Due to the curve of the coronary artery and the overlap, cross between its branches, some of its
information is lost in the 3D-2D imaging process, which may leads to the inaccuracy in reconstructing
three-dimensional vascular tree structure from angiographic images. In this paper, a new
three-dimensional reconstruction method using overlap detection for 3-D projection is proposed to
improve this problem, and experiments proves that the method can raise the accuracy of the
Texture classification is very important in remote sensing images, X-ray photos, cell image interpretation and
processing, and is also the active research areas of computer vision, image processing, image analysis, image retrieval,
and so on. As to spatial domain image, texture analysis can use statistical methods to calculate the texture feature vector.
In this paper, we use the gray level co-occurrence matrix and Gabor filter feature vector to calculate the feature vector.
For the feature vector classification under normal circumstances we can use Bayesian method, KNN method, BP neural
network. In this paper, we use a statistical classification method which is based on SVM method to classify images.
Image classification generally includes image preprocessing, image feature extraction, image feature selection and
image classification in four steps. In this paper, we use a gray-scale image, by calculating the image gray level cooccurrence
matrix and Gabor filtering method to get feature extraction, and then use SVM to training and classification.
From the test results, it shows that the SVM method is the better way to solve the problem of texture features for
image classification and it shows strong adaptability and robustness for image classification.
This paper presents a method to segment moving human bodies. A self-adaptive background model is used to update the background image(so-called reference image). By calculating the Euclidean distance of corresponding points in the current and background image, we can check out the foreground objects. And the shadow can be detected and removed according to the characteristics of the shadow regions shown in HSV space. Finally, target tracking is implemented by calculating the relativity of color histogram between the moving areas in two succeeding images.