SIFT parallelization system and method based on recursion Gaussian filtering on CUDA platform

Abstract

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.

Description

technical field [0001] The present invention relates to a SIFT (Scale-invariant feature transform) parallelization method on a CUDA (Compute unified device architecture) platform, in particular to a SIFT parallelization method based on recursive Gaussian filtering method. Background technique [0002] Video data, as a dynamic, intuitive, and visual digital media, is increasingly appearing in various information services and applications, and with the reduction of storage costs, the advancement of high-speed transmission and compression technology, the number of video information has increased Rapid expansion, therefore, the amount of calculation for video content analysis and processing has also greatly increased. [0003] Since the amount of video data is often large and the algorithm is relatively complex, the amount of calculation is usually relatively large, and the single use of CPU often cannot meet the real-time requirements. In addition, the implementation of video...

AI Technical Summary

Problems solved by technology

This algorithm has high stability for rotation, scaling, and brightness changes, but the algorithm is relatively complex and the amount of calculation is large, and it cannot meet the real-time requirements well, so it is necessary to consider using a parallel method to accelerate the calculation of SIFT

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