Automatic fast mine monitoring image stitching method

Abstract

The invention discloses an automatic fast mine monitoring image stitching method comprising the following steps: first, a Harris algorithm is adopted to initially obtain feature points at a single scale, an SIFT algorithm is adopted to precisely locate the feature points, and SIFT descriptor information is utilized on the basis so as to stably and quickly extract image feature points; second, a locality sensitive Hashing (LSH) algorithm is adopted to carry out feature matching search and solve the problem that the time efficiency of a Kd tree search algorithm in a high-dimensional data set is not high; third, an improved RANSAC algorithm is adopted to screen matching points and calculate a transform matrix so as to improve the accuracy of matching point screening; and finally, stitching is carried out by an image fusion algorithm. Experimental results show the method can be applied to automatic coal mine underground video monitoring image stitching, and has scientific reference value to image stitching in other complex environments.

Description

technical field [0001] The invention relates to the technical field of image splicing, in particular to an automatic and fast splicing method for mine monitoring images. Background technique [0002] The core problem of image stitching is to accurately find the position and range of overlapping regions of images to be stitched, that is, image registration. Most of the currently proposed image registration algorithms can be divided into two categories: methods based on grayscale registration and methods based on feature registration. The grayscale-based registration method has a large amount of calculation, cannot meet the real-time requirements, and is easily affected by image rotation, deformation and occlusion, while the feature-based registration method can overcome these shortcomings and improve the accuracy of matching. It has a wide range of applications in image registration, such as Harris algorithm, SIFT algorithm, etc. Many scholars at home and abroad have done a...

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Problems solved by technology

[0003] Although the various methods mentioned above are all oriented to a certain range of application fields and have their own characteristics, generally speaking, there are not many image stitching algorithms that can be applied to the complex environment of underground coal mines. Due to the limitations of the algorithm itself, many image sequences cannot be stitched together successfully, so it is necessary to study an image stitching algorithm with a high success rate; in addition, because the image stitching technology can be widely used in the video surveillance system of coal mines, so The real-time problem is also a key issue in the research; moreover, due to the influence of low illumination, uneven illumination and underground coal dust in coal mines, the monitoring image is degraded to a large extent, resulting in poor image quality. The sequence cannot be successfully stitched together, which brings a lot of inconvenience to the post-processing and evaluation of the monitoring image, and it is difficult to meet the needs of the free monitoring scene of coal mine safety production

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