Image matching method

An image, to-be-matched technology, applied in the field of image processing, can solve problems such as strong noise sensitivity, slow calculation speed, and poor illumination change effect.

Inactive Publication Date: 2012-07-18
NORTH CHINA UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

Harris——Laplacian algorithm (Mikolajczyk K, Schmid C.Indexing based on scale invariant interestpoints[C]. / / Proceedings of the 8 th International Conference on Computer Vision, Vancouver, 2001: 525-531) simple calculation, low dimensionality, easy to match, but strong sensitivity to noise
SIFT algorithm (Lowe D G. Distinctive image features from scale invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110) has rotation invariance, scale invariance, affine transformation invariance, etc. feature, but is slower to compute and doesn't work well with lighting changes
Later, Mikolajczyk (Mikolajczyk K, and Schmid C.A performance evaluation of local descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630) compared various feature point descriptors, Concluded that SIFT has the best results, but their affine invariance is poor

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[0051] Various aspects are now described with reference to the figures. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. It may be evident, however, that such aspects can be practiced without these specific details.

[0052] As used in this application, the terms "component", "module", "system" and the like are intended to refer to a computer-related entity such as, but not limited to, hardware, firmware, a combination of hardware and software, software, Or software in execution. For example, a component may be, but is not limited to being limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. For example, both an application running on a computing device and the computing device can be components. One or more components can reside within a process and / or thread of execution ...

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Abstract

The invention discloses a method. The method comprises the following steps: (a) inputting a two-dimensional grayscale image (x, y); (b) based on the two-dimensional grayscale image and a scale factor, creating a difference of Gaussian scale space, wherein the number of layers of the difference of Gaussian scale space is dependent on the scale factor; (c) selecting one or more remarkable regions from the layers in the scale space; (d) carrying out region description on the selected remarkable regions by using region descriptors; and (e) respectively taking a reference image and an image to be matched as the two-dimensional grayscale image for obtaining the remarkable regions and the related region descriptors of the two-dimensional grayscale image and the reference image, and, based on the Euclidean distances of the obtained region descriptors of the two-dimensional grayscale image and the reference image, carrying out region matching on the remarkable regions of the two-dimensional grayscale image and the reference image.

Description

[0001] joint research [0002] This application is supported by the following funds from North China University of Technology: National Natural Science Foundation of China (No.61103113) technical field [0003] The present invention relates to the field of image processing, and more specifically, to a salient region extraction and matching method, device and computer program product based on Gaussian difference scale space. Background technique [0004] In recent years, the application of scale space theory in image processing has gradually become one of the hot spots in the field of image processing and computer vision. At present, there are many algorithms for extracting scale-invariant feature points: Harris-Laplacian algorithm, SIFT algorithm and SURF algorithm. Harris——Laplacian algorithm (Mikolajczyk K, Schmid C.Indexing based on scale invariant interestpoints[C]. / / Proceedings of the 8 th International Conference on Computer Vision, Vancouver, 2001: 525-531) is simp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 张萌萌李泽明
Owner NORTH CHINA UNIVERSITY OF TECHNOLOGY
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