Contour reconstruction of three-dimensional spiral CT damage image

In order to improve the diagnosis and analysis ability of 3D spiral CT and to reconstruct the contour of 3D spiral CT damage image,a contour reconstruction method based on sharpening template enhancement for 3D spiral CT damage image is proposed.This method uses the active contour LasSO model to extract the contour feature of the 3D spiral CT damage image and enhances the information by sharpening the template en.hancement technique and makes the noise separation of the 3D spiral CT damage image.The spiral CT image was procesed with ENT,and the statistical shape model of 3D spiral CT damage image was established.The.gradient algorithm is used to decompose the feature to realize the analysis and reconstruction of the contour feature of the 3D spiral CT damage image,so as to improve the adaptive feature matching ability and the ability to locate the abnormal feature points.The simulation results show that in the 3D spiral CT damage image contour reconstruction,the proposed method performs well in the feature matching of the output pixels,shortens the contour reconstruction time by 20/ms,and provides a strong ability to express the image information.The normalized reconstruction error of CES is 30%,which improves the recognition ability of 3D spiral CT damage image,and increases the signal-to noise ratio of peak output by 40 dB over other methods.

Study on Detection and Recognition of Speed Signs

Speed sign detection and recognition are the important part of the driving assistant systems.Combining gradient-based random Hough transform with BP network,a method is proposed to detect and recognize speed signs.Firstly,the gradient-based random Hough transform is used to detect and locate the speed signs.Then four contour features of the 'digit' inside the speed signs are extracted and recognized using the method of BP network.The results show that this method can detect and recognize the speed signs accurately and efficiently.