Accurate Feature Matching In Complex Environments And Its Applications

One basic problem in computer vision is image feature matching, which can be used widely in object recognition and analysis, 3D reconstruction, pattern recognition, robot vision navigation, medical image analysis and many other fields. But in the practical application, as the image conditions vary, which all can cause grey scale distortion and geometric deformation, these factors bring certain difficulties to image matching. In order to achieve perfect effect, scholars both at home and abroad devote themselves to researching the image matching algorithm, tring to find one algorithm which has high matching precision, strong robustness and fine real-time.In this article, the image feature matching problem is discussed and regarding the feature points matching problems the detail research has been done, the main achievements are as follows:1)A new algorithm of feature matching based on balanced probabilistic model is proposed in a probabilistic framework to solve the problem that feature point matching precision drops under complicated conditions. Firstly using Random Walks with Restart(RWR) the likelihood values of each candidate matching point sets are estimated, which are used as matching probability between a set of candidate points and other ones in RWR model. And a balancing analysis and treatment to the adjacency matrix of RWR is taken, then a balancing matrix will be obtained, which can improve the matching precision considerablly. Last an optimal matching set can be got by imposing a sequential method with mapping constraints in a simple way.2)Non-rigid cell contour tracking has been realized on the basis of feature matching algorithm which is based on balancing probabilistic model. Firstly the position tracking is accomplished using the feature matching algorithm which is based on balancing probabilistic model, then an automatic calibration to object is presented with the results of feature matching and some simple information about gray and grads. Last a precise object contour tracking under noisy conditions is presented accuately combined with Growcut, a kind of mage segmentation method.3)Profile face tracking accurately in the condition of knowing the frontal face can be achieved by applying the feature matching algorithm which is based on balancing probabilistic model, and it improves the accuracy and real-timing. This article applies the image feature matching algorithm to KRR(Kernel Ridge Regression) then calibration points are obtained as initial information. At the same time, a new algorithm is proposed --CAAM(Conditional Active Appearance Model) inverse compositional algorithm. Assuming the calibration points of frontal face is known, transforming the corresponding relationship between the shape model and basic shape in the original AAM into the one between profile face feature points and frontal face feature points. The model parameters can be optimized iteratively according to establishing shape model and inverse compositional algorithm, at last the precise profile face calibration points can be obtained.