Research On Probabilistic Graphical Model Oriented Image Processing

Information uncertainty in image processing has become a new hot-spot as achallenging research. Probabilistic graphical model provides an important means forresolving the uncertainty of intelligent information field. This paper made an intensivestudy on local features of an image, segmentation for the in-homogenous images andvisual tracking based upon probabilistic graphical model.Robustness and discrimination are the core problems of different kinds of localfeature descriptors, but robustness and discrimination are contradictory. Namely, for afeature descriptor with robustness, its power of discriminating the content of an image isweak. Correspondingly, the one with discrimination has worse robustness. Hence, tobalance the trade-off between robustness and discrimination, the research based on localfeature descriptors has very important value in mage processing.Image with intensity in-homogeneity consists of multiple gray levels. Althoughclassical edge-based models can segment the image with intensity in-homogeneity, theyare sensitive for segmenting the objects with weak or discontinuous boundary and noise;region-based models have been successfully used in binary phase segmentation with theassumption that each image region is statistically homogeneous. However, region-basedmodels do not work well for the image with intensity in-homogeneity. Hence, theresearch of region-based models about integrating local feature information based onprobabilistic graphical model has very important academic and application role for thein-homogeneous image.Appearance variations of the target object will cause the drifting or missingproblem of the target object during tracking. To cope with the problem, most existingonline methods utilize fixed prior model to update appearance model. These methodsare either too generic to drift to the similar objects or too restrictive to fail in dramaticchanges. Therefore, a promising research direction is to construct an adaptive priormodel which can adapt to changes incrementally during tracking.The main contributions of this paper are summarized as follows:First of all, to deal with shape segmentation of the object in complicated scenes, anovel method based on MRF model with implicit shape prior was proposed. First, theobject was segmented using the initial contour. Then, an implicit shape model with levelset signed distance function was built to improve the accuracy of object segmentation, and a MRF energy function with the constraint of the existing shape prior wasconstructed. The optimal value of energy function could be found by graph cut method.The precise segmentation of object was obtained by applying the shape alignment andmax-flow method to evolve the initial contour. Experimental results show that theproposed method can better cope with the clutter and noise, as well as partial occlusionsand affine transformation of the shape. Thus the robust stability of segmentation resultis improved.Secondly, to solve effectively the difficult and ineffective segmentation for thein-homogenous images, a novel fast method based on the local region active contourmodel was proposed. A new energy function was defined by combining kernel functionand cut metric. Utilization of the kernel function was favor of computing thein-homogenous distribution of local regions effectively. On the other hand, betterapproximation of the curve length by cut metric could help the contours to evolve intothe object boundary quickly. In addition, in the evolving process of contours, amax-flow method was adopted, which avoided an expensive computational level setmethod. Experimental results using synthetic and real images show that the proposedmethod can effectively segment objects with the weak boundary in in-homogenousimages, as well as the complex structure objects with multi-gray levels. At the sametime, the method is robust to noise and the initial contours.Thirdly, to cope with appearance variations of the target object during visualtracking, a nonparametric Bayesian multi-modal appearance model for learning overtime was proposed. First, by taking the temporal Dirichlet process as prior distribution,the proposed model separated target samples previously estimated into several clusters.Each cluster represented a certain type of the target appearance, which was modeled asdiscriminative classifiers. Then, to balance the trade-off between the classification errorof appearance model and the cost for splitting the clusters, the multi-modal appearancemodel was automatically learned by the use of Bayesian posterior inference. Finally,based on the Noisy-OR model, a greedy algorithm was used to discriminate the targetobject by combining the outputs of appearance classifiers. The simulation results showthat the proposed method can robustly track an object under rapid appearance changesand improve the tracking performance.Fourthly, to effectively solve the drifting problem of a varying target object duringtarget tracking, an adaptive prior appearance model was presented. First, the methodcombined hierarchical dirichlet process evolutionary clustering model and online boosting learning into a coherent framework. By taking the hierarchical dirichletprocess as prior distribution, the prior appearance knowledge could adapt to changeover time. On the other hand, appearance model of each moment was smoothlyconstrained by the mixture proportion of a type of appearance cluster. Then, to balancethe classification error of appearance model and the cost for splitting the clusters, themulti-modal appearance model was automatically learned by the use of Bayesianposterior inference. Finally, based on the weighting factor of appearance clusters, thetarget object was discriminated by combining the outputs of appearance classifiers. Thesimulation results show that the learned appearance model can adapt to the appearancevariations of the target object and achieve better tracking results with high accuracy.