Improved MSER image matching algorithm

An image matching algorithm and MSER technology, applied in the field of computer vision, can solve the problem of insufficient robustness of radiation transformation, save running time, meet the requirements of image matching accuracy, and achieve the effect of good robustness

Inactive Publication Date: 2017-03-22
HUNAN VISION SPLEND PHOTOELECTRIC TECH
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However, SURF features are not robus

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[0053] Now in conjunction with the accompanying drawings, the present invention will be described in detail. This embodiment is an image matching algorithm based on improved MSER, which includes the following steps:

[0054] The first step is to extract the MSER features of the two images respectively;

[0055] Maximally Stable External Region, referred to as MSER, the MSER detector has the best performance in most cases in many affine invariant feature regions. The MSER is a region of extreme density with respect to its surroundings. The basic idea is: for any grayscale image, select all possible thresholds 0-255 from small to large. Set the pixels smaller than the threshold in the image to 0, and the pixels larger than the threshold to 1, so 256 binary images are obtained. For each image, there will be many connected regions. MSER is the threshold value in these regions. Vary the area whose area changes by less than a certain value.

[0056] 1.1. Value area extraction

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Abstract

The invention relates to the field of computer vision and particularly relates to an improved MSER image matching algorithm. A speeded up robust feature (SURF) and a maximally stable extremal region feature (MSER) are combined to carry out image feature extraction and matching so as to generate a feature vector, and then the Euclidean distance is used to carry out the coarse matching of an image so as to preliminarily correct the space geometric distortion of the image. Then the scale invariance of an H-L feature is applied, and a feature point comprising a large amount of image structure information can be detected. According to the algorithm, the complementarity of feature extraction of two parties in the multiple transformation conditions of the image can be fully utilized, and the robustness of matching between images in a complex environment in a time condition acceptable range is achieved.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to an image matching algorithm based on improved MSER. Background technique [0002] Image matching is not only one of the key technologies of image processing, but also a core issue in the fields of medical imaging, computer vision and pattern recognition. [0003] Regarding image feature extraction and matching, many effective algorithms have been proposed at home and abroad. Today's feature extraction algorithms mainly include three categories: corner feature detection, spot feature detection and region feature detection. Among them, the SIFT (Scale-invariant feature transform) algorithm proposed by Lowe et al., the extracted feature points have good stability for image translation, rotation, scale and certain viewpoint changes, and have been widely used. However, the obvious disadvantage of the SIFT algorithm is that the amount of calculation data is large and the time complexit...

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Application Information

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IPC IPC(8): G06K9/62G06K9/46G06K9/36
CPCG06V10/20G06V10/247G06V10/462G06F18/22G06F18/253
Inventor 颜微付东奇马昊辰
Owner HUNAN VISION SPLEND PHOTOELECTRIC TECH
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