Pub Date : 2006-12-01DOI: 10.1142/9789812834461_0023
M. Aguzzi, M. Albanesi
In this paper a novel approach to the compression of sparse histogram images is proposed. First, we define a sparsity index which gives hints on the relationship between the mathematical concept of matrix sparsity and the visual information of pixel distribution. We use this index to better understand the scope of our approach and its preferred field of applicability, and to evaluate the performance. We present two algorithms which modify one of the coding steps of the JPEG2000 standard for lossless image compression. A theoretical study of the gain referring to the standard is given. Experimental results on well standardized images of the literature confirm the expectations, especially for high sparse images.
{"title":"A Novel Approach to Sparse Histogram Image Lossless Compression using JPEG2000","authors":"M. Aguzzi, M. Albanesi","doi":"10.1142/9789812834461_0023","DOIUrl":"https://doi.org/10.1142/9789812834461_0023","url":null,"abstract":"In this paper a novel approach to the compression of sparse histogram images is proposed. First, we define a sparsity index which gives hints on the relationship between the mathematical concept of matrix sparsity and the visual information of pixel distribution. We use this index to better understand the scope of our approach and its preferred field of applicability, and to evaluate the performance. We present two algorithms which modify one of the coding steps of the JPEG2000 standard for lossless image compression. A theoretical study of the gain referring to the standard is given. Experimental results on well standardized images of the literature confirm the expectations, especially for high sparse images.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122603116","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 : 2006-12-01DOI: 10.1142/9789812834461_0025
H. Lin, Syuan-Liang Chen, Jen-Hung Lin
In this paper we present a system for the reconstruction of 3D models of architectural scenes from single or multiple uncalibrated images. The partial 3D model of a building is recovered from a single image using geometric constraints such as parallelism and orthogonality, which are likely to be found in most architectural scenes. The approximate corner positions of a building are selected interactively by a user and then further refined automatically using Hough transform. The relative depths of the corner points are calculated according to the perspective projection model. Partial 3D models recovered from different viewpoints are registered to a common coordinate system for integration. The 3D model registration process is carried out using modified ICP (iterative closest point) algorithm with the initial parameters provided by geometric constraints of the building. The integrated 3D model is then fitted with piecewise planar surfaces to generate a more geometrically consistent model. The acquired images are finally mapped onto the surface of the reconstructed 3D model to create a photo-realistic model. A working system which allows a user to interactively build a 3D model of an architectural scene from single or multiple images has been proposed and implemented.
{"title":"Architectural Scene Reconstruction from single or Multiple Uncalibrated Images","authors":"H. Lin, Syuan-Liang Chen, Jen-Hung Lin","doi":"10.1142/9789812834461_0025","DOIUrl":"https://doi.org/10.1142/9789812834461_0025","url":null,"abstract":"In this paper we present a system for the reconstruction of 3D models of architectural scenes from single or multiple uncalibrated images. The partial 3D model of a building is recovered from a single image using geometric constraints such as parallelism and orthogonality, which are likely to be found in most architectural scenes. The approximate corner positions of a building are selected interactively by a user and then further refined automatically using Hough transform. The relative depths of the corner points are calculated according to the perspective projection model. Partial 3D models recovered from different viewpoints are registered to a common coordinate system for integration. The 3D model registration process is carried out using modified ICP (iterative closest point) algorithm with the initial parameters provided by geometric constraints of the building. The integrated 3D model is then fitted with piecewise planar surfaces to generate a more geometrically consistent model. The acquired images are finally mapped onto the surface of the reconstructed 3D model to create a photo-realistic model. A working system which allows a user to interactively build a 3D model of an architectural scene from single or multiple images has been proposed and implemented.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130827587","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 : 2005-11-01DOI: 10.1142/9789812834461_0018
M. Choraś
Biometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. In fact, only biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. The future of biometrics will surely lead to systems based on image analysis as the data acquisition is very simple and requires only cameras, scanners or sensors. More importantly such methods could be passive, which means that the user does not have to take active part in the whole process or, in fact, would not even know that the process of identification takes place. There are many possible data sources for human identification systems, but the physiological biometrics seem to have many advantages over methods based on human behaviour. The most interesting human anatomical parts for such passive, physiological biometrics systems based on images acquired from cameras are face and ear. Both of those methods contain large volume of unique features that allow to distinctively identify many users and will be surely implemented into efficient biometrics systems for many applications. The article introduces to ear biometrics and presents its advantages over face biometrics in passive human identification systems. Then the geometrical method of feature extraction from human ear images in order to perform human identification is presented.
{"title":"Ear Biometrics Based on Geometrical Feature Extraction","authors":"M. Choraś","doi":"10.1142/9789812834461_0018","DOIUrl":"https://doi.org/10.1142/9789812834461_0018","url":null,"abstract":"Biometrics identification methods proved to be very efficient, more natural and easy for users than traditional methods of human identification. In fact, only biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. The future of biometrics will surely lead to systems based on image analysis as the data acquisition is very simple and requires only cameras, scanners or sensors. More importantly such methods could be passive, which means that the user does not have to take active part in the whole process or, in fact, would not even know that the process of identification takes place. There are many possible data sources for human identification systems, but the physiological biometrics seem to have many advantages over methods based on human behaviour. The most interesting human anatomical parts for such passive, physiological biometrics systems based on images acquired from cameras are face and ear. Both of those methods contain large volume of unique features that allow to distinctively identify many users and will be surely implemented into efficient biometrics systems for many applications. The article introduces to ear biometrics and presents its advantages over face biometrics in passive human identification systems. Then the geometrical method of feature extraction from human ear images in order to perform human identification is presented.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129940455","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 : 2005-11-01DOI: 10.1142/9789812834461_0015
A. Sugimoto, Mitsuhiro Kimura, Takashi Matsuyama
We propose a two-step method for detecting human heads with their orientations. In the first step, the method employs an ellipse as the contour model of human-head appearances to deal with wide variety of appearances. Our method then evaluates the ellipse to detect possible human heads. In the second step, on the other hand, our method focuses on features inside the ellipse, such as eyes, the mouth or cheeks, to model facial components. The method evaluates not only such components themselves but also their geometric configuration to eliminate false positives in the first step and, at the same time, to estimate face orientations. Our intensive experiments show that our method can correctly and stably detect human heads with their orientations.
{"title":"Detecting Human Heads with their orientations","authors":"A. Sugimoto, Mitsuhiro Kimura, Takashi Matsuyama","doi":"10.1142/9789812834461_0015","DOIUrl":"https://doi.org/10.1142/9789812834461_0015","url":null,"abstract":"We propose a two-step method for detecting human heads with their orientations. In the first step, the method employs an ellipse as the contour model of human-head appearances to deal with wide variety of appearances. Our method then evaluates the ellipse to detect possible human heads. In the second step, on the other hand, our method focuses on features inside the ellipse, such as eyes, the mouth or cheeks, to model facial components. The method evaluates not only such components themselves but also their geometric configuration to eliminate false positives in the first step and, at the same time, to estimate face orientations. Our intensive experiments show that our method can correctly and stably detect human heads with their orientations.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125411461","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 : 2005-11-01DOI: 10.1142/9789812834461_0016
A. Sappa, Niki Aifanti, S. Malassiotis, M. Strintzis
This paper presents a new approach for human walking modeling from monocular image sequences. A kinematics model and a walking motion model are introduced in order to exploit prior knowledge. The proposed technique consists of two steps. Initially, an efficient feature point selection and tracking approach is used to compute feature points’ trajectories. Peaks and valleys of these trajectories are used to detect key frames— frames where both legs are in contact with the floor. Secondly, motion models associated with each joint are locally tuned by using those key frames. Differently than previous approaches, this tuning process is not performed at every frame, reducing CPU time. In addition, the movement’s frequency is defined by the elapsed time between two consecutive key frames, which allows handling walking displacement at different speed. Experimental results with different video sequences are presented.
{"title":"Prior Knowledge Based Motion Model Representation","authors":"A. Sappa, Niki Aifanti, S. Malassiotis, M. Strintzis","doi":"10.1142/9789812834461_0016","DOIUrl":"https://doi.org/10.1142/9789812834461_0016","url":null,"abstract":"This paper presents a new approach for human walking modeling from monocular image sequences. A kinematics model and a walking motion model are introduced in order to exploit prior knowledge. The proposed technique consists of two steps. Initially, an efficient feature point selection and tracking approach is used to compute feature points’ trajectories. Peaks and valleys of these trajectories are used to detect key frames— frames where both legs are in contact with the floor. Secondly, motion models associated with each joint are locally tuned by using those key frames. Differently than previous approaches, this tuning process is not performed at every frame, reducing CPU time. In addition, the movement’s frequency is defined by the elapsed time between two consecutive key frames, which allows handling walking displacement at different speed. Experimental results with different video sequences are presented.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114963459","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 : 2005-11-01DOI: 10.1142/9789812834461_0021
Micky Kelager, Anders Fleron, Kenny Erleben
This paper describes an improvement of a classical energy-based model to simulate elastically deformable solids. The classical model lacks the ability to prevent the collapsing of solids under influence of external forces, such as user interactions and collision. A thorough explanation is given for the origins of instabilities, and extensions that solve the issues are proposed to the physical model. Within the original framework of the classical model a complete restoration of area and volume is introduced. The improved model is suitable for interactive simulation and can recover from volumetric collapsing, in particular upon large deformation.
{"title":"Area and Volume restoration in Elastically Deformable solids","authors":"Micky Kelager, Anders Fleron, Kenny Erleben","doi":"10.1142/9789812834461_0021","DOIUrl":"https://doi.org/10.1142/9789812834461_0021","url":null,"abstract":"This paper describes an improvement of a classical energy-based model to simulate elastically deformable solids. The classical model lacks the ability to prevent the collapsing of solids under influence of external forces, such as user interactions and collision. A thorough explanation is given for the origins of instabilities, and extensions that solve the issues are proposed to the physical model. Within the original framework of the classical model a complete restoration of area and volume is introduced. The improved model is suitable for interactive simulation and can recover from volumetric collapsing, in particular upon large deformation.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133971581","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 : 2005-11-01DOI: 10.1142/9789812834461_0017
J. Pantrigo, Ángel Sánchez, A. S. Montemayor, Kostas Gianikellis
Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Model-based tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensional state-space and, therefore, the tracking becomes computationally very expensive or even infeasible. Due to the dynamic nature of the problem, some sequential estimation algorithms like particle filters are usually applied to visual tracking. Unfortunately, particle filter fails in high dimensional estimation problems such as articulated objects or multiple object tracking. These problems are called emph{dynamic optimization problems}. Metaheuristics, which are high level general strategies for designing heuristics procedures, have emerged for solving many real world combinatorial problems as a way to efficiently and effectively exploring the problem search space. Path relinking (PR) and scatter search (SS) are evolutionary metaheuristics successfully applied to several hard optimization problems. PRPF and SSPF algorithms respectively hybridize both, particle filter and these two population-based metaheuristic schemes. In this paper, We present and compare two different hybrid algorithms called Path Relinking Particle Filter (PRPF) and Scatter Search Particle Filter (SSPF), applied to 2D human motion tracking. Experimental results show that the proposed algorithms increase the performance of standard particle filters.
{"title":"Combining Particle filter and Population-Based Metaheuristics for Visual Articulated Motion Tracking","authors":"J. Pantrigo, Ángel Sánchez, A. S. Montemayor, Kostas Gianikellis","doi":"10.1142/9789812834461_0017","DOIUrl":"https://doi.org/10.1142/9789812834461_0017","url":null,"abstract":"Visual tracking of articulated motion is a complex task with high computational costs. Because of the fact that articulated objects are usually represented as a set of linked limbs, tracking is performed with the support of a model. Model-based tracking allows determining object pose in an effortless way and handling occlusions. However, the use of articulated models generates a multidimensional state-space and, therefore, the tracking becomes computationally very expensive or even infeasible. Due to the dynamic nature of the problem, some sequential estimation algorithms like particle filters are usually applied to visual tracking. Unfortunately, particle filter fails in high dimensional estimation problems such as articulated objects or multiple object tracking. These problems are called emph{dynamic optimization problems}. Metaheuristics, which are high level general strategies for designing heuristics procedures, have emerged for solving many real world combinatorial problems as a way to efficiently and effectively exploring the problem search space. Path relinking (PR) and scatter search (SS) are evolutionary metaheuristics successfully applied to several hard optimization problems. PRPF and SSPF algorithms respectively hybridize both, particle filter and these two population-based metaheuristic schemes. In this paper, We present and compare two different hybrid algorithms called Path Relinking Particle Filter (PRPF) and Scatter Search Particle Filter (SSPF), applied to 2D human motion tracking. Experimental results show that the proposed algorithms increase the performance of standard particle filters.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122200775","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 : 2005-11-01DOI: 10.1142/9789812834461_0013
J. Melenchón, Ignasi Iriondo Sanz, L. Meler
A novel way to learn and track simultaneously the appearance of a previously non-seen face without intrusive techniques can be found in this article. The presented approach has a causal behaviour: no future frames are needed to process the current ones. The model used in the tracking process is refined with each input frame thanks to a new algorithm for the simultaneous and incremental computation of the singular value decomposition (SVD) and the mean of the data. Previously developed methods about iterative computation of SVD are taken into account and an original way to extract the mean information from the reduced SVD of a matrix is also considered. Furthermore, the results are produced with linear computational cost and sublinear memory requirements with respect to the size of the data. Finally, experimental results are included, showing the tracking performance and some comparisons between the batch and our incremental computation of the SVD with mean information.
{"title":"Simultaneous and Causal Appearance Learning and Tracking","authors":"J. Melenchón, Ignasi Iriondo Sanz, L. Meler","doi":"10.1142/9789812834461_0013","DOIUrl":"https://doi.org/10.1142/9789812834461_0013","url":null,"abstract":"A novel way to learn and track simultaneously the appearance of a previously non-seen face without intrusive techniques can be found in this article. The presented approach has a causal behaviour: no future frames are needed to process the current ones. The model used in the tracking process is refined with each input frame thanks to a new algorithm for the simultaneous and incremental computation of the singular value decomposition (SVD) and the mean of the data. Previously developed methods about iterative computation of SVD are taken into account and an original way to extract the mean information from the reduced SVD of a matrix is also considered. Furthermore, the results are produced with linear computational cost and sublinear memory requirements with respect to the size of the data. Finally, experimental results are included, showing the tracking performance and some comparisons between the batch and our incremental computation of the SVD with mean information.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129028916","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 : 2005-11-01DOI: 10.1142/9789812834461_0014
Jordi Gonzàlez, Javier Varona, F. X. Roca, Juan José Villanueva
In this paper, we address the analysis of human actions by comparing different performances of the same action executed by different actors. Specifically, we present a comparison procedure applied to the walking action, but the scheme can be applied to other different actions, such as bending, running, etc. To achieve fair comparison results, we define a novel human body model based on joint angles, which maximizes the differences between human postures and, moreover, reflects the anatomical structure of human beings. Subsequently, a human action space, called aSpace, is built in order to represent each performance (i.e., each predefined sequence of postures) as a parametric manifold. The final human action representation is called p-action, which is based on the most characteristic human body postures found during several walking performances. These postures are found automatically by means of a predefined distance function, and they are called key-frames. By using key-frames, we synchronize any performance with respect to the p-action. Furthermore, by considering an arc length parameterization, independence from the speed at which performances are played is attained. As a result, the style of human walking can be successfully analysed by establishing the differences of the joints between female and male walkers.
{"title":"A Comparison Framework for walking performances using aSpaces","authors":"Jordi Gonzàlez, Javier Varona, F. X. Roca, Juan José Villanueva","doi":"10.1142/9789812834461_0014","DOIUrl":"https://doi.org/10.1142/9789812834461_0014","url":null,"abstract":"In this paper, we address the analysis of human actions by comparing different performances of the same action executed by different actors. Specifically, we present a comparison procedure applied to the walking action, but the scheme can be applied to other different actions, such as bending, running, etc. To achieve fair comparison results, we define a novel human body model based on joint angles, which maximizes the differences between human postures and, moreover, reflects the anatomical structure of human beings. Subsequently, a human action space, called aSpace, is built in order to represent each performance (i.e., each predefined sequence of postures) as a parametric manifold. The final human action representation is called p-action, which is based on the most characteristic human body postures found during several walking performances. These postures are found automatically by means of a predefined distance function, and they are called key-frames. By using key-frames, we synchronize any performance with respect to the p-action. Furthermore, by considering an arc length parameterization, independence from the speed at which performances are played is attained. As a result, the style of human walking can be successfully analysed by establishing the differences of the joints between female and male walkers.","PeriodicalId":181042,"journal":{"name":"Progress in Computer Vision and Image Analysis","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127008071","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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