Pub Date : 2005-10-01DOI: 10.1142/9789812834461_0011
Jonathan Milgram, R. Sabourin, M. Cheriet
The motivation of this work is based on two key observations. First, the classification algorithms can be separated into two main categories: discriminative and model-based approaches. Second, two types of patterns can generate problems: ambiguous patterns and outliers. While, the first approach tries to minimize the first type of error, but cannot deal effectively with outliers, the second approach, which is based on the development of a model for each class, make the outlier detection possible, but are not sufficiently discriminant. Thus, we propose to combine these two different approaches in a modular two-stage classification system embedded in a probabilistic framework. In the first stage we pre-estimate the posterior probabilities with a model-based approach and we re-estimate only the highest probabilities with appropriate Support Vector Classifiers (SVC) in the second stage. Another advantage of this combination is to reduce the principal burden of SVC, the processing time necessary to make a decision and to open the way to use SVC in classification problem with a large number of classes. Finally, the first experiments on the benchmark database MNIST have shown that our dynamic classification process allows to maintain the accuracy of SVCs, while decreasing complexity by a factor 8.7 and making the outlier rejection available.
{"title":"Combining Model-Based and Discriminative Approaches in a Modular Two-stage Classification System: Application to isolated Handwritten Digit Recognition","authors":"Jonathan Milgram, R. Sabourin, M. Cheriet","doi":"10.1142/9789812834461_0011","DOIUrl":"https://doi.org/10.1142/9789812834461_0011","url":null,"abstract":"The motivation of this work is based on two key observations. First, the classification algorithms can be separated into two main categories: discriminative and model-based approaches. Second, two types of patterns can generate problems: ambiguous patterns and outliers. While, the first approach tries to minimize the first type of error, but cannot deal effectively with outliers, the second approach, which is based on the development of a model for each class, make the outlier detection possible, but are not sufficiently discriminant. Thus, we propose to combine these two different approaches in a modular two-stage classification system embedded in a probabilistic framework. In the first stage we pre-estimate the posterior probabilities with a model-based approach and we re-estimate only the highest probabilities with appropriate Support Vector Classifiers (SVC) in the second stage. Another advantage of this combination is to reduce the principal burden of SVC, the processing time necessary to make a decision and to open the way to use SVC in classification problem with a large number of classes. Finally, the first experiments on the benchmark database MNIST have shown that our dynamic classification process allows to maintain the accuracy of SVCs, while decreasing complexity by a factor 8.7 and making the outlier rejection available.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131657723","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 : 2004-07-13DOI: 10.1142/9789812834461_0007
Joan-Josep Climent, Pere Mars
This paper presents a tracking algorithm for automatic instrument localization in robotically assisted laparoscopic surgery. We present a simple and robust system that doesn't need the presence of artificial marks, or special colours to distinguish the instruments. So, the system enables the robot to track the usual instruments used in laparoscopic operations. Since the instruments are normally the most structured objects in laparoscopic scenes, the algorithm uses the Hough transform to detect straight lines in the scene. In order to distinguish among different instruments or other structured elements present in the scene, motion information is also used. We give in this paper a detailed description of all stages of the system.
{"title":"Automatic Instrument Localization in Laparoscopic Surgery","authors":"Joan-Josep Climent, Pere Mars","doi":"10.1142/9789812834461_0007","DOIUrl":"https://doi.org/10.1142/9789812834461_0007","url":null,"abstract":"This paper presents a tracking algorithm for automatic instrument localization in robotically assisted laparoscopic surgery. We present a simple and robust system that doesn't need the presence of artificial marks, or special colours to distinguish the instruments. So, the system enables the robot to track the usual instruments used in laparoscopic operations. Since the instruments are normally the most structured objects in laparoscopic scenes, the algorithm uses the Hough transform to detect straight lines in the scene. In order to distinguish among different instruments or other structured elements present in the scene, motion information is also used. We give in this paper a detailed description of all stages of the system.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123933863","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 : 2004-07-09DOI: 10.1142/9789812834461_0009
Cong Jin, Jiaxiong Peng
Blind digital watermarking, which can detect watermark without using the original image, is a key technique practical intellectual property protecting systems and concealment correspondence systems. In this paper, we discussed a blind detection method for the digital image watermark. The theories research show that the orthogonal projection sequence of a digital image is one-to-one correspondence with this digital image. To make use of this conclusion, we designed and realized a kind of blind watermark detector with the good performance. To calculate the correlation value between the image and watermark, the intensity information of digital image is not adopted, but the orthogonal projection sequence of this image is adopted. Experiment results show that this watermark detector not only to have very strong resistant ability to translation and rotation attacks, but also to have the good robustness to Gaussian noise. Performance of this watermark detector is better than general detector designed by making use of the intensity information directly. The conclusions obtained by experiments are useful to the research in the future.
{"title":"Robustness of a blind Image Watermark detector designed by orthogonal Projection","authors":"Cong Jin, Jiaxiong Peng","doi":"10.1142/9789812834461_0009","DOIUrl":"https://doi.org/10.1142/9789812834461_0009","url":null,"abstract":"Blind digital watermarking, which can detect watermark without using the original image, is a key technique practical intellectual property protecting systems and concealment correspondence systems. In this paper, we discussed a blind detection method for the digital image watermark. The theories research show that the orthogonal projection sequence of a digital image is one-to-one correspondence with this digital image. To make use of this conclusion, we designed and realized a kind of blind watermark detector with the good performance. To calculate the correlation value between the image and watermark, the intensity information of digital image is not adopted, but the orthogonal projection sequence of this image is adopted. Experiment results show that this watermark detector not only to have very strong resistant ability to translation and rotation attacks, but also to have the good robustness to Gaussian noise. Performance of this watermark detector is better than general detector designed by making use of the intensity information directly. The conclusions obtained by experiments are useful to the research in the future.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"22 6S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115947189","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 : 2003-02-28DOI: 10.1142/9789812834461_0003
A. Turiel
In the latest years, multifractal analysis has been applied to image analysis. The multifractal framework takes advantage of multiscaling properties of images to decompose them as a collection of different fractal components, each one associated to a singularity exponent (an exponent characterizing the way in which that part of the image evolves under changes in scale). One of those components, characterized by the least possible exponent, seems to be the most informative about the whole image. Very recently it has been proposed an algorithm to reconstruct the image from this component, just using physical information conveyed by it. In this paper, we will show that the same algorithm can be used to assess the relevance of the other fractal parts of the image.
{"title":"Relevance of multifractal Textures in Static Images","authors":"A. Turiel","doi":"10.1142/9789812834461_0003","DOIUrl":"https://doi.org/10.1142/9789812834461_0003","url":null,"abstract":"In the latest years, multifractal analysis has been applied to image analysis. The multifractal framework takes advantage of multiscaling properties of images to decompose them as a collection of different fractal components, each one associated to a singularity exponent (an exponent characterizing the way in which that part of the image evolves under changes in scale). One of those components, characterized by the least possible exponent, seems to be the most informative about the whole image. Very recently it has been proposed an algorithm to reconstruct the image from this component, just using physical information conveyed by it. In this paper, we will show that the same algorithm can be used to assess the relevance of the other fractal parts of the image.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130323115","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 : 1900-01-01DOI: 10.1142/9789812834461_0008
M. Hassaballah, M. Makky, Y. B. Mahdy
{"title":"A Fast Fractal Image Compression Method Based on Entropy","authors":"M. Hassaballah, M. Makky, Y. B. Mahdy","doi":"10.1142/9789812834461_0008","DOIUrl":"https://doi.org/10.1142/9789812834461_0008","url":null,"abstract":"","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131784880","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 : 1900-01-01DOI: 10.1142/9789812834461_0006
A. Boudraa, L. Bentabet, F. Salzenstein, L. Guillon
{"title":"Dempster-Shafer's Basic Probability Assignment Based on fuzzy Membership Functions","authors":"A. Boudraa, L. Bentabet, F. Salzenstein, L. Guillon","doi":"10.1142/9789812834461_0006","DOIUrl":"https://doi.org/10.1142/9789812834461_0006","url":null,"abstract":"","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124862747","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 : 1900-01-01DOI: 10.1142/9789812834461_0002
M. C. D. Andrade
{"title":"An Interactive Algorithm for Image smoothing and Segmentation","authors":"M. C. D. Andrade","doi":"10.1142/9789812834461_0002","DOIUrl":"https://doi.org/10.1142/9789812834461_0002","url":null,"abstract":"","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128509078","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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