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
CN106529591AInactive Publication Date: 2017-03-22HUNAN VISION SPLEND PHOTOELECTRIC TECH
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
HUNAN VISION SPLEND PHOTOELECTRIC TECH
Publication Date
2017-03-22
Estimated Expiration
Not applicable · inactive patent

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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.
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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...

Claims

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