Region-based image segmentation is well-addressed by the Chan-Vese (CV) model. However, this approach fails when images are affected by artifacts (outliers) and illumination bias that outweigh the actual image contrast. Here, we introduce a model for segmenting such images. In a single energy functional, we introduce 1) a dynamic artifact class preventing intensity outliers from skewing the segmentation, and 2), in Retinex-fashion, we decompose the image into a piecewise-constant structural part and a smooth bias part. The CV-segmentation terms then only act on the structure, and only in regions not identified as artifacts. The segmentation is parameterized using a phase-field, and efficiently minimized using threshold dynamics. We demonstrate the proposed model on a series of sample images from diverse modalities exhibiting artifacts and/or bias. Our algorithm typically converges within 10-50 iterations and takes fractions of a second on standard equipment to produce meaningful results. We expect our method to be useful for damaged images, and anticipate use in applications where artifacts and bias are actual features of interest, such as lesion detection and bias field correction in medical imaging, e.g., in magnetic resonance imaging (MRI).

It is because of many reasons the trajectory calculated from the theoretical model and the actual trajectory have some error, so the experimental results on the theoretical trajectory must be corrected. In this paper, two degrees of freedom of particle trajectory equations are used to determine the ballistic coefficient. And a SVM Neural Network which has a great learning ability and generalization ability of the extremely small sample is used to adaptive learning the solver deviation of the fit between the trajectory and measured trajectory and amend the ballistic coefficient and modified theoretical trajectory solver results. The test shows that this method has a good precision and stability, and the algorithm can be simple programmed. And it has some value in engineering.

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Bridge recognition algorithm based on straight-line characteristic is proposed in order to automatically recognize bridge from aerial images, which includes the steps of edge detection, straight-line extraction, coarse location for bridge, accurate location for bridge. Meanwhile, realize the fast accurate location for bridge area by modified 8-neighborhood connectivity processing. The experiment result shows the reliability and efficiency of the method proposed in this article.

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