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Research On The Theory Of Bayesian Network And It's Application In Object Detection

Posted on:2005-02-07Degree:DoctorType:Dissertation
Country:ChinaCandidate:R G WangFull Text:PDF
GTID:1118360152455947Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
After an overview on research results about Bayesian networks (BNs) in our country and the developed countries, this thesis researches systematically the knowledge representation, inference methods and explanation mechanism of BNs and makes some extension on them. The BN applications in detection of objects, such as recognition of buildings in aerophotograph images, and detection and location of texts in images, are discussed in detail.The main contents and novel parts of the thesis are as follows:1. The conditional independence of random variables in graphical models is discussed. The basic principle and methods of knowledge representation and inference for BNs are discussed. An inference algorithm is presented using variable elimination based on the extended junction-tree, which determines the sequence of variable elimination by depth-first search of the extended junction-tree and solves the problem of selecting variables for elimination.2. Based on the discussion of existing BN explanation methods, we proposed a new explanation mechanism of BN inference. In this mechanism, the necessity factor and abundance factor are inducted for explaining to what extent the evidences affect. The concept of direction of probabilistic distribution changements is inducted for determining the direction of the evidence effect on inference conclusion as well as detecting the possible conflict phenomenon of evidences. A path of evidence effect on the inference conclusion is generated for explanation by analyzing the network structure quantitatively and qualitatively. An application case shows that the explanation method proposed in the thesis is quite reasonable and effective.3. On the basis of discussing projection properties of buildings, a BN based algorithm of perceptual organization is presented, which can be used to detect objects, such as buildings in an aerophotograph image. This algorithm includes four steps: edge extraction, parallelogram generation, hypothesis generation and hypothesis examination. The information fusion is realized by BN inference, and the self- adaptability of perception is realized by BN learning. The experiment results show the effectiveness of the algorithm.4. A new object-oriented probabilistic graphical model, named object probabilistic model (OPM) is put forward, which can reduce the complexity of knowledge representation and inference of a large-scale Bayesian network. OPM can decompose a BN into many models named classes, each of which has two interface nodes for propagating probabilistic information. OPM makes full use of the conditional independence implicated by the hierarchical structural and reduces the complexity of model construction and knowledge representation. The inference mechanism of OPM is realized by generalizing the variable elimination algorithm of BN. The computing complexity of inference process of OPM can be controlled by adjust the controlling parameters in inference algorithm. Some experiment results of applying OPM to detect and localize texts in an image show that the new model and relevant algorithm are of important theoretic and practical significances.5. Some comparisons are made between Bayesian networks and the of certainty factor models. After discussing the theory foundations of the certainty factor model and its drawbacks, the equivalence of the probabilistic inference equation of a Noisy-OR model, a simplified BN model, and the inference equation of a certainty factor model is argued. The advantages of BNs over the certainty factor models are discussed in knowledge representation and acquisition and their inference.
Keywords/Search Tags:Bayesian network, Intelligence information process, Probabilistic expert system, Image analysis, Object detection
PDF Full Text Request
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