469 results about "CUDA" patented technology

Provided are an STFT parallelization system and method based on recursion Gaussian filtering on a CUDA platform. The method comprises the steps that first, original images are transmitted to a GPU end for conducting a series of Gaussian filtering and downsampling to establish a Gaussian pyramid, Gaussian filtering is conducted through a recursion Gaussian filter, and then substraction is conducted on the adjacent images to obtain a Gaussian difference pyramid; second, a thread block is used as a unit to load in an image, each thread is used for processing one pixel, and the pixel is compared with the adjacent 26 pixels to obtain local extreme points; third, each thread is used for processing one local extreme point, and positioning and selecting of key points are conducted; fourth, one thread block is used for calculating the direction of one key point, one thread is used for calculating the direction and the amplitude value of one pixel in the neighbourhood of the key point, the direction and the amplitude valve are accumulated to a gradient histogram through an atomic addition provided by a CUDA, and the information such as the coordinates and the directions of the key points are transmitted to a host end according to the directions of the key points obtained by the gradient histogram; fifth, one thread block is used for calculating one key point descriptor, then a calculating result is transmitted to the host end, and SIFT feature extraction is achieved. The STFT parallelization system and method based on the recursion Gaussian filtering on the CUDA platform improve the calculating speed of an SIFT algorithm.

The invention discloses a method and a device for generating a depth map, which belongs to the technical field of computer vision. The method comprises the following steps: performing gray-scale transformation by using a left view and a right view of an input image pair as a reference image and a target image respectively; computing upper, lower, left and right gradient matrixes of the reference image after the gray-scale transformation; computing a cost function matrix according to parallax, and processing borders; computing an original information storage matrix, and initializing upper, lower, left and right information storage matrixes; performing iterative calculation in a GPU memory by using a CUDA belief propagation algorithm to acquire upper, lower, left and right information storage matrixes after iteration, and computing a belief matrix according to the result of the iteration; and computing the depth map according to the belief matrix. The device comprises a gray-scale transformation module, a gradient computation module, a cost function matrix computation module, an original information storage matrix computation module, an iteration module, a belief matrix computation module and a depth map computation module. The method and the device improve computing efficiency of the depth map and realize high-quality and fast generation of the depth map.

The invention provides a CT (computerized tomography) on-line reconstruction and visualization method based on a CUDA (compute unified device architecture). The method comprises the following steps of obtaining projection data and pre-treating the obtained projection data; realizing FDK (Feldkamp, Davis and Kress) weighted filter process by a CPU (central processing unit); accelerating CUDA to realize FDK weighted back protection; and accelerating CUDA to realize volume rendering. The method provided by the invention can be used for implementing on-line reconstruction for cone beam CT data, so that a cone beam CT system can reconstruct a CT three-dimensional image while sampling the projection data, and an integrated CT image can be obtained finally along with the completion of the projection data, thereby realizing real-time on-line feedback.

The invention provides an ultrasonic training system based on CT (Computed Tomography) image simulation and positioning. Ultrasound image simulation and CT volumetric data rendering are accelerated to be achieved through a GPU (Graphics Processing Unit) and the real-time performance of the system is improved. A curved surface matching module is used for performing surface matching on read human body CT volumetric data and physical model data with a physical model as the standard and achieving elastic transformation of a curved surface based on an interpolation method of thin plate splines; an ultrasonic simulation probe position tracking module is used for performing real-time calculation on ultrasonic simulation probe positions relative to the physical model by a marking point tracking method and obtaining arbitrary angle CT slice images according to a position matrix; an image enhancement and ultrasonic image simulation generation module is used for improving the vessel contrast ratio in CT images by a multi-scale enhancement method and achieving the ultrasound image simulation based on the CT volumetric data; and an integration display module is used for accelerating to achieve rendering display of the CT volumetric data based on CUDA (Compute Unified Device Architecture) and integrating and displaying ultrasound simulation images and three-dimensional CT images according to the obtained position matrix.

The invention discloses a technology for restoring a depth image and combining virtual and real scenes based on a GPU (Graphic Processing Unit). The technology mainly comprises the following steps: (1), collecting the depth image and a colourized image; (2), performing down-sampling of the images so as to ensure real-time restoring speed; (3), segmenting the colourized image by using a QuickShift algorithm, wherein the specific algorithm is realized by using a CUDA (Compute Unified Device Architecture) based on GPU operation; (4), processing a segmented block lacking of depth data by utilizing the segmentation result of the colourized image; registering the Kinect depth image and colourized image at first; filling a deleted region by using an average depth value of the region if the depth data exists in the region; and filling by using the average depth value of a neighbourhood region if all depth information in the region is deleted; and (5), performing up-sampling of the images. According to the invention, the bug restoring problem of the Kinect depth images is solved in combination with an image sampling technology and a CUDA technology based on the QuickShift algorithm and the GPU operation; on this basis, virtual objects and real objects are superposed, so that shading between virtual objects and the real objects is realized; and thus, realistic interaction is enhanced.

One embodiment of the present invention sets forth a technique for translating application programs written using a parallel programming model for execution on multi-core graphics processing unit (GPU) for execution by general purpose central processing unit (CPU). Portions of the application program that rely on specific features of the multi-core GPU are converted by a translator for execution by a general purpose CPU. The application program is partitioned into regions of synchronization independent instructions. The instructions are classified as convergent or divergent and divergent memory references that are shared between regions are replicated. Thread loops are inserted to ensure correct sharing of memory between various threads during execution by the general purpose CPU.