40,541 research outputs found

    Region and graph-based motion segmentation

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    Indexado ISIThis paper describes an approach for integrating motion estimation and region clustering techniques with the purpose of obtaining precise multiple motion segmentation. Motivated by the good results obtained in static segmentation we propose a hybrid approach where motion segmentation is achieved within a region-based clustering approach taken the initial result of a spatial pre-segmentation and extended to include motion information. Motion vectors are first estimated with a multiscale variational method applied directly over the input images and then refined by incorporating segmentation results into a region-based warping scheme. The complete algorithm facilitates obtaining spatially continuous segmentation maps which are closely related to actual object boundaries. A comparative study is made with some of the best known motion segmentation algorithms

    Region-based spatial and temporal image segmentation

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    This work discusses region-based representations for image and video sequence segmentation. It presents effective image segmentation techniques and demonstrates how these techniques may be integrated into algorithms that solve some of the motion segmentation problems. The region-based representation offers a way to perform a first level of abstraction and to reduce the number of elements to process with respect to the classical pixel-based representation. Motion segmentation is a fundamental technique for the analysis and the understanding of image sequences of real scenes. Motion segmentation 'describes' the sequence as sets of pixels moving coherently across one sequence with associated motions. This description is essential to the identification of the objects in the scene and to a more efficient manipulation of video sequences. This thesis presents a hybrid framework based on the combination of spatial and motion information for the segmentation of moving objects in image sequences accordingly with their motion. We formulate the problem as graph labelling over a region moving graph where nodes correspond coherently to moving atomic regions. This is a flexible high-level representation which individualizes moving independent objects. Starting from an over-segmentation of the image, the objects are formed by merging neighbouring regions together based on their mutual spatial and temporal similarity, taking spatial and motion information into account with the emphasis being on the second. Final segmentation is obtained by a spectral-based graph cuts approach. The initial phase for the moving object segmentation aims to reduce image noise without destroying the topological structure of the objects by anisotropic bilateral filtering. An initial spatial partition into a set of homogeneous regions is obtained by the watershed transform. Motion vector of each region is estimated by a variational approach. Next a region moving graph is constructed by a combination of normalized similarity between regions where mean intensity of the regions, gradient magnitude between regions, and motion information of the regions are considered. The motion similarity measure among regions is based on human perceptual characteristics. Finally, a spectral-based graph cut approach clusters and labels each moving region. The motion segmentation approach is based on a static image segmentation method proposed by the author of this dissertation. The main idea is to use atomic regions to guide a segmentation using the intensity and the gradient information through a similarity graph-based approach. This method produces simpler segmentations, less over-segmented and compares favourably with the state-of-the-art methods. To evaluate the segmentation results a new evaluation metric is proposed, which takes into attention the way humans perceive visual information. By incorporating spatial and motion information simultaneously in a region-based framework, we can visually obtain meaningful segmentation results. Experimental results of the proposed technique performance are given for different image sequences with or without camera motion and for still images. In the last case a comparison with the state-of-the-art approaches is made

    Spatio-temporal clustering of probabilistic region trajectories

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    We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences

    Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters

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    Segmentation of an object from a video is a challenging task in multimedia applications. Depending on the application, automatic or interactive methods are desired; however, regardless of the application type, efficient computation of video object segmentation is crucial for time-critical applications; specifically, mobile and interactive applications require near real-time efficiencies. In this paper, we address the problem of video segmentation from the perspective of efficiency. We initially redefine the problem of video object segmentation as the propagation of MRF energies along the temporal domain. For this purpose, a novel and efficient method is proposed to propagate MRF energies throughout the frames via bilateral filters without using any global texture, color or shape model. Recently presented bi-exponential filter is utilized for efficiency, whereas a novel technique is also developed to dynamically solve graph-cuts for varying, non-lattice graphs in general linear filtering scenario. These improvements are experimented for both automatic and interactive video segmentation scenarios. Moreover, in addition to the efficiency, segmentation quality is also tested both quantitatively and qualitatively. Indeed, for some challenging examples, significant time efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014

    Point-wise mutual information-based video segmentation with high temporal consistency

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    In this paper, we tackle the problem of temporally consistent boundary detection and hierarchical segmentation in videos. While finding the best high-level reasoning of region assignments in videos is the focus of much recent research, temporal consistency in boundary detection has so far only rarely been tackled. We argue that temporally consistent boundaries are a key component to temporally consistent region assignment. The proposed method is based on the point-wise mutual information (PMI) of spatio-temporal voxels. Temporal consistency is established by an evaluation of PMI-based point affinities in the spectral domain over space and time. Thus, the proposed method is independent of any optical flow computation or previously learned motion models. The proposed low-level video segmentation method outperforms the learning-based state of the art in terms of standard region metrics

    Geodesic Distance Histogram Feature for Video Segmentation

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    This paper proposes a geodesic-distance-based feature that encodes global information for improved video segmentation algorithms. The feature is a joint histogram of intensity and geodesic distances, where the geodesic distances are computed as the shortest paths between superpixels via their boundaries. We also incorporate adaptive voting weights and spatial pyramid configurations to include spatial information into the geodesic histogram feature and show that this further improves results. The feature is generic and can be used as part of various algorithms. In experiments, we test the geodesic histogram feature by incorporating it into two existing video segmentation frameworks. This leads to significantly better performance in 3D video segmentation benchmarks on two datasets
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