Pub Date : 2002-12-10DOI: 10.1109/ICPR.2002.1048402
E. Polat, M. Yeasin, Rajeev Sharma
We present a multiple object tracking framework that employs two common methods for tracking and image matching, namely Multiple Hypothesis Tracking (MHT) and Hausdorff image matching. We use the MHT algorithm to track image edges simultaneously. This algorithm is capable of tracking multiple edges with limited occlusions and is suitable for resolving any data association uncertainty caused by background clutter and closely-spaced edges. We use the Hausdorff matching algorithm to organize individual edges into objects given their two-dimensional models. The combined technique provides a robust probabilistic tracking framework which is capable of tracking complex objects in cluttered background in video sequences.
{"title":"Multiple complex object tracking using a combined technique","authors":"E. Polat, M. Yeasin, Rajeev Sharma","doi":"10.1109/ICPR.2002.1048402","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048402","url":null,"abstract":"We present a multiple object tracking framework that employs two common methods for tracking and image matching, namely Multiple Hypothesis Tracking (MHT) and Hausdorff image matching. We use the MHT algorithm to track image edges simultaneously. This algorithm is capable of tracking multiple edges with limited occlusions and is suitable for resolving any data association uncertainty caused by background clutter and closely-spaced edges. We use the Hausdorff matching algorithm to organize individual edges into objects given their two-dimensional models. The combined technique provides a robust probabilistic tracking framework which is capable of tracking complex objects in cluttered background in video sequences.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128689290","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048243
Yanlai Li, Kuanquan Wang, David Zhang
This paper presents a very fast step acceleration based training algorithm (SATA) for multilayer feedforward neural network training. The most outstanding virtue of this algorithm is that it does not need to calculate the gradient of the target function. In each iteration step, the computation only concentrates on the corresponding varied part. The proposed algorithm has attributes in simplicity, flexibility and feasibility, as well as high speed of convergence. Compared with the other methods, including the conventional backpropagation (BP), conjugate gradient, and weight extrapolation based BP, many simulations confirmed the superiority of this algorithm in terms of converging speed and computation time required.
{"title":"Step acceleration based training algorithm for feedforward neural networks","authors":"Yanlai Li, Kuanquan Wang, David Zhang","doi":"10.1109/ICPR.2002.1048243","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048243","url":null,"abstract":"This paper presents a very fast step acceleration based training algorithm (SATA) for multilayer feedforward neural network training. The most outstanding virtue of this algorithm is that it does not need to calculate the gradient of the target function. In each iteration step, the computation only concentrates on the corresponding varied part. The proposed algorithm has attributes in simplicity, flexibility and feasibility, as well as high speed of convergence. Compared with the other methods, including the conventional backpropagation (BP), conjugate gradient, and weight extrapolation based BP, many simulations confirmed the superiority of this algorithm in terms of converging speed and computation time required.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127337852","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048230
A. Rukhin, P. Grother, P. Phillips, Stefan Leigh, A. Heckert, E. Newton
Nonparametric statistics for quantifying dependence between the output rankings of face recognition algorithms are described Analysis of the archived results of a large face recognition study shows that even the better algorithms exhibit significantly different behaviors. It is found that there is significant dependence in the rankings given by two algorithms to similar and dissimilar faces but that other samples are ranked independently. A class of functions known as copulas is used; it is shown that the correlations arise from a mixture of two copulas.
{"title":"Dependence characteristics of face recognition algorithms","authors":"A. Rukhin, P. Grother, P. Phillips, Stefan Leigh, A. Heckert, E. Newton","doi":"10.1109/ICPR.2002.1048230","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048230","url":null,"abstract":"Nonparametric statistics for quantifying dependence between the output rankings of face recognition algorithms are described Analysis of the archived results of a large face recognition study shows that even the better algorithms exhibit significantly different behaviors. It is found that there is significant dependence in the rankings given by two algorithms to similar and dissimilar faces but that other samples are ranked independently. A class of functions known as copulas is used; it is shown that the correlations arise from a mixture of two copulas.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116647218","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048459
C. Fookes, Bennamoun, A. Lamanna
Mutual information (MI) has shown promise as an effective stereo matching measure for images affected by radiometric distortion. This is due to the robustness of MI against changes in illumination. However MI-based approaches are particularly prone to the generation of false matches due to the small statistical power of the matching windows. The paper proposes extensions to MI-based stereo matching in order to increase the robustness of the algorithm. Firstly, prior probabilities are incorporated into the MI measure in order to considerably increase the statistical power of the matching windows. These prior probabilities, which are calculated from the global joint histogram between the stereo pair, are tuned to a two level hierarchical approach. A 2D match surface, in which the match score is computed for every possible combination of template and matching window, is also utilised. This enforces left-right consistency and uniqueness constraints. These additions to MI-based stereo matching significantly enhance the algorithm's ability to detect correct matches while decreasing computation time and improving the accuracy.
{"title":"Improved stereo image matching using mutual information and hierarchical prior probabilities","authors":"C. Fookes, Bennamoun, A. Lamanna","doi":"10.1109/ICPR.2002.1048459","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048459","url":null,"abstract":"Mutual information (MI) has shown promise as an effective stereo matching measure for images affected by radiometric distortion. This is due to the robustness of MI against changes in illumination. However MI-based approaches are particularly prone to the generation of false matches due to the small statistical power of the matching windows. The paper proposes extensions to MI-based stereo matching in order to increase the robustness of the algorithm. Firstly, prior probabilities are incorporated into the MI measure in order to considerably increase the statistical power of the matching windows. These prior probabilities, which are calculated from the global joint histogram between the stereo pair, are tuned to a two level hierarchical approach. A 2D match surface, in which the match score is computed for every possible combination of template and matching window, is also utilised. This enforces left-right consistency and uniqueness constraints. These additions to MI-based stereo matching significantly enhance the algorithm's ability to detect correct matches while decreasing computation time and improving the accuracy.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"136 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131206586","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048363
Chun-Shien Lu, Jan-Ru Chen, H. M. Liao, Kuo-Chin Fan
This paper proposes a compressed domain video watermarking scheme for copyright protection. Our scheme is designed based on the concept of communications with side information. For making the real-time detection a reality, the watermark is directly embedded and detected in the VLC domain. The typical problems of video watermarking such as preservation of bit rate, video attacks, real-time detection is examined. The performance of the new watermarking scheme is examined by checking its robustness capability against attacks together with false positive analysis.
{"title":"Real-time MPEG2 video watermarking in the VLC domain","authors":"Chun-Shien Lu, Jan-Ru Chen, H. M. Liao, Kuo-Chin Fan","doi":"10.1109/ICPR.2002.1048363","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048363","url":null,"abstract":"This paper proposes a compressed domain video watermarking scheme for copyright protection. Our scheme is designed based on the concept of communications with side information. For making the real-time detection a reality, the watermark is directly embedded and detected in the VLC domain. The typical problems of video watermarking such as preservation of bit rate, video attacks, real-time detection is examined. The performance of the new watermarking scheme is examined by checking its robustness capability against attacks together with false positive analysis.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131867060","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048231
B. Fasel
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task but is also robust with regard to face location changes and scale variations. This is achieved by deploying convolutional neural networks, which are either trained for facial expression recognition or face identity recognition. Combining the outputs of these networks allows us to obtain a subject dependent or personalized recognition of facial expressions.
{"title":"Robust face analysis using convolutional neural networks","authors":"B. Fasel","doi":"10.1109/ICPR.2002.1048231","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048231","url":null,"abstract":"Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task but is also robust with regard to face location changes and scale variations. This is achieved by deploying convolutional neural networks, which are either trained for facial expression recognition or face identity recognition. Combining the outputs of these networks allows us to obtain a subject dependent or personalized recognition of facial expressions.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"392 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133052623","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048383
A. Robles-Kelly, Sudeep Sarkar, E. Hancock
We present a fast non-iterative method for approximating the leading eigenvector so as to render graph-spectral based grouping algorithms more efficient. The approximation is based on a linear perturbation analysis and applies to matrices that are non-sparse, non-negative and symmetric. For an N/spl times/N matrix, the approximation can be implemented with complexity as low as O(4N/sup 2/). We provide a performance analysis and demonstrate the usefulness of our method on image segmentation problems.
{"title":"A fast leading eigenvector approximation for segmentation and grouping","authors":"A. Robles-Kelly, Sudeep Sarkar, E. Hancock","doi":"10.1109/ICPR.2002.1048383","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048383","url":null,"abstract":"We present a fast non-iterative method for approximating the leading eigenvector so as to render graph-spectral based grouping algorithms more efficient. The approximation is based on a linear perturbation analysis and applies to matrices that are non-sparse, non-negative and symmetric. For an N/spl times/N matrix, the approximation can be implemented with complexity as low as O(4N/sup 2/). We provide a performance analysis and demonstrate the usefulness of our method on image segmentation problems.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131736428","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048345
P. Paclík, R. Duin, Geert M. P. van Kempen, R. Kohlus
In this paper we present an application of statistical pattern recognition for segmentation of backscatter images (BSE) in product analysis of laundry detergents. Currently, application experts segment BSE images interactively which is both time consuming and expert dependent. We present a new, automatic procedure for supervised BSE segmentation which is trained using additional multi-spectral EDX images. Each time a new feature selection procedure is employed to find a convenient feature subset for a particular segmentation problem. The performance of the presented algorithm is evaluated using ground-truth segmentation results. It is compared with that of interactive segmentation performed by the analyst.
{"title":"Supervised segmentation of textures in backscatter images","authors":"P. Paclík, R. Duin, Geert M. P. van Kempen, R. Kohlus","doi":"10.1109/ICPR.2002.1048345","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048345","url":null,"abstract":"In this paper we present an application of statistical pattern recognition for segmentation of backscatter images (BSE) in product analysis of laundry detergents. Currently, application experts segment BSE images interactively which is both time consuming and expert dependent. We present a new, automatic procedure for supervised BSE segmentation which is trained using additional multi-spectral EDX images. Each time a new feature selection procedure is employed to find a convenient feature subset for a particular segmentation problem. The performance of the presented algorithm is evaluated using ground-truth segmentation results. It is compared with that of interactive segmentation performed by the analyst.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131936883","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048485
M. Koskela, Jorma T. Laaksonen, E. Oja
The MPEG-7 standard is emerging as both a general framework for content description and a collection of specific, agreed-upon content descriptors. We have developed a neural, self-organizing technique for content-based image retrieval. In this paper we apply the visual content descriptors provided by MPEG-7 in our PicSOM system and compare our own image indexing technique with a reference method based on vector quantization. The results of our experiments show that the MPEG-7 descriptors can be used as such in the PicSOM system.
{"title":"Using MPEG-7 descriptors in image retrieval with self-organizing maps","authors":"M. Koskela, Jorma T. Laaksonen, E. Oja","doi":"10.1109/ICPR.2002.1048485","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048485","url":null,"abstract":"The MPEG-7 standard is emerging as both a general framework for content description and a collection of specific, agreed-upon content descriptors. We have developed a neural, self-organizing technique for content-based image retrieval. In this paper we apply the visual content descriptors provided by MPEG-7 in our PicSOM system and compare our own image indexing technique with a reference method based on vector quantization. The results of our experiments show that the MPEG-7 descriptors can be used as such in the PicSOM system.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133966430","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 : 2002-12-10DOI: 10.1109/ICPR.2002.1048431
Gu Xu, Yu-Fei Ma, HongJiang Zhang, Shiqiang Yang
Motion is an important cue for video understanding and is widely used in many semantic video analyses. We present a new motion representation scheme in which motion in a video is represented by the responses of frames to a set of motion filters. Each of these filters is designed to be most responsive to a type of dominant motion. Then we employ hidden Markov models (HMMs) to characterize the motion patterns based on these features and thus classify basketball video into 16 events. The evaluation by human satisfaction rate to classification result is 75%, demonstrating effectiveness of the proposed approach to recognizing semantic events in video.
{"title":"Motion based event recognition using HMM","authors":"Gu Xu, Yu-Fei Ma, HongJiang Zhang, Shiqiang Yang","doi":"10.1109/ICPR.2002.1048431","DOIUrl":"https://doi.org/10.1109/ICPR.2002.1048431","url":null,"abstract":"Motion is an important cue for video understanding and is widely used in many semantic video analyses. We present a new motion representation scheme in which motion in a video is represented by the responses of frames to a set of motion filters. Each of these filters is designed to be most responsive to a type of dominant motion. Then we employ hidden Markov models (HMMs) to characterize the motion patterns based on these features and thus classify basketball video into 16 events. The evaluation by human satisfaction rate to classification result is 75%, demonstrating effectiveness of the proposed approach to recognizing semantic events in video.","PeriodicalId":159502,"journal":{"name":"Object recognition supported by user interaction for service robots","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121224503","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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