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In the present paper, we propose a method for reconstructing the surfaces of objects from stereovision data. Both the fitness of stereo data to surfaces and interrelation between the surfaces are defined in the framework of a three-dimensional (3D) Markov random field (MRF) model. The surface reconstruction is accomplished by searching for the most likely state of the MRF model. An experimental result...
In this paper, we derive a data association algorithm for object tracking in a maximum a posteriori framework: the output of the algorithm is the sequence of measurement-to-target associations with maximum a posteriori probability. We model the object motion as a Markov process, and solve this otherwise combinatorially complex problem efficiently by applying the Viterbi algorithm. A method for combining...
Segmentation of videos into layers of foreground objects and background has many important applications, such as video compression, human computer interaction, and motion analysis. Most existing methods work on image pixels or color segmentations which are computation expensive. Some methods require extensive manual input, static cameras, and/or rigid scenes. In this paper we propose a fully automatic...
In this paper, we present an automatic system designed for detect the presence of split defects in sheet-metal forming processes. The image acquisition system includes basically a CCD progressive camera and a diffuse illumination system mounted on the end-effector of a 6-dof robot. The inspection-robot displaces the image acquisition system over the pieces proceeding from the sheet-metal forming line...
We propose an object detection method using particle filters. Our approach estimates the probability of object presence in the current image given the history of observations up to current time. To do so, object presence is modelled by a two-state Markov chain, and the problem is translated into sequential Bayesian estimation which can be solved by particle filters. The observation density, required...
Images obtained with catadioptric sensors contain significant deformations which prevent the direct use of classical image treatments. Thus, Markov random fields (MRF) whose usefulness is now obvious for projective image processing, cannot be used directly on catadioptric images because of the inadequacy of the neighborhood. In this paper, we propose to define a new neighborhood for MRF by using the...
This paper presents Markov random fields (MRFs) to segment strokes of Chinese characters. The distortions caused by the thinning process make the thinning-based stroke segmentation difficult to extract continuous strokes and handle the ambiguous intersection regions. The MRFs reflect the local statistical dependencies at neighboring sites of the stroke skeleton, where the likelihood clique potential...
Tagging and tracking protein molecules with the help of laser scanning confocal microscope (LSCM) is a key to better understanding of proteomics in diverse aspects. One challenge of tracking multiple green fluorescent protein (GFP) clusters is how to deal with the interaction between multiple objects, namely splitting and merging. In this paper, we propose a framework to track multiple GFP clusters...
This paper integrates Markov random fields (MRFs) with type-2 fuzzy sets (T2 FSs) referred to as T2 FMRFs, which can handle the fuzziness of the labeling space as well as the randomness of observations within the unified framework. Because fuzzy and random uncertainties exist in many computer vision problems, we extend the maximum a posteriori (MAP) criterion for the best labeling configuration by...
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