Pub Date : 2001-09-26DOI: 10.1109/ICIAP.2001.957077
G. Triantafyllidis, D. Tzovaras, M. Strintzis
A novel frequency domain technique for image blocking artifact detection is presented. The algorithm detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the DCT coefficients are modeled by a Laplacian probability function. Experimental results illustrating the performance of the proposed method are presented and evaluated.
{"title":"Detection of blocking artifacts of compressed still images","authors":"G. Triantafyllidis, D. Tzovaras, M. Strintzis","doi":"10.1109/ICIAP.2001.957077","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.957077","url":null,"abstract":"A novel frequency domain technique for image blocking artifact detection is presented. The algorithm detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the DCT coefficients are modeled by a Laplacian probability function. Experimental results illustrating the performance of the proposed method are presented and evaluated.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132102628","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 : 2001-09-26DOI: 10.1109/ICIAP.2001.957026
T. Lourens, HIroshi G. Okuno, H. Kitano
An approach to symbolic contour extraction is described that consists of three stages: enhancement, detection, and extraction of contours and corners. Contours and corners are enhanced by models of monkey cortical complex and endstopped cells. Detection of corners and local contour maxima is performed by selection of local maxima in both contour and corner enhanced images. These maxima form the anchor points of a greedy contour following algorithm that extracts the contours. This algorithm is based on the idea of spatially linking neurons along a contour that fire in synchrony to indicate an extracted contour. The extracted contours and detected corners represent the symbolic representation of the image. The advantage of the proposed model over other models is that the same low constant thresholds for corner and local contour maxima detection are used for different images. Closed contours are guaranteed by the contour following algorithm to yield a fully symbolic representation which is more suitable for reasoning and recognition. In this respect our methodology is unique, and clearly different from the standard (edge) contour detection methods. The results of the extracted contours (when displayed as being detected) show similar or better results compared to the SUSAN and Canny-CSS detectors.
{"title":"Automatic graph extraction from color images","authors":"T. Lourens, HIroshi G. Okuno, H. Kitano","doi":"10.1109/ICIAP.2001.957026","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.957026","url":null,"abstract":"An approach to symbolic contour extraction is described that consists of three stages: enhancement, detection, and extraction of contours and corners. Contours and corners are enhanced by models of monkey cortical complex and endstopped cells. Detection of corners and local contour maxima is performed by selection of local maxima in both contour and corner enhanced images. These maxima form the anchor points of a greedy contour following algorithm that extracts the contours. This algorithm is based on the idea of spatially linking neurons along a contour that fire in synchrony to indicate an extracted contour. The extracted contours and detected corners represent the symbolic representation of the image. The advantage of the proposed model over other models is that the same low constant thresholds for corner and local contour maxima detection are used for different images. Closed contours are guaranteed by the contour following algorithm to yield a fully symbolic representation which is more suitable for reasoning and recognition. In this respect our methodology is unique, and clearly different from the standard (edge) contour detection methods. The results of the extracted contours (when displayed as being detected) show similar or better results compared to the SUSAN and Canny-CSS detectors.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128021033","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 : 2001-09-26DOI: 10.1109/ICIAP.2001.956979
Mun-Ho Jeong, Y. Kuno, N. Shimada, Y. Shirai
We present a method to track and recognise shape-changing hand gestures simultaneously. The switching linear model using the active contour model corresponds well to temporal shapes and motions of hands. Inference in the switching linear model is computationally intractable and therefore the learning process cannot be performed via the exact EM (expectation maximization) algorithm. However, we present an approximate EM algorithm using a collapsing method in which some Gaussians are merged into a single Gaussian. Tracking is performed through the forward algorithm based on Kalman filtering and the collapsing method. We also present the regularized smoothing, which plays a role in reducing jump changes between the training sequences of state vectors to cope with complex-variable hand shapes. The recognition process is performed by the selection of a model with the maximum likelihood from some learned models while tracking is being performed. Experiments for several shape-changing hand gestures are demonstrated.
{"title":"Recognition of shape-changing hand gestures based on switching linear model","authors":"Mun-Ho Jeong, Y. Kuno, N. Shimada, Y. Shirai","doi":"10.1109/ICIAP.2001.956979","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.956979","url":null,"abstract":"We present a method to track and recognise shape-changing hand gestures simultaneously. The switching linear model using the active contour model corresponds well to temporal shapes and motions of hands. Inference in the switching linear model is computationally intractable and therefore the learning process cannot be performed via the exact EM (expectation maximization) algorithm. However, we present an approximate EM algorithm using a collapsing method in which some Gaussians are merged into a single Gaussian. Tracking is performed through the forward algorithm based on Kalman filtering and the collapsing method. We also present the regularized smoothing, which plays a role in reducing jump changes between the training sequences of state vectors to cope with complex-variable hand shapes. The recognition process is performed by the selection of a model with the maximum likelihood from some learned models while tracking is being performed. Experiments for several shape-changing hand gestures are demonstrated.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134444744","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 : 2001-09-26DOI: 10.1109/ICIAP.2001.956992
L. Cinque, G. Foresti, A. Gumina, S. Levialdi
This paper presents a clustering approach for image segmentation based on a modified fuzzy ART model. The goal of the proposed approach is to find a simple model able to instance a prototype for each cluster in order to avoid complex post-processing phases. Some results and comparisons with other models present in the literature, like SOM and original fuzzy ART are presented. Qualitative and quantitative evaluations confirm the validity of our approach.
{"title":"A modified fuzzy ART for image segmentation","authors":"L. Cinque, G. Foresti, A. Gumina, S. Levialdi","doi":"10.1109/ICIAP.2001.956992","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.956992","url":null,"abstract":"This paper presents a clustering approach for image segmentation based on a modified fuzzy ART model. The goal of the proposed approach is to find a simple model able to instance a prototype for each cluster in order to avoid complex post-processing phases. Some results and comparisons with other models present in the literature, like SOM and original fuzzy ART are presented. Qualitative and quantitative evaluations confirm the validity of our approach.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130597343","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 : 2001-09-26DOI: 10.1109/ICIAP.2001.957027
H. Muhammed, P. Ammenberg, E. Bengtsson
A pixel in a hyperspectral image can be considered as a mixture of the reflectance spectra of several substances. The mixture coefficients correspond to the (relative) amounts of these substances. The benefit of hyperspectral imagery is that many different substances can be characterised and recognised by their spectral signatures. Independent component analysis (ICA) can be used for the blind separation of mixed statistically independent signals. Principal component analysis (PCA) also gives interesting results. The next step is to interpret and use the ICA or PCA results efficiently. This can be achieved by using a new technique called feature-vector based analysis (FVBA), which produces a number of component-feature vector pairs. The obtained feature vectors and the corresponding components represent, in this case, the spectral signatures and the corresponding image weight coefficients (the relative concentration maps) of the different constituting substances.
{"title":"Using feature-vector based analysis, based on principal component analysis and independent component analysis, for analysing hyperspectral images","authors":"H. Muhammed, P. Ammenberg, E. Bengtsson","doi":"10.1109/ICIAP.2001.957027","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.957027","url":null,"abstract":"A pixel in a hyperspectral image can be considered as a mixture of the reflectance spectra of several substances. The mixture coefficients correspond to the (relative) amounts of these substances. The benefit of hyperspectral imagery is that many different substances can be characterised and recognised by their spectral signatures. Independent component analysis (ICA) can be used for the blind separation of mixed statistically independent signals. Principal component analysis (PCA) also gives interesting results. The next step is to interpret and use the ICA or PCA results efficiently. This can be achieved by using a new technique called feature-vector based analysis (FVBA), which produces a number of component-feature vector pairs. The obtained feature vectors and the corresponding components represent, in this case, the spectral signatures and the corresponding image weight coefficients (the relative concentration maps) of the different constituting substances.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115338645","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 : 2001-09-26DOI: 10.1109/ICIAP.2001.957044
M. Albanesi, S. Bandelli, Marco Ferretti
We propose a multiresolution indexing algorithm based on color histogram which exploits the wavelet decomposition and a customized quantization for content-based image retrieval. The aim is to extract automatically the chromatic content of the images and to represent it with simple, robust, efficient and low computational cost descriptors. The proposed method has been integrated for a complete CBIR system, where the classification of images is performed on a qualitative subjective color perception. The system allows testing the semantic and chromatic class homogeneity previously defined by a human observer. Experimental results have been evaluated by the quantitative assessment parameters (averaged precision and recall). Multiresolution proved to be a valid framework to introduce spatiality in color histogram indexing, to dramatically decrease the computational complexity and to validate the qualitative subjective classification.
{"title":"Quantitative assessment of qualitative color perception in image database retrieval","authors":"M. Albanesi, S. Bandelli, Marco Ferretti","doi":"10.1109/ICIAP.2001.957044","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.957044","url":null,"abstract":"We propose a multiresolution indexing algorithm based on color histogram which exploits the wavelet decomposition and a customized quantization for content-based image retrieval. The aim is to extract automatically the chromatic content of the images and to represent it with simple, robust, efficient and low computational cost descriptors. The proposed method has been integrated for a complete CBIR system, where the classification of images is performed on a qualitative subjective color perception. The system allows testing the semantic and chromatic class homogeneity previously defined by a human observer. Experimental results have been evaluated by the quantitative assessment parameters (averaged precision and recall). Multiresolution proved to be a valid framework to introduce spatiality in color histogram indexing, to dramatically decrease the computational complexity and to validate the qualitative subjective classification.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"79 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115658683","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 : 2001-09-26DOI: 10.1109/ICIAP.2001.957041
Y. Kompatsiaris, Evangelia Triantafyllou, M. Strintzis
The development of an intelligent image content-based search engine for the World-Wide Web is presented. This system will offer a new form of media representation and access of content available in the WWW. Information web crawlers continuously traverse the Internet and collect images that are subsequently indexed based on integrated feature vectors. As a basis for the indexing, the K-means algorithm is used, modified so as to take into account the coherence of the regions. Based on the extracted regions, characteristic features are extracted using color texture and shape/region boundary information. These features along with additional information such as the URL location and the date of index procedure are stored in a database. The user can access and search this indexed content through the Web with an advanced and user-friendly interface. The output of the system is a set of links to the content available in the WWW, ranked according to their similarity to the image submitted by the user. Experimental results demonstrate the performance of the system.
{"title":"A World Wide Web region-based image search engine","authors":"Y. Kompatsiaris, Evangelia Triantafyllou, M. Strintzis","doi":"10.1109/ICIAP.2001.957041","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.957041","url":null,"abstract":"The development of an intelligent image content-based search engine for the World-Wide Web is presented. This system will offer a new form of media representation and access of content available in the WWW. Information web crawlers continuously traverse the Internet and collect images that are subsequently indexed based on integrated feature vectors. As a basis for the indexing, the K-means algorithm is used, modified so as to take into account the coherence of the regions. Based on the extracted regions, characteristic features are extracted using color texture and shape/region boundary information. These features along with additional information such as the URL location and the date of index procedure are stored in a database. The user can access and search this indexed content through the Web with an advanced and user-friendly interface. The output of the system is a set of links to the content available in the WWW, ranked according to their similarity to the image submitted by the user. Experimental results demonstrate the performance of the system.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124696477","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 : 2001-09-26DOI: 10.1109/ICIAP.2001.956985
A. Rydberg, G. Borgefors
Over-segmentation is a common problem for all kinds of segmentation tasks. Automated segmentation of natural scenes is no exception. This paper proposes a solution to the over-segmentation problem, with the emphasis on satellite images of farmland. In many cases, an agricultural field can be considered as a flat region having a rather large area, a compact shape, and straight region boundaries because it is a man-made object. Our approach for dividing farmland into individual field units uses region shape, as well as spectral information, when merging over-segmented regions. The results from the presented method are compared to two different methods of segmentation as well as interpreted field boundaries. The results show that task-specific knowledge adds important information to the decision step for the merging procedure of regions. About 70% of the edges are classified within one pixel away from the ground truth edges using our methods.
{"title":"Feature based merging of application specific regions","authors":"A. Rydberg, G. Borgefors","doi":"10.1109/ICIAP.2001.956985","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.956985","url":null,"abstract":"Over-segmentation is a common problem for all kinds of segmentation tasks. Automated segmentation of natural scenes is no exception. This paper proposes a solution to the over-segmentation problem, with the emphasis on satellite images of farmland. In many cases, an agricultural field can be considered as a flat region having a rather large area, a compact shape, and straight region boundaries because it is a man-made object. Our approach for dividing farmland into individual field units uses region shape, as well as spectral information, when merging over-segmented regions. The results from the presented method are compared to two different methods of segmentation as well as interpreted field boundaries. The results show that task-specific knowledge adds important information to the decision step for the merging procedure of regions. About 70% of the edges are classified within one pixel away from the ground truth edges using our methods.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124435777","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 : 2001-09-26DOI: 10.1109/ICIAP.2001.957057
F. Oberti, G. Ferrari, C. Regazzoni
A general architecture for distributed third-generation surveillance systems is discussed. In particular an approach for selecting the optimal distribution of intelligence (task allocation) is presented. The introduction of recognition tasks which can cause the interruption of the processing and transmission flow is discussed. Experimental results over a simulated system illustrate the presented approach for optimal distribution of intelligence.
{"title":"Recognition driven burst transmissions in distributed third generation surveillance systems","authors":"F. Oberti, G. Ferrari, C. Regazzoni","doi":"10.1109/ICIAP.2001.957057","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.957057","url":null,"abstract":"A general architecture for distributed third-generation surveillance systems is discussed. In particular an approach for selecting the optimal distribution of intelligence (task allocation) is presented. The introduction of recognition tasks which can cause the interruption of the processing and transmission flow is discussed. Experimental results over a simulated system illustrate the presented approach for optimal distribution of intelligence.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126006710","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 : 2001-09-26DOI: 10.1109/ICIAP.2001.957013
Riccardo Distasi, M. Nappi, M. Tucci, S. Vitulano
Many intrinsically 2-dimensional visual signals can be effectively encoded in a 1D form. This simpler representation is well-suited to both pattern recognition and image retrieval tasks. In particular, this paper deals with contour and texture, combined together in order to obtain an effective technique for content-based image indexing. The proposed method, named CONTEXT, represents CONtours and TEXTures by a vector containing the location and energy of the signal maxima. Such a representation has been utilized as the feature extraction engine in an image retrieval system for image databases. The homogeneous treatment reserved to both contour and texture information makes the algorithm elegant and easy to implement and extend. The data used for experimentally assessing CONTEXT were contours and textures from various application domains, plus a database of medical images. The experiments reveal a high discriminating power which in turn yields a high perceived quality of the retrieval results.
{"title":"CONTEXT: a technique for image retrieval integrating CONtour and TEXTure information","authors":"Riccardo Distasi, M. Nappi, M. Tucci, S. Vitulano","doi":"10.1109/ICIAP.2001.957013","DOIUrl":"https://doi.org/10.1109/ICIAP.2001.957013","url":null,"abstract":"Many intrinsically 2-dimensional visual signals can be effectively encoded in a 1D form. This simpler representation is well-suited to both pattern recognition and image retrieval tasks. In particular, this paper deals with contour and texture, combined together in order to obtain an effective technique for content-based image indexing. The proposed method, named CONTEXT, represents CONtours and TEXTures by a vector containing the location and energy of the signal maxima. Such a representation has been utilized as the feature extraction engine in an image retrieval system for image databases. The homogeneous treatment reserved to both contour and texture information makes the algorithm elegant and easy to implement and extend. The data used for experimentally assessing CONTEXT were contours and textures from various application domains, plus a database of medical images. The experiments reveal a high discriminating power which in turn yields a high perceived quality of the retrieval results.","PeriodicalId":365627,"journal":{"name":"Proceedings 11th International Conference on Image Analysis and Processing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127831643","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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