Boudaoud, B. Matuszewski, L. Shark
{"title":"An Efficient Feature Based Matching Algorithm for Stereo Images","authors":"Bo Tang, D. Ait-Boudaoud, B. Matuszewski, L. Shark","doi":"10.1109/GMAI.2006.6","DOIUrl":"https://doi.org/10.1109/GMAI.2006.6","url":null,"abstract":"A novel efficient feature based stereo matching algorithm is presented in this paper. The proposed method links the detected feature points into chains and the matching process is achieved by comparing some of the feature points from different chains. A matching score based on 2 dimensional normalised cross correlation (2D NCC) is used to determine whether feature points are well matched to construct a feature correspondence. This process improves the reliability and the efficiency of the algorithm by concentrating on matching corresponding chains. The proposed method is tested and validated using real scenes and synthetic data images. Experimental results indicate that this novel algorithm is more reliable especially for images in which a number of vertical features are detected. It also compares well with existing methods in terms of speed of execution","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121475869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 32
DIJA PROJECT: Forging Preforms Design Using Trade Knowledge DIJA项目:使用行业知识设计锻造预锻件
Geometric Modeling and Imaging--New Trends (GMAI'06) Pub Date : 2006-07-05 DOI: 10.1109/GMAI.2006.17
E. Brosse, Y. Gardan, E. Perrin
{"title":"DIJA PROJECT: Forging Preforms Design Using Trade Knowledge","authors":"E. Brosse, Y. Gardan, E. Perrin","doi":"10.1109/GMAI.2006.17","DOIUrl":"https://doi.org/10.1109/GMAI.2006.17","url":null,"abstract":"In this paper, we present a new method dedicated to hot forging. We show how to supply automatically the three-dimensional preforms sequence from the rough product. Our method uses a methodology coming from French forging industries. In particular, it implements rules and know-how related to this trade. We present the main steps of this method which are relative to form feature extraction, to knowledge representation, and to geometrical concerns. But, in this study, we only focus on our bi-dimensional outlines representation and how hot forging know-how is using on it. An industrial case-study is developed that validates our concepts and this case-study has been approved by local blacksmiths","PeriodicalId":438098,"journal":{"name":"Geometric Modeling and Imaging--New Trends (GMAI'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116109164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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