Image matching method based on improved Harris-Laplace and scale invariant feature transform (SIFT) descriptor

An image matching and descriptor technology, applied in the field of image processing, can solve the problems of image noise and fine texture changes, matching accuracy and other problems, and achieve the effect of overcoming the effect of noise and high-precision matching results.

Inactive Publication Date: 2013-02-06
NANJING UNIV
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Problems solved by technology

[0004] However, some feature points extracted by SIFT in the feature detection stage may be located on the edge where the brightness only changes in one direction. Such feature points are easily affected by image noise and fine texture changes. If the above feature points are used to match images, it is bound to Will have a certain impact on the matching accuracy

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  • Image matching method based on improved Harris-Laplace and scale invariant feature transform (SIFT) descriptor
  • Image matching method based on improved Harris-Laplace and scale invariant feature transform (SIFT) descriptor
  • Image matching method based on improved Harris-Laplace and scale invariant feature transform (SIFT) descriptor

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[0017] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention Modifications in equivalent forms all fall within the scope defined by the appended claims of this application.

[0018] The basic ideas of the present invention are as follows: in the feature detection stage, the improved Harris-Laplace is used to extract key points, determine the main direction of key points, and generate feature points; in the feature description stage, SIFT descriptors are used to describe feature points; in the feature matching stage, BBF algorithm and RANSAC are respectively used Algorithm rough matching and fine matching feature points.

[0019] In order to m...

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Abstract

The invention discloses an image matching method based on improved Harris-Laplace and a scale invariant feature transform (SIFT) descriptor. The method comprises the following steps: 1, extracting key points on an image by adopting the improved Harris-Laplace and determining the main direction of the key points to generate feature points; 2, describing the feature points by using the SIFT descriptor; and 3, roughly matching and finely matching the feature points respectively by a best bin first (BBF) nearest neighbor search algorithm and a random sample consensus algorithm (RANSAC). The key points extracted by the improved Harris-Laplace not only have invariance on illumination change, rotation change and scale change, but also can effectively overcome the influence of noise, so that the method has a high-precision matching result.

Description

technical field [0001] The invention relates to an image processing method, in particular to an image matching method based on improved Harris-Laplace and SIFT descriptors. Background technique [0002] The current image registration method mainly relies on manually extracting the same-name points. For large-scale images, it will inevitably consume a lot of human resources. At the same time, due to perception deviation, there will be a certain deviation between the extracted same-name points, which will directly affect image registration. accuracy. Image matching can provide the underlying basis for automatic image registration. Therefore, an image matching method with high accuracy is particularly important for automatic image registration. [0003] Image matching is the process of automatically finding the target with the same name, which can be divided into matching based on area gray level and matching based on features. Compared with the matching based on the area gra...

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Application Information

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IPC IPC(8): G06T7/00G06T5/00
Inventor 佘江峰徐秋辉宋晓群
Owner NANJING UNIV
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