Wang Yangping

TL;DR: In this paper, a CUDA-based DICOM medical image dynamic nonlinear window modulation method is proposed, which comprises the steps of (1) reading pixel value information and DicOM image tag information and setting the window width and the window level of an image window, and using a nonlinear function for window modulation; (2) adopting a parallel algorithm to calculate a mapping equation in nonlinearwindow modulation, and obtaining pixel data of a DIB image through calculation; (3) according to a pixel data set composed of the pixel data, obtained through calculation,

TL;DR: In this paper, a 2D-3D medical image parallel registration method based on combination similarity measure was proposed, which comprises the following steps: first, using a CUDA (Compute Unified Device Architecture) parallel computing model to finish a quick generation process of a DRR (Digitally Reconstructed Radiograph) image; combining a SAD (Sum of Absolute Difference) with PI (pattern intensity) as new similarity measure to carry out parallel computation on GPU (Graphics Processing Unit); and finally, transferring a combination similarity measured value to CPU (Central Processing Unit),

TL;DR: In this article, a wavelet-based interactive segmentation method for a polygonal outline evolution medical CT (computed tomography) image is proposed, which comprises the following steps: interactively acquiring the initial polygon-al outline of a to-be-segmented region in the CT image (generating an initial outline and a direct and reactive rendering-based polygon based on a longitudinal axis by aiming at object regions with different features by adopting a single seed point-based initial outline generation method); performing wavelet modulus maximum-based edge detection to obtain

Abstract: In order to improve the time-consuming and large error problem of camera motion estimation in dense trajectory feature extraction of video, a dense trajectory action recognition algorithm based on Improved Speeded-Up Robust Features (SURF) is proposed. The algorithm mainly performs dense sampling of video images, and then executes camera motion estimation. In the feature point detection stage, the Gaussian pyramid layer was constructed dynamically to improve the real-time and accuracy of feature point extraction. Based on the SURF algorithm, the brightness center algorithm is used to obtain direction of feature. Binary Robust Independent Elementary Feature (BRIEF) is used to generate feature descriptors to determine matching points and optimized images, then to conducts feature tracking and feature extraction on the images to classify features. The experimental results show that the algorithm performs better in terms of speed when removing camera motion, and improves the real-time performance of feature extraction and the accuracy of action recognition.