Many previous image processing methods discard low-frequency components of images to extract illumination invariant for face recognition. However, this method may cause distortion of processed images and perform poorly under normal lighting. In this paper, a new method is proposed to deal with illumination problem in face recognition. Firstly, we define a score to denote a relative difference of the first and second largest similarities between the query input and the individuals in the gallery classes. Then, according to the score, we choose the appropriate images, raw or processed images, to involve the recognition. The experiment in ORL, CMU-PIE and Extended Yale B face databases shows that our adaptive method give more robust result after combination and perform better than the traditional fusion operators, the sum and the maximum of similarities.
{"title":"Raw vs. Processed: How to Use the Raw and Processed Images for Robust Face Recognition under Varying Illumination","authors":"Li Xu, Lei Huang, Chang-ping Liu","doi":"10.1109/ICPR.2010.660","DOIUrl":"https://doi.org/10.1109/ICPR.2010.660","url":null,"abstract":"Many previous image processing methods discard low-frequency components of images to extract illumination invariant for face recognition. However, this method may cause distortion of processed images and perform poorly under normal lighting. In this paper, a new method is proposed to deal with illumination problem in face recognition. Firstly, we define a score to denote a relative difference of the first and second largest similarities between the query input and the individuals in the gallery classes. Then, according to the score, we choose the appropriate images, raw or processed images, to involve the recognition. The experiment in ORL, CMU-PIE and Extended Yale B face databases shows that our adaptive method give more robust result after combination and perform better than the traditional fusion operators, the sum and the maximum of similarities.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124392806","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}
Visual exploration systems enable users to search, browse, and explore voluminous multimedia databases in an interactive and playful manner. Whether users know the database's contents in advance or not, these systems guide the user's exploration process by visualizing the database contents and allowing him or her to issue queries intuitively. In order to improve the efficiency of content-based visual exploration systems, we propose an efficient query evaluation scheme which aims at reducing the total number of costly similarity computations. We evaluate our approach on different state-of-the-art image databases.
{"title":"Improving the Efficiency of Content-Based Multimedia Exploration","authors":"C. Beecks, Sascha Wiedenfeld, T. Seidl","doi":"10.1109/ICPR.2010.774","DOIUrl":"https://doi.org/10.1109/ICPR.2010.774","url":null,"abstract":"Visual exploration systems enable users to search, browse, and explore voluminous multimedia databases in an interactive and playful manner. Whether users know the database's contents in advance or not, these systems guide the user's exploration process by visualizing the database contents and allowing him or her to issue queries intuitively. In order to improve the efficiency of content-based visual exploration systems, we propose an efficient query evaluation scheme which aims at reducing the total number of costly similarity computations. We evaluate our approach on different state-of-the-art image databases.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124754186","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}
The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to avoid the problem of failing of misled mean shift vectors. As a result, we can obtain a more robust clustering result compared to the conventional mean shift algorithm. We also propose an alternative way to assign weights for large size datasets and noisy datasets.
{"title":"Improved Mean Shift Algorithm with Heterogeneous Node Weights","authors":"J. Yoon, Simon P. Wilson","doi":"10.1109/ICPR.2010.1026","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1026","url":null,"abstract":"The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to avoid the problem of failing of misled mean shift vectors. As a result, we can obtain a more robust clustering result compared to the conventional mean shift algorithm. We also propose an alternative way to assign weights for large size datasets and noisy datasets.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132080952","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}
The Spiral Architecture has been developed as a fast way of indexing a hexagonal pixel-based image. In combination with spiral addition and spiral multiplication, methods have been developed for hexagonal image processing operations such as translation and rotation. Using the Spiral Architecture as the basis for our operator structure, we present a general approach to the computation of adaptive coarse scale Laplacian operators for use on hexagonal pixel-based images. We evaluate the proposed operators using simulated hexagonal images and demonstrate improved performance when compared with rectangular Laplacian operators such as Marr-Hildreth
{"title":"Coarse Scale Feature Extraction Using the Spiral Architecture Structure","authors":"S. Coleman, B. Scotney, B. Gardiner","doi":"10.1109/ICPR.2010.580","DOIUrl":"https://doi.org/10.1109/ICPR.2010.580","url":null,"abstract":"The Spiral Architecture has been developed as a fast way of indexing a hexagonal pixel-based image. In combination with spiral addition and spiral multiplication, methods have been developed for hexagonal image processing operations such as translation and rotation. Using the Spiral Architecture as the basis for our operator structure, we present a general approach to the computation of adaptive coarse scale Laplacian operators for use on hexagonal pixel-based images. We evaluate the proposed operators using simulated hexagonal images and demonstrate improved performance when compared with rectangular Laplacian operators such as Marr-Hildreth","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114186381","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}
Hand pointing has been an intuitive gesture for human interaction with computers. Big challenges are still posted for accurate estimation of finger pointing direction in a 3D space. In this paper, we present a novel hand pointing estimation system based on two regular cameras, which includes hand region detection, hand finger estimation, two views’ feature detection, and 3D pointing direction estimation. Based on the idea of binary pattern face detector, we extend the work to hand detection, in which a polar coordinate system is proposed to represent the hand region, and achieved a good result in terms of the robustness to hand orientation variation. To estimate the pointing direction, we applied an AAM based approach to detect and track 14 feature points along the hand contour from a top view and a side view. Combining two views of the hand features, the 3D pointing direction is estimated. The experiments have demonstrated the feasibility of the system.
{"title":"Hand Pointing Estimation for Human Computer Interaction Based on Two Orthogonal-Views","authors":"Kaoning Hu, Shaun J. Canavan, L. Yin","doi":"10.1109/ICPR.2010.916","DOIUrl":"https://doi.org/10.1109/ICPR.2010.916","url":null,"abstract":"Hand pointing has been an intuitive gesture for human interaction with computers. Big challenges are still posted for accurate estimation of finger pointing direction in a 3D space. In this paper, we present a novel hand pointing estimation system based on two regular cameras, which includes hand region detection, hand finger estimation, two views’ feature detection, and 3D pointing direction estimation. Based on the idea of binary pattern face detector, we extend the work to hand detection, in which a polar coordinate system is proposed to represent the hand region, and achieved a good result in terms of the robustness to hand orientation variation. To estimate the pointing direction, we applied an AAM based approach to detect and track 14 feature points along the hand contour from a top view and a side view. Combining two views of the hand features, the 3D pointing direction is estimated. The experiments have demonstrated the feasibility of the system.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123798594","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}
This paper presents the binary flattenings of shapes, first as a connected operator suppressing particles or holes, second as an erosion in a particular lattice of shapes. Using this erosion, it is then possible to construct a distance from a shape to another and derive from it an interpolation function between shapes.
{"title":"Shape Interpolation with Flattenings","authors":"F. Meyer","doi":"10.1109/ICPR.2010.514","DOIUrl":"https://doi.org/10.1109/ICPR.2010.514","url":null,"abstract":"This paper presents the binary flattenings of shapes, first as a connected operator suppressing particles or holes, second as an erosion in a particular lattice of shapes. Using this erosion, it is then possible to construct a distance from a shape to another and derive from it an interpolation function between shapes.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124943186","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}
Traditional methods of detection tend to under perform in the presence of the strong and variable background clutter that characterize a medical ultrasound image. In this paper, we present a novel diffusion based technique to localize acoustically dense objects in an ultrasound image. The approach is premised on the observation that the topology of noise in ultrasound images is more sensitive to diffusion than that of any such physical object (???). We show that our method when applied to the problem of fetal head detection and automatic measurement of head circumference in 59 obstetric scans compares remarkably well with manually assisted measurements. Based on fetal age estimates and their bounds specified in Standard OB Tables [6], the Gestational Age predictions from automated measurements is found to be within ± 2SD in 95% and 98% of cases when compared with manual measurements by two experts. The framework is general and can be extended to object localization in diverse applications of ultrasound imaging.
{"title":"Model-Based Detection of Acoustically Dense Objects in Ultrasound","authors":"Jyotirmoy Banerjee, K. Krishnan","doi":"10.1109/ICPR.2010.1122","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1122","url":null,"abstract":"Traditional methods of detection tend to under perform in the presence of the strong and variable background clutter that characterize a medical ultrasound image. In this paper, we present a novel diffusion based technique to localize acoustically dense objects in an ultrasound image. The approach is premised on the observation that the topology of noise in ultrasound images is more sensitive to diffusion than that of any such physical object (???). We show that our method when applied to the problem of fetal head detection and automatic measurement of head circumference in 59 obstetric scans compares remarkably well with manually assisted measurements. Based on fetal age estimates and their bounds specified in Standard OB Tables [6], the Gestational Age predictions from automated measurements is found to be within ± 2SD in 95% and 98% of cases when compared with manual measurements by two experts. The framework is general and can be extended to object localization in diverse applications of ultrasound imaging.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117116781","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}
David Rivest-Hénault, M. Cheriet, S. Deschênes, C. Lapierre
A new level-set based active contour method for the segmentation of small blood vessels and other elongated structures is presented. Its main particularity is the presence of a length increasing force in the contour driving equation. The effect of this force is to push the active contour in the direction of thin elongated shapes. Although the proposed force is not stable in general, our experiments show that with few precautions it can successfully be integrated in a practical segmentation scheme and that it helps to segment a longer part of the structures of interest. For the segmentation of blood vessels, this may reduce the amount of user interactivity needed: only a small region inside the structure of interest need to be specified.
{"title":"Length Increasing Active Contour for the Segmentation of Small Blood Vessels","authors":"David Rivest-Hénault, M. Cheriet, S. Deschênes, C. Lapierre","doi":"10.1109/ICPR.2010.685","DOIUrl":"https://doi.org/10.1109/ICPR.2010.685","url":null,"abstract":"A new level-set based active contour method for the segmentation of small blood vessels and other elongated structures is presented. Its main particularity is the presence of a length increasing force in the contour driving equation. The effect of this force is to push the active contour in the direction of thin elongated shapes. Although the proposed force is not stable in general, our experiments show that with few precautions it can successfully be integrated in a practical segmentation scheme and that it helps to segment a longer part of the structures of interest. For the segmentation of blood vessels, this may reduce the amount of user interactivity needed: only a small region inside the structure of interest need to be specified.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115789755","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}
We experimentally compare the performance of two fast algorithms for computing the local discrete wavelet transform of one-dimensional signals: the Mallatalgorithm and a recursive algorithm. For the comparison purposes, we analyze Haar wavelet bases for one and two-dimensional signals, an extension of the Haar basis with the scale coefficient 3, and biorthogonal polynomial spline wavelets with finite support.
{"title":"Research the Performance of a Recursive Algorithm of the Local Discrete Wavelet Transform","authors":"V. N. Kopenkov, V. Myasnikov","doi":"10.1109/ICPR.2010.1081","DOIUrl":"https://doi.org/10.1109/ICPR.2010.1081","url":null,"abstract":"We experimentally compare the performance of two fast algorithms for computing the local discrete wavelet transform of one-dimensional signals: the Mallatalgorithm and a recursive algorithm. For the comparison purposes, we analyze Haar wavelet bases for one and two-dimensional signals, an extension of the Haar basis with the scale coefficient 3, and biorthogonal polynomial spline wavelets with finite support.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114982602","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}
This paper investigates a part-based recognition method of handwritten digits. In the proposed method, the global structure of digit patterns is discarded by representing each pattern by just a set of local feature vectors. The method is then comprised of two steps. First, each of J local feature vectors of a target pattern is recognized into one of ten categories (``0''--``9'') by the nearest neighbor discrimination with a large database of reference vectors. Second, the category of the target pattern is determined by the majority voting on the J local recognition results. Despite a pessimistic expectation, we have reached recognition rates much higher than 90% for the task of digit recognition.
{"title":"Analysis of Local Features for Handwritten Character Recognition","authors":"S. Uchida, M. Liwicki","doi":"10.1109/ICPR.2010.479","DOIUrl":"https://doi.org/10.1109/ICPR.2010.479","url":null,"abstract":"This paper investigates a part-based recognition method of handwritten digits. In the proposed method, the global structure of digit patterns is discarded by representing each pattern by just a set of local feature vectors. The method is then comprised of two steps. First, each of J local feature vectors of a target pattern is recognized into one of ten categories (``0''--``9'') by the nearest neighbor discrimination with a large database of reference vectors. Second, the category of the target pattern is determined by the majority voting on the J local recognition results. Despite a pessimistic expectation, we have reached recognition rates much higher than 90% for the task of digit recognition.","PeriodicalId":309591,"journal":{"name":"2010 20th International Conference on Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115492737","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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