Conference Paper

# A variational framework for image segmentation combining motion estimation and shape regularization

Dept. of Comput. Sci., Univ. of California, Los Angeles, CA, USA;

DOI: 10.1109/CVPR.2003.1211337 Conference: Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, Volume: 1 Source: DBLP

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**ABSTRACT:**Model-free tracking is important for solving tasks such as moving-object tracking and action recognition in cases where no prior object knowledge is available. For this purpose, we extend the concept of spatially synchronous dynamics in spin-lattice models to the spatiotemporal domain to track segments within an image sequence. The method is related to synchronization processes in neural networks and based on superparamagnetic clustering of data. Spin interactions result in the formation of clusters of correlated spins, providing an automatic labeling of corresponding image regions. The algorithm obeys detailed balance. This is an important property as it allows for consistent spin-transfer across subsequent frames, which can be used for segment tracking. Therefore, in the tracking process the correct equilibrium will always be found, which is an important advance as compared with other more heuristic tracking procedures. In the case of long image sequences, i.e., movies, the algorithm is augmented with a feedback mechanism, further stabilizing segment tracking.Sensors 01/2009; · 2.05 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**Geometric shapes embedded in 2D or 3D images often have boundaries with both high and low curvature regions. These boundaries of varying curvature can be efficiently captured by adaptive grids such as quadtrees and octrees. Using these trees, we propose to store sample values at the centers of the tree cells in order to simplify the tree data structure, and to take advantage of the image pyramid. The difficulty with using a cell-centered tree approach is the interpolation of the values sampled at the cell centers. To solve this problem, we first restrict the tree refinement and coarsening rules so that only a small number of local connectivity types are produced. For these connectivity types, we can precompute the weights for a continuous interpolation. Using this interpolation, we show that region-based image segmentation of 2D and 3D images can be performed efficiently.Proc SPIE 02/2009; -
##### Article: AERIAL AND SATELLITE IMAGE SEGMENTATION BASED ON SHAPE CONSTRAINED GEODESIC ACTIVE CONTOURS

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**ABSTRACT:**In this paper, we propose a novel mathematical model to address segmentation from an aerial and satellite imagery. Segmentation is performed through the consistent recovery of the zero iso-surfaces of a level set function towards image's foreground (desired object for extraction) and background discrimination. The level set method is embedded in a geodesic active contours variational formulation. Geodesic active contours technique is an advancement of the classical snakes and active contours that can handle the limitation to change their topology. The proposed mathematical model consists of a functional with two basic energies; one that forms a region-based energetic module for the evolving interface in the level set space and an another one that takes into account the a priori knowledge of the geometry of the desired for extraction objects. With such a functional shape information is embedded into the level set based segmentation scheme. Shape information can cope with missing or misleading information in the input images due to noise, clutter and occlusion. The shape priors were developed to segment objects of familiar shape in a given image. Promising results demonstrate the potentials of our approach, like in cases where the desired object for extraction were buildings.01/2009;

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