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A novel parametric deformable model of a goal object controlled by shape and appearance priors learned from co-aligned training images is introduced. The shape prior is built in a linear space of vectors of distances to the training boundaries from their common centroid. The appearance prior is modeled with a spatially homogeneous 2nd-order Markov-Gibbs random field (MGRF) of gray levels within each training boundary. Geometric structure of the MGRF and Gibbs potentials are analytically estimated from the training data. To accurately separate goal objects from arbitrary background, the deformable model is evolved by solving an Eikonal partial differential equation with a speed function combining the shape and appearance priors and the current appearance model. The latter represents empirical gray level marginals inside and outside an e...
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5459314
New techniques for unsupervised segmentation of multimodal grayscale images are proposed such that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of grey levels.
Designing parallel versions of sequential algorithms has attracted renewed attention, due to recent hardware advances, including various general-purpose multi-core and many-core processors, as well as special-purpose FPGA implementations. P systems consist of networks of autonomous cells, such that each cell transforms its input signals in accord with its symbol-rewriting rules and feeds the output results into its immediate neighbours. Inherent massive intra- and inter-cell parallelisms make P systems a prospective theoretical testbed for designing efficient parallel and parallel-sequential algorithms. This paper discusses the ability of P systems to implement the symmetric dynamic programming stereo (SDPS) matching algorithm, which explicitly accounts for binocular or monocular visibility of 3D surface points. Given enough cells, the...
This paper presents a new stochastic marked point process for describing images in terms of a finite library of geometric objects. Image analysis based on conventional marked point processes has already produced convincing results but at the expense of parameter tuning, computing time, and model specificity. Our more general multimarked point process has simpler parametric setting, yields notably shorter computing times, and can be applied to a variety of applications. Both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model, and a Jump-Diffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show that the proposed approach has good potential. We conclude with a discussi...
Designing parallel versions of sequential algorithms has attracted renewed attention, due to recent hardware advances, including various general-purpose multi-core, multiple core and many-core processors, as well as special-purpose FPGA implementations. P systems consist of networks of autonomous cells, such that each cell transforms its input signals in accord with symbol-rewriting rules and feeds the output results into its immediate neighbours. Inherent intra- and inter-cell parallelism make the P systems a prospective theoretical testbed for designing parallel algorithms. This paper discusses capabilities of P systems to implement the symmetric dynamic programming algorithm for stereo matching, with due account to binocular or monocular visibility of 3D surface points.
Algorithms incorporating 3D information have proven to be superior to purely 2D approaches in many areas of computer vision including face biometrics and recognition. Still, the range of methods for feature extraction from 3D surfaces is limited. Very popular in 2D image analysis, active contours have been generalized to curved surfaces only recently. Current implementations require a global surface parametrisation. We show that a balloon force cannot be included properly in existing methods, making them unsuitable for applications with noisy data. To overcome this drawback we propose a new algorithm for evolving geodesic active contours on implicit surfaces. We also introduce a new narrowband scheme which results in linear computational complexity. The performance Of Our model is illustrated on various real and synthetic 3D surfaces.
17th international conference on pattern recognition - ICPR2004, August 23-26, 2004, Cambridge, United Kingdom
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