Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234056
S. Tominaga, Sachiko Okamoto
This paper describes a method for classifying object materials on a raw circuit board based on surface-spectral reflectance. First we introduce a multi-spectral imaging system for observing tiny objects and capturing their spectral data. The imaging system is composed of a liquid-crystal tunable filter, a monochrome CCD camera, macro-lens and a personal computer. We describe how we can estimate the spectral reflectance functions of object surfaces by using the multi-spectral imaging system. We show that dielectric materials like plastics can be distinguished from metals based on the reflectance difference in changing illumination geometries. Then an algorithm is presented for classifying the objects into several circuit elements based on the estimated spectral-reflectances. Region segmentation results of the circuit board are demonstrated in an experiment using a real board. The performance of the proposed imaging system and algorithms is examined in comparison with the RGB-based methods using a normal color camera.
{"title":"Reflectance-based material classification for printed circuit boards","authors":"S. Tominaga, Sachiko Okamoto","doi":"10.1109/ICIAP.2003.1234056","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234056","url":null,"abstract":"This paper describes a method for classifying object materials on a raw circuit board based on surface-spectral reflectance. First we introduce a multi-spectral imaging system for observing tiny objects and capturing their spectral data. The imaging system is composed of a liquid-crystal tunable filter, a monochrome CCD camera, macro-lens and a personal computer. We describe how we can estimate the spectral reflectance functions of object surfaces by using the multi-spectral imaging system. We show that dielectric materials like plastics can be distinguished from metals based on the reflectance difference in changing illumination geometries. Then an algorithm is presented for classifying the objects into several circuit elements based on the estimated spectral-reflectances. Region segmentation results of the circuit board are demonstrated in an experiment using a real board. The performance of the proposed imaging system and algorithms is examined in comparison with the RGB-based methods using a normal color camera.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116443222","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}
Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234068
H. Muhammed
A new class of neuro fuzzy systems, based on so-called weighted neural networks (WNN), is introduced and used for unsupervised fuzzy clustering and image segmentation. Incremental and fixed (or grid-partitioned) weighted neural networks are presented and used for this purpose. The WNN algorithm (incremental or grid-partitioned) produces a net, of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in the input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of resulting clusters is determined by this procedure. Experiments confirm the usefulness and efficiency of the proposed neuro fuzzy systems for image segmentation and, in general, for clustering multi- and high-dimensional data.
{"title":"Unsupervised fuzzy clustering and image segmentation using weighted neural networks","authors":"H. Muhammed","doi":"10.1109/ICIAP.2003.1234068","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234068","url":null,"abstract":"A new class of neuro fuzzy systems, based on so-called weighted neural networks (WNN), is introduced and used for unsupervised fuzzy clustering and image segmentation. Incremental and fixed (or grid-partitioned) weighted neural networks are presented and used for this purpose. The WNN algorithm (incremental or grid-partitioned) produces a net, of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in the input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of resulting clusters is determined by this procedure. Experiments confirm the usefulness and efficiency of the proposed neuro fuzzy systems for image segmentation and, in general, for clustering multi- and high-dimensional data.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124369469","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}
Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234106
A. Torii, Y. Wakazono, H. Murakami, A. Imiya
We propose a superresolution process for gray-level images based on a resolution-conversion method for discrete terrain in a space. With our resolution-conversion method, sampling a terrain and expressing it as a discrete surface allows us to estimate an original surface from a low-resolution one applying the resolution-conversion method.
{"title":"Resolution conversion of gray-level images by discrete geometry","authors":"A. Torii, Y. Wakazono, H. Murakami, A. Imiya","doi":"10.1109/ICIAP.2003.1234106","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234106","url":null,"abstract":"We propose a superresolution process for gray-level images based on a resolution-conversion method for discrete terrain in a space. With our resolution-conversion method, sampling a terrain and expressing it as a discrete surface allows us to estimate an original surface from a low-resolution one applying the resolution-conversion method.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116011530","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}
Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234113
G. Fumera, I. Pillai, F. Roli
The aim of this paper is to evaluate the potential usefulness of the reject option for text categorisation (TC) tasks. The reject option is a technique used in statistical pattern recognition for improving classification reliability. Our work is motivated by the fact that, although the reject option proved to be useful in several pattern recognition problems, it has not yet been considered for TC tasks. Since TC tasks differ from usual pattern recognition problems in the performance measures used and in the fact that documents can belong to more than one category, we developed a specific rejection technique for TC problems. The performance improvement achievable by using the reject option was experimentally evaluated on the Reuters dataset, which is a standard benchmark for TC systems.
{"title":"Classification with reject option in text categorisation systems","authors":"G. Fumera, I. Pillai, F. Roli","doi":"10.1109/ICIAP.2003.1234113","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234113","url":null,"abstract":"The aim of this paper is to evaluate the potential usefulness of the reject option for text categorisation (TC) tasks. The reject option is a technique used in statistical pattern recognition for improving classification reliability. Our work is motivated by the fact that, although the reject option proved to be useful in several pattern recognition problems, it has not yet been considered for TC tasks. Since TC tasks differ from usual pattern recognition problems in the performance measures used and in the fact that documents can belong to more than one category, we developed a specific rejection technique for TC problems. The performance improvement achievable by using the reject option was experimentally evaluated on the Reuters dataset, which is a standard benchmark for TC systems.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114747265","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}
Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234119
G. Garibotto, M. Corvi, Carlo Cibei, Sara Sciarrino
The proposed system is aimed at detecting and classifying 3D moving objects for security control of unmanned automatic railway stations. Most common approaches are based on active sensors like optical barriers or laser scanning devices. The proposed approach, named 3DMODS, is based on stereo vision technology, using a prediction-verification paradigm. Adaptive change detection is performed at the video rate to detect immediately moving objects in the scene. Object features are collected by "scanning" the scene with different parallel planes at variable height, to verify the volumetric consistency of the detected object. A prediction of stereo correspondence is performed, using homographic transformation on the set of predefined 3D planes, to verify whether the detected change is really a moving 3D object with a significant size, or just a phantom caused by shadows or highlights. A simple classification scheme is currently implemented to decide for an alarm candidate in case of relevant object size, but much more complex and flexible solutions are possible, to recognize all the relevant objects in the scene and achieve much more robust alarm detection performance.
{"title":"3DMODS: 3D moving obstacle detection system","authors":"G. Garibotto, M. Corvi, Carlo Cibei, Sara Sciarrino","doi":"10.1109/ICIAP.2003.1234119","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234119","url":null,"abstract":"The proposed system is aimed at detecting and classifying 3D moving objects for security control of unmanned automatic railway stations. Most common approaches are based on active sensors like optical barriers or laser scanning devices. The proposed approach, named 3DMODS, is based on stereo vision technology, using a prediction-verification paradigm. Adaptive change detection is performed at the video rate to detect immediately moving objects in the scene. Object features are collected by \"scanning\" the scene with different parallel planes at variable height, to verify the volumetric consistency of the detected object. A prediction of stereo correspondence is performed, using homographic transformation on the set of predefined 3D planes, to verify whether the detected change is really a moving 3D object with a significant size, or just a phantom caused by shadows or highlights. A simple classification scheme is currently implemented to decide for an alarm candidate in case of relevant object size, but much more complex and flexible solutions are possible, to recognize all the relevant objects in the scene and achieve much more robust alarm detection performance.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117336565","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}
Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234062
L. Costa, D. Schubert
This article describes an image analysis framework for the characterization and analysis of cell trajectories considering the following three biologically relevant parameters: (a) the behavior of each individual cell; (b) interactions between each pair of cells; and (c) interactions between each cell and its environment. The potential of the overall framework is illustrated with respect to real cell displacement data.
{"title":"A framework for cell movement image analysis","authors":"L. Costa, D. Schubert","doi":"10.1109/ICIAP.2003.1234062","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234062","url":null,"abstract":"This article describes an image analysis framework for the characterization and analysis of cell trajectories considering the following three biologically relevant parameters: (a) the behavior of each individual cell; (b) interactions between each pair of cells; and (c) interactions between each cell and its environment. The potential of the overall framework is illustrated with respect to real cell displacement data.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121866491","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}
Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234129
W. Clocksin
We present a segmentation method that works for overlapping and closely packed nuclei in noisy images that have high variation in background intensity. The method has been tested on fluorescence in-situ hybridisation images of interphase leucocyte nuclei. Accurate segmentation is required in support of an automatic procedure for assaying telomere content on a per area per nucleus basis. The method first finds a single seed point for each nucleus that uniquely identifies that nucleus. Seed points are located by an efficient iterative mode-finding algorithm based on robust nonparametric density estimation. Acting simultaneously on all nuclei in the image, and using the seed points as origins, flexible closed contours are dilated until each nucleus is circumscribed. Unlike previous approaches, the contour equations include a repulsive term that prevents different contours from intersecting, thereby preserving the identity of nearby or overlapping nuclei, and the contour is adaptively remeshed for greater efficiency The locations of the seed points are not critical in providing an accurate segmentation. The advantage of this method from an implementation point of view is that the computation of seed points and contours is highly efficient and robust compared with alternative approaches. The method is illustrated using data from a clinical pilot study.
{"title":"Automatic segmentation of overlapping nuclei with high background variation using robust estimation and flexible contour models","authors":"W. Clocksin","doi":"10.1109/ICIAP.2003.1234129","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234129","url":null,"abstract":"We present a segmentation method that works for overlapping and closely packed nuclei in noisy images that have high variation in background intensity. The method has been tested on fluorescence in-situ hybridisation images of interphase leucocyte nuclei. Accurate segmentation is required in support of an automatic procedure for assaying telomere content on a per area per nucleus basis. The method first finds a single seed point for each nucleus that uniquely identifies that nucleus. Seed points are located by an efficient iterative mode-finding algorithm based on robust nonparametric density estimation. Acting simultaneously on all nuclei in the image, and using the seed points as origins, flexible closed contours are dilated until each nucleus is circumscribed. Unlike previous approaches, the contour equations include a repulsive term that prevents different contours from intersecting, thereby preserving the identity of nearby or overlapping nuclei, and the contour is adaptively remeshed for greater efficiency The locations of the seed points are not critical in providing an accurate segmentation. The advantage of this method from an implementation point of view is that the computation of seed points and contours is highly efficient and robust compared with alternative approaches. The method is illustrated using data from a clinical pilot study.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125868787","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}
Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234100
V. Gesù, F. Isgrò, D. Tegolo, E. Trucco
The paper introduces a software system for detecting and tracking starfish in an underwater video sequence. The target of such a system is to help biologists in giving an estimate of the number of starfish present in a particular area of the sea-bottom. The nature of the input images is characterised by a low signal/noise ratio and by the presence of noisy background represented by pebbles; this makes the detection a non-trivial task. The procedure we use is a chain of several steps that starts from the extraction of the area of interest and ends with a classifier and a tracker providing the necessary information for counting the starfish present in the scene.
{"title":"Finding essential features for tracking starfish in a video sequence","authors":"V. Gesù, F. Isgrò, D. Tegolo, E. Trucco","doi":"10.1109/ICIAP.2003.1234100","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234100","url":null,"abstract":"The paper introduces a software system for detecting and tracking starfish in an underwater video sequence. The target of such a system is to help biologists in giving an estimate of the number of starfish present in a particular area of the sea-bottom. The nature of the input images is characterised by a low signal/noise ratio and by the presence of noisy background represented by pebbles; this makes the detection a non-trivial task. The procedure we use is a chain of several steps that starts from the extraction of the area of interest and ends with a classifier and a tracker providing the necessary information for counting the starfish present in the scene.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116781840","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}
Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234116
H. Legal-Ayala, J. Facon
We present a new segmentation approach by thresholding based on learning strategy. This strategy is based only on one image and its ideal thresholded version. A decision matrix is generated from each pixel and each gray level. At the moment of new image segmentation, the best solution for each pixel is evaluated by means of K nearest neighbors in the decision matrix. Comparative tests were performed on signature, fingerprint and magnetic resonance images.
{"title":"Segmentation approach by learning: different image applications","authors":"H. Legal-Ayala, J. Facon","doi":"10.1109/ICIAP.2003.1234116","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234116","url":null,"abstract":"We present a new segmentation approach by thresholding based on learning strategy. This strategy is based only on one image and its ideal thresholded version. A decision matrix is generated from each pixel and each gray level. At the moment of new image segmentation, the best solution for each pixel is evaluated by means of K nearest neighbors in the decision matrix. Comparative tests were performed on signature, fingerprint and magnetic resonance images.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126249950","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}
Pub Date : 2003-09-17DOI: 10.1109/ICIAP.2003.1234032
Lijun Jiang, Shiqian Wu, Dajun Wu, H. Eng
3D shape modeling using color light projection is a new and promising technique. It is suitable for fast 3D modeling of moving objects, like human heads. In this paper, we demonstrated a new method for 3D head modeling by incorporating a priori knowledge of human face and unequal phase, stepping to alleviate the color coupling errors raised in the color projection scheme. Unlike prior art, the proposed method utilizes the wrapped phase difference between face and reference directly so that the processing is one-course, and time is shortened. An algorithm corresponding to a specific paradigm with R-G-B phase step set to 0-45-180 degree is given. Experimental results demonstrate the effectiveness of the method.
{"title":"Head modeling using color unequal phase stepping method","authors":"Lijun Jiang, Shiqian Wu, Dajun Wu, H. Eng","doi":"10.1109/ICIAP.2003.1234032","DOIUrl":"https://doi.org/10.1109/ICIAP.2003.1234032","url":null,"abstract":"3D shape modeling using color light projection is a new and promising technique. It is suitable for fast 3D modeling of moving objects, like human heads. In this paper, we demonstrated a new method for 3D head modeling by incorporating a priori knowledge of human face and unequal phase, stepping to alleviate the color coupling errors raised in the color projection scheme. Unlike prior art, the proposed method utilizes the wrapped phase difference between face and reference directly so that the processing is one-course, and time is shortened. An algorithm corresponding to a specific paradigm with R-G-B phase step set to 0-45-180 degree is given. Experimental results demonstrate the effectiveness of the method.","PeriodicalId":218076,"journal":{"name":"12th International Conference on Image Analysis and Processing, 2003.Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128299155","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}
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