Aiming at the problem of iterative convergence of Markov chain Monte Carlo method, an improved spatially constrained Bayesian network model was proposed and applied in the image segmentation domain based on the Gaussian mixture model with spatial smoothing constraint. Latent Dirichlet Allocation (LDA) probability density model and the parameter mix process of Gauss-Markov theorem were used to achieve parameter smoothing. According to the spatial information transcendental transformation operation, the LDA conformance polynomial distribution was introduced into the context hybrid structure of the pixel to be used to replace the mapping operation in the traditional expectation maximization algorithm. LDA parameters were represented by a closed form, which facilitated to accurately estimate the relative proportion of MAP (Maximum A Posteriori) framework to context mixture structure. The experimental results in terms of PRI (Probabilistic Rand Index), VoI (Variation of Information), GCE (Global Consistency Error) and BDE (Boundary Displacement Error) show that the proposed method has better effect in image segmentation, its robustness is less influenced by Gauss noise compared with JSEG (Joint Systems Engineering Group), CTM (Current Transformation Matrix) and MM (Maximum A Posteriori Probability-Maximum Likelihood).

According to the problem of the traditional Gravitational Search Algorithm (GSA) such as falling into the local minimum point easily, a hybrid algorithm based on Estimation of Distribution (ED) and gravitational search (GSEDA) was proposed. By characterizing the distribution of current solutions found by GSA, ED was used to generate promising solutions based on the constructed probability matrix, thus guiding the search to new solution areas. The proposed GSEDA was able to balance the exploration and exploitation of the search, therefore possessing a better local optima jumping capacity. The experimental results based on the traveling salesman problem indicate that GSEDA performs better than traditional algorithms in terms of solution quality and robustness.

In order to solve the problem of geometric distortion and low efficiency in the process of biased skeleton trim, a new algorithm of biased skeleton trim based on intersecting cortical model was proposed. At first, according to inherent features of skeleton biased branch, definitions of endpoint and junction point were introduced and revised in the algorithm to accurately locate skeleton branch and biased branch. Then, with that information and the iteration number of intersecting cortical model, flameout condition of neurons spreading was set up. Finally, guided by that condition, the biased skeleton branch can be judged fast and trimmed accurately, with the aid of impulse dynamically generated by ignition neurons, which has biological nature of parallel transmission. Compared with conventional methods based on mathematical morphology, the experimental results show that the proposed algorithm has good performance in structural integrity of skeleton, as well as computation speed and anti-noise ability.

In order to solve the semantic conflict of emergency cases understanding in Case-Based Reasoning (CBR), an emergency case ontology model based on CBR was defined on the basis of the extended ABC ontology model according to the analysis of emergency cases, and the key elements of the model were described in detail, such as concepts, relations, axioms and instances. Finally, the model was validated by the analysis on the case of the nuclear crises of the first nuclear power station in Fukushima.

Aiming at the problem that the existing robust cascade postural regression algorithm lacks shape constraint, and has low localization accuracy and unsatisfactory success rate in complex face and occlusion situations, a novel positioning algorithm for pose estimation of facial feature points was proposed to improve the accuracy and success rate. A regional block operation was performed on face feature points to implement shape constraint. To improve the algorithm performance, a regression operation was performed on partial feature point positions to reduce the scale of regression, and the shape index feature was introduced to sampling prior operation. The experimental results show that the proposed algorithm has higher localization accuracy and robustness for complex face and occlusion, and meets the realtime requirement.