{"title":"Factorization with missing data for 3D structure recovery","authors":"Rui F. C. Guerreiro, P. Aguiar","doi":"10.1109/MMSP.2002.1203259","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203259","url":null,"abstract":"Matrix factorization methods are now widely used to recover 3D structure from 2D projections [C. Tomasi and T. Kanade. International Journal of Computer Vision, 9(2), 1992] . In this practice, the observation matrix to be factored out has missing data, due to the limited field of view and the occlusion that occur in real video sequences. In opposition to the optimality of the SVD to factor out matrices without missing entries, the optimal solution for the missing data case is not known. In R.F.C. Guerreiro and P.M.Q. Aguiar [IEEE ICIP, New York, USA, September 2002] we introduced suboptimal algorithms that proved to be more efficient than previous approaches to the factorization of matrices with missing data. In this paper we make an experimental analysis of the algorithms of R.F.C. Guerreiro and P.M.Q. Aguiar [IEEE ICIP, New York, USA, September 2002] and demonstrate their performance in virtual reality and video compression applications. We conclude that these algorithms are adequate to the amount of missing entries that may occur when processing real videos; robust to the typical level of noise in practical applications; and computationally as simple as the factorization of matrices without missing entries.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121796725","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}
{"title":"Content-based video database retrieval through robust corner tracking","authors":"F. Mokhtarian, F. Mohanna","doi":"10.1109/MMSP.2002.1203287","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203287","url":null,"abstract":"A content-based video retrieval system based on extracting corners from frames and tracking them through video databases is proposed. To extract corners from each frame of video sequence in pre-processing stage, proposed multi-scale corner detector is applied. As a user interface, a proposed fast active contour model has been used to specify one object of interest as a query in one of the frames of each video shot. This frame which is in the selected-frame are extracted and tracked forwardly and backwardly through the whole of that shot using proposed multiple-match tracker. The multi-match tracker, which does not make any important assumptions or use any motion models, can retrieve the query in any video sequence even when there is unconstrained and non-smooth motion. By tracking the corners or query object forwardly and backwardly, the positions of similar objects in each video frame are determined. Two methods are considered for demonstrating the query and its similar objects to the user. Experiments have been carried out on a wide range of real video databases. All the results confirm that among the existing techniques, proposed method which exploits image corners is believed to be more generally applicable.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122792255","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}
{"title":"Watermarking for 3D NURBS graphic data","authors":"Jae Jun Lee, N. Cho, Jongweon Kim","doi":"10.1109/MMSP.2002.1203306","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203306","url":null,"abstract":"In this paper, two watermarking algorithms for nonuniform rational b-spline (NURBS) are proposed. One is suitable for steganography, and the other for watermarking. Both algorithms do not directly embed data into the parameters of NURBS, but into the 2D virtual images extracted from the sampled points of 3D model. As a result, the proposed algorithm for steganography preserves the data size of the model and can embed more information than the conventional algorithm. Also, the watermarking algorithm can use any of existing 2D watermarking techniques when embedding data into the virtual 2D images. From the experiment, it is found that the algorithm is robust against the operations to knots and control points. It is also robust against the remodeling of NURBS model through approximation.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121987287","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}
{"title":"Comparison of residual compression methods in motion compensated video","authors":"Weekiong Poh, D. Monro","doi":"10.1109/MMSP.2002.1203260","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203260","url":null,"abstract":"In this paper, we compare the objective performance of several algorithms for coding motion compensated residuals, including the wavelet transform, matching pursuits and an improved embedded DCT-based coding method. A motion-compensated prediction-based approach with overlapped block matching compensation (OBMC) is used in all systems in the evaluation. The results show that matching pursuits outperforms transform-based methods by 0.1-1.0 dB in PSNR. The embedded DCT coder usually outperforms the wavelet-based SPIHT-AC in the examples considered, and SPIHT is usually better than JPEG 2000.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123481339","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}
Tiago Rosa Maria Paula Queluz, T. Brandão, A. Rodrigues
{"title":"Signal combining techniques for video watermarking extraction","authors":"Tiago Rosa Maria Paula Queluz, T. Brandão, A. Rodrigues","doi":"10.1109/MMSP.2002.1203317","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203317","url":null,"abstract":"This paper analyses the effect of signal combining techniques in video watermark extraction. A spread-spectrum like discrete cosine transform domain (DCT domain) watermarking technique is used as embedding method, together with common error correction codes (BCH, Reed-Solomon with multilevel signaling, and binary convolutional codes with Viterbi decoding). Besides an analytical evaluation of the signal combining strategies, its effectiveness is also assessed experimentally under MPEG-2 video compression.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"366 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114094127","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}
{"title":"Statistical techniques in video data management","authors":"M. Naphade","doi":"10.1109/MMSP.2002.1203284","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203284","url":null,"abstract":"Media analysis for video indexing is witnessing an increasing influence of statistical techniques. Examples of these techniques include the use of generative models as well as discriminant techniques for video structuring, classification, summarization, indexing and retrieval. Advances in multimedia analysis are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases and smart sensors. This paper highlights the statistical techniques in multimedia retrieval with particular emphasis on semantic characterization.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121467874","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}
{"title":"Scalable robust audio fingerprinting using MPEG-7 content description","authors":"J. Herre, O. Hellmuth, M. Cremer","doi":"10.1109/MMSP.2002.1203273","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203273","url":null,"abstract":"Much interest has recently been received by systems for audio fingerprinting which enable automatic content-based identification by extracting unique signatures from the signal. Among other aspects, the main requirements for such systems include robustness to a wide range of signal distortions and availability of fast search methods, even for large fingerprint databases. This paper describes the provisions of the MPEG-7 standard for audio fingerprinting which allow for interoperability of fingerprints generated according to the open standardized specification for extraction. In addition, it discusses the ability to generate scalable fingerprints providing different trade-offs between fingerprint compactness, temporal coverage and robustness of recognition, and gives experimental results for a number of system configurations.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121474072","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}
{"title":"Summarizing video using non-negative similarity matrix factorization","authors":"Matthew L. Cooper, J. Foote","doi":"10.1109/MMSP.2002.1203239","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203239","url":null,"abstract":"We present a novel approach to automatically extracting summary excerpts from audio video and video. Our approach is to maximize the average similarity between the excerpt and the source. We first calculate a similarity matrix by comparing each pair of time samples using a quantitative similarity measure. To determine the segment with highest average similarity, we maximize the summation of the self-similarity matrix over the support of the segment. To select multiple excerpts while avoiding redundancy, we compute the non-negative matrix factorization (NMF) of the similarity matrix into its essential structural components. We then build a summary comprised of excerpts from the main components, selecting the excerpts for maximum average similarity within each component. Variations integrating segmentation and other information are also discussed, and experimental results are presented.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123995875","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}
{"title":"An experimental study on the performance of visual information retrieval similarity models","authors":"H. Eidenberger, C. Breiteneder","doi":"10.1109/MMSP.2002.1203289","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203289","url":null,"abstract":"This paper is an experimental study on the performance of the two major methods for macro-level similarity measurement: linear weighted merging and logical retrieval. Performance is measured as the average query execution time for a significant number of tests. The two models were implemented in the standard version (as they are applied in a number of prototypes) and in an optimized version. The results show that optimized logical retrieval clearly outperforms optimized linear weighted merging.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134170901","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}
{"title":"Compact and robust speech recognition for embedded use on microprocessors","authors":"N. Hataoka, H. Kokubo, Y. Obuchi, A. Amano","doi":"10.1109/MMSP.2002.1203302","DOIUrl":"https://doi.org/10.1109/MMSP.2002.1203302","url":null,"abstract":"We propose a compact and noise robust embedded speech recognition system implemented on microprocessors aiming for sophisticated HMIs (human machine interfaces) of car information systems. The compactness is essential for embedded systems because there are strict restrictions of CPU (central processing unit) power and available memory capacities. In this paper, first we report noise robust acoustic HMMs (hidden Markov models) and a compact spectral subtraction (SS) method after exhausting evaluation stages using real speech data recorded at car running environments. Next, we propose very novel memory assignment of acoustic models based on the product codes or sub-vector quantization technique resulting on 1 fourth memory reduction for the 2000-word vocabulary.","PeriodicalId":398813,"journal":{"name":"2002 IEEE Workshop on Multimedia Signal Processing.","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131121619","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}