The Experts below are selected from a list of 4755 Experts worldwide ranked by ideXlab platform
Kevin Novins - One of the best experts on this subject based on the ideXlab platform.
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Motion Segmentation in long image sequences
Proceedings of the 11th British Machine Vision Conference, 2000Co-Authors: Steven Mills, Kevin NovinsAbstract:Long image sequences provide a wealth of information, which means that a compact representation is needed to efficiently process them. In this paper a novel representation for Motion Segmentation in long image sequences is presented. This representation – the feature interval graph – measures the pairwise rigidity of features in the scene. The feature interval graph is re- cursively computed,making it a compact representation, and uses an interval model of uncertainty. The feature interval graph forms the basis for new al- gorithms for Motion Segmentation and occlusion analysis. Results of these algorithms are presented on synthetic and laboratory scenes.
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BMVC - Motion Segmentation in Long Image Sequences
Procedings of the British Machine Vision Conference 2000, 2000Co-Authors: Steven Mills, Kevin NovinsAbstract:Long image sequences provide a wealth of information, which means that a compact representation is needed to efficiently process them. In this paper a novel representation for Motion Segmentation in long image sequences is presented. This representation ‐ the feature interval graph ‐ measures the pairwise rigidity of features in the scene. The feature interval graph is recursively computed, making it a compact representation, and uses an interval model of uncertainty. The feature interval graph forms the basis for new algorithms for Motion Segmentation and occlusion analysis. Results of these algorithms are presented on synthetic and laboratory scenes. The analysis of long image sequences is a growing area of research in computer vision. Long sequence analysis is important for visual surveillance, mobile robotics, and other areas where a dynamic scene is observed over a long period of time. Long image sequences provide a wealth of information, but this raises the problem of efficient representation. It is not feasible to store an entire sequence, and so a compact representation is needed which can be efficiently computed, and does not grow with the length of the sequence. In this paper the particular problem of Motion Segmentation in long image sequences is addressed. In Motion Segmentation the goal is to cluster the scene, or features extracted from it, into regions having a common Motion. The clusters correspond to independently moving objects in the scene and so are useful for tracking and navigation. Section 2 presents some previous approaches to the task of Motion Segmentation in long image sequences. These approaches make use of the Kalman filter [2], which provides a compact and robust representation of information gathered from the image sequence. The Kalman filter, however, relies on a Gaussian model of uncertainty, and assumes that the variances of these distributions are known. In Section 3 a new representation for Motion Segmentation is presented. This representation, the feature interval graph, measures pairwise rigidity information directly and shares many of the advantages of the Kalman filter, but requires less a priori knowledge. The feature interval graph is then used to develop new algorithms for Motion Segmentation and the analysis of partial occlusions in the scene in Sections 4 and 5. An analysis of these algorithms is given in Section 6, followed by some concluding remarks.
Steven Mills - One of the best experts on this subject based on the ideXlab platform.
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Motion Segmentation in long image sequences
Proceedings of the 11th British Machine Vision Conference, 2000Co-Authors: Steven Mills, Kevin NovinsAbstract:Long image sequences provide a wealth of information, which means that a compact representation is needed to efficiently process them. In this paper a novel representation for Motion Segmentation in long image sequences is presented. This representation – the feature interval graph – measures the pairwise rigidity of features in the scene. The feature interval graph is re- cursively computed,making it a compact representation, and uses an interval model of uncertainty. The feature interval graph forms the basis for new al- gorithms for Motion Segmentation and occlusion analysis. Results of these algorithms are presented on synthetic and laboratory scenes.
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BMVC - Motion Segmentation in Long Image Sequences
Procedings of the British Machine Vision Conference 2000, 2000Co-Authors: Steven Mills, Kevin NovinsAbstract:Long image sequences provide a wealth of information, which means that a compact representation is needed to efficiently process them. In this paper a novel representation for Motion Segmentation in long image sequences is presented. This representation ‐ the feature interval graph ‐ measures the pairwise rigidity of features in the scene. The feature interval graph is recursively computed, making it a compact representation, and uses an interval model of uncertainty. The feature interval graph forms the basis for new algorithms for Motion Segmentation and occlusion analysis. Results of these algorithms are presented on synthetic and laboratory scenes. The analysis of long image sequences is a growing area of research in computer vision. Long sequence analysis is important for visual surveillance, mobile robotics, and other areas where a dynamic scene is observed over a long period of time. Long image sequences provide a wealth of information, but this raises the problem of efficient representation. It is not feasible to store an entire sequence, and so a compact representation is needed which can be efficiently computed, and does not grow with the length of the sequence. In this paper the particular problem of Motion Segmentation in long image sequences is addressed. In Motion Segmentation the goal is to cluster the scene, or features extracted from it, into regions having a common Motion. The clusters correspond to independently moving objects in the scene and so are useful for tracking and navigation. Section 2 presents some previous approaches to the task of Motion Segmentation in long image sequences. These approaches make use of the Kalman filter [2], which provides a compact and robust representation of information gathered from the image sequence. The Kalman filter, however, relies on a Gaussian model of uncertainty, and assumes that the variances of these distributions are known. In Section 3 a new representation for Motion Segmentation is presented. This representation, the feature interval graph, measures pairwise rigidity information directly and shares many of the advantages of the Kalman filter, but requires less a priori knowledge. The feature interval graph is then used to develop new algorithms for Motion Segmentation and the analysis of partial occlusions in the scene in Sections 4 and 5. An analysis of these algorithms is given in Section 6, followed by some concluding remarks.
C V Jawahar - One of the best experts on this subject based on the ideXlab platform.
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ICVGIP - Realtime Motion Segmentation based multibody visual SLAM
Proceedings of the Seventh Indian Conference on Computer Vision Graphics and Image Processing - ICVGIP '10, 2010Co-Authors: Abhijit Kundu, K. Madhava Krishna, C V JawaharAbstract:In this paper, we present a practical vision based Simultaneous Localization and Mapping (SLAM) system for a highly dynamic environment. We adopt a multibody Structure from Motion (SfM) approach, which is the generalization of classical SfM to dynamic scenes with multiple rigidly moving objects. The proposed framework of multibody visual SLAM allows choosing between full 3D reconstruction or simply tracking of the moving objects, which adds flexibility to the system, for scenes containing non-rigid objects or objects having insufficient features for reconstruction. The solution demands a Motion Segmentation framework that can segment feature points belonging to different Motions and maintain the Segmentation with time. We propose a realtime incremental Motion Segmentation algorithm for this purpose. The Motion Segmentation is robust and is capable of segmenting difficult degenerate Motions, where the moving objects is followed by a moving camera in the same direction. This robustness is attributed to the use of efficient geometric constraints and a probability framework which propagates the uncertainty in the system. The Motion Segmentation module is tightly coupled with feature tracking and visual SLAM, by exploring various feed-backs in between these modules. The integrated system can simultaneously perform realtime visual SLAM and tracking of multiple moving objects using only a single monocular camera.
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Realtime Motion Segmentation based multibody visual SLAM
Proceedings of the Seventh Indian Conference on Computer Vision Graphics and Image Processing - ICVGIP '10, 2010Co-Authors: Abhijit Kundu, K. Madhava Krishna, C V JawaharAbstract:In this paper, we present a practical vision based Simultane- ous Localization and Mapping (SLAM) system for a highly dynamic environment. We adopt a multibody Structure from Motion (SfM) approach, which is the generalization of classical SfM to dynamic scenes with multiple rigidly mov- ing objects. The proposed framework of multibody visual SLAM allows choosing between full 3D reconstruction or simply tracking of the moving objects, which adds flexibil- ity to the system, for scenes containing non-rigid objects or objects having insufficient features for reconstruction. The solution demands a Motion Segmentation framework that can segment feature points belonging to different Motions and maintain the Segmentation with time. We propose a re- altime incremental Motion Segmentation algorithm for this purpose. The Motion Segmentation is robust and is capa- ble of segmenting difficult degenerate Motions, where the moving objects is followed by a moving camera in the same direction. This robustness is attributed to the use of ef- ficient geometric constraints and a probability framework which propagates the uncertainty in the system. The mo- tion Segmentation module is tightly coupled with feature tracking and visual SLAM, by exploring various feed-backs in between these modules. The integrated system can si- multaneously perform realtime visual SLAM and tracking of multiple moving objects using only a single monocular camera.
Janusz Konrad - One of the best experts on this subject based on the ideXlab platform.
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multiple Motion Segmentation with level sets
IEEE Transactions on Image Processing, 2003Co-Authors: Abdolreza Mansouri, Janusz KonradAbstract:Segmentation of Motion in an image sequence is one of the most challenging problems in image processing, while at the same time one that finds numerous applications. To date, a wealth of approaches to Motion Segmentation have been proposed. Many of them suffer from the local nature of the models used. Global models, such as those based on Markov random fields, perform, in general, better. In this paper, we propose a new approach to Motion Segmentation that is based on a global model. The novelty of the approach is twofold. First, inspired by recent work of other researchers we formulate the problem as that of region competition, but we solve it using the level set methodology. The key features of a level set representation, as compared to active contours, often used in this context, are its ability to handle variations in the topology of the Segmentation and its numerical stability. The second novelty of the paper is the formulation in which, unlike in many other Motion Segmentation algorithms, we do not use intensity boundaries as an accessory; the Segmentation is purely based on Motion. This permits accurate estimation of Motion boundaries of an object even when its intensity boundaries are hardly visible. Since occasionally intensity boundaries may prove beneficial, we extend the formulation to account for the coincidence of Motion and intensity boundaries. In addition, we generalize the approach to multiple Motions. We discuss possible discretizations of the evolution (PDE) equations and we give details of an initialization scheme so that the results could be duplicated. We show numerous experimental results for various formulations on natural images with either synthetic or natural Motion.
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Multiple Motion Segmentation with level sets
Image and Video Communications and Processing 2000, 2000Co-Authors: A.-r. Mansouri, Bounlith Sirivong, Janusz KonradAbstract:Motion Segmentation of an image sequence belongs to the most difficult and important problems in video processing and compression, and in computer vision. In this paper, we consider the problem of segmenting an image into multiple regions possibly undergoing different Motions. To this end we use level sets of functions evolving according to certain partial differential equations. Contrary to numerous other Motion Segmentation algorithms based on level sets, we compute accurate Motion boundaries without relying on intensity boundaries as an accessory. This will be illustrated on examples where intensity boundaries are hardly visible and yet Motion boundaries are accurately identified. The main benefit of the level set representation is in its ability to handle variations in the topology of the level sets. As a result, it is only necessary to know the total number of distinct Motion classes and their parameters. We describe an automatic initialization procedure that is based on feature point correspondences and K-means clustering in a 6-parameter space of affine parameters. We illustrate the performance of the proposed algorithm on real images with both real and synthetic Motion.
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ICIP (2) - Motion Segmentation with level sets
Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 1999Co-Authors: A.-r. Mansouri, Janusz KonradAbstract:Motion Segmentation is an important problem in video processing and compression, and in computer vision. It is usually performed by either first estimating a field of Motion parameters and then segmenting it, or by applying joint Motion estimation and Segmentation. Motion Segmentation methods often constrain the set of possible solutions by forcing Motion discontinuities to coincide with intensity discontinuities. In this paper, we propose an iterative method for joint Motion estimation and Segmentation that is based on level sets. The Motion within individual segments is parametric and the method does not use the intensity discontinuity constraint, but is shown to be accurate for images with both synthetic and natural Motion compliant with the assumed Motion models.
Zhuwen Li - One of the best experts on this subject based on the ideXlab platform.
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3D Rigid Motion Segmentation with Mixed and Unknown Number of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019Co-Authors: Xun Xu, Loong-fah Cheong, Zhuwen LiAbstract:Many real-world video sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for Motion Segmentation on video sequences would lead to difficulty. Even when we are confronted with a general scene-Motion, the fundamental matrix approach as a model for Motion Segmentation still suffers from several defects, which we discuss in this paper. The full potential of the fundamental matrix approach could only be realized if we judiciously harness information from the simpler homography model. From these considerations, we propose a multi-model spectral clustering framework that synergistically combines multiple models (homography and fundamental matrix) together. We show that the performance can be substantially improved in this way. For general Motion Segmentation tasks, the number of independently moving objects is often unknown a priori and needs to be estimated from the observations. This is referred to as model selection and it is essentially still an open research problem. In this work, we propose a set of model selection criteria balancing data fidelity and model complexity. We perform extensive testing on existing Motion Segmentation datasets with both Segmentation and model selection tasks, achieving state-of-the-art performance on all of them; we also put forth a more realistic and challenging dataset adapted from the KITTI benchmark, containing real-world effects such as strong perspectives and strong forward translations not seen in the traditional datasets.
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CVPR - Motion Segmentation by Exploiting Complementary Geometric Models
2018 IEEE CVF Conference on Computer Vision and Pattern Recognition, 2018Co-Authors: Xun Xu, Loong-fah Cheong, Zhuwen LiAbstract:Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for Motion Segmentation would lead to difficulty. Even when we are confronted with a general scene-Motion, the fundamental matrix approach as a model for Motion Segmentation still suffers from several defects, which we discuss in this paper. The full potential of the fundamental matrix approach could only be realized if we judiciously harness information from the simpler homography model. From these considerations, we propose a multi-view spectral clustering framework that synergistically combines multiple models together. We show that the performance can be substantially improved in this way. We perform extensive testing on existing Motion Segmentation datasets, achieving state-of-the-art performance on all of them; we also put forth a more realistic and challenging dataset adapted from the KITTI benchmark, containing real-world effects such as strong perspectives and strong forward translations not seen in the traditional datasets.
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Motion Segmentation by Exploiting Complementary Geometric Models
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Xun Xu, Loong-fah Cheong, Zhuwen LiAbstract:Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for Motion Segmentation would lead to difficulty. Even when we are confronted with a general scene-Motion, the fundamental matrix approach as a model for Motion Segmentation still suffers from several defects, which we discuss in this paper. The full potential of the fundamental matrix approach could only be realized if we judiciously harness information from the simpler homography model. From these considerations, we propose a multi-view spectral clustering framework that synergistically combines multiple models together. We show that the performance can be substantially improved in this way. We perform extensive testing on existing Motion Segmentation datasets, achieving state-of-the-art performance on all of them; we also put forth a more realistic and challenging dataset adapted from the KITTI benchmark, containing real-world effects such as strong perspectives and strong forward translations not seen in the traditional datasets.
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perspective Motion Segmentation via collaborative clustering
International Conference on Computer Vision, 2013Co-Authors: Zhuwen Li, Loong-fah Cheong, Steven Zhiying ZhouAbstract:This paper addresses real-world challenges in the Motion Segmentation problem, including perspective effects, missing data, and unknown number of Motions. It first formulates the 3-D Motion Segmentation from two perspective views as a subspace clustering problem, utilizing the epipolar constraint of an image pair. It then combines the point correspondence information across multiple image frames via a collaborative clustering step, in which tight integration is achieved via a mixed norm optimization scheme. For model selection, we propose an over-segment and merge approach, where the merging step is based on the property of the ell_1-norm of the mutual sparse representation of two over-segmented groups. The resulting algorithm can deal with incomplete trajectories and perspective effects substantially better than state-of-the-art two-frame and multi-frame methods. Experiments on a 62-clip dataset show the significant superiority of the proposed idea in both Segmentation accuracy and model selection.