Method for increasing number of feature points in weak texture region of image

A technology of regional features and weak textures, applied in the field of computer vision, can solve the problems of high complexity of SIFT algorithm, low complexity of binary algorithm, inability to meet real-time requirements, etc. poor sex effect

Active Publication Date: 2017-07-18
XIDIAN UNIV +1
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Problems solved by technology

[0003] To sum up, the existing problems in the prior art are: the SIFT algorithm is highly complex, requires a large amount of floating-point calculations and buffer space, and cannot meet

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  • Method for increasing number of feature points in weak texture region of image
  • Method for increasing number of feature points in weak texture region of image
  • Method for increasing number of feature points in weak texture region of image

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[0052] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0053] The application principle of the present invention will be described in detail below with reference to the accompanying drawings.

[0054] Such as figure 1 As shown, the method for increasing the number of feature points in a weak texture region of an image provided by an embodiment of the present invention includes the following steps:

[0055] S101: Extract the details of an image to obtain a detailed texture map of an image; construct a Gaussian difference pyramid on the original image, and detect corner points at the same time, and the two detection methods are calculated in parallel;

[0056] S102: Perform corn...

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Abstract

The invention belongs to the technical field of computer visions, and discloses a method for increasing the number of feature points in a weak texture region of one image. The method comprises the steps: carrying out the detail extraction of one image, and obtaining the detail texture map of one image; building a Gaussian difference pyramid for an original image, and detecting corners; carrying out the parallel computing of the detail extraction and the Gaussian difference pyramid, carrying out the corner extraction of the detail map, and finally forming a feature point set through merging; generating a binary descriptor with rotation invariance on the corresponding image for the extracted feature points; carrying out the matching and filtering of the descriptors of two images, and obtaining a correct matching point set. The method employs a binary operator, and can generate a description vector through simple summation and comparison. Meanwhile, the method employs the parallel computing of corner detection and spatial extreme point detection, greatly improves the stability of feature points, is low in coupling degree of calculation steps, is high in parallelism degree, and is very suitable for hardware implementation.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a method for increasing the number of feature points in an image weak texture area. Background technique [0002] Image matching is an extremely important technology in the field of computer vision, and its applications include pattern recognition, automatic navigation, 3D reconstruction, impact stitching and other fields. Currently, image matching mainly uses matching methods based on image local features. An excellent local feature needs to have the properties of small amount of calculation, good robustness, and insensitivity to image changes. The performance of image local features has become the technical bottleneck of these applications, and the result directly affects the effect of its application. With the rapid rise of the high-definition video processing industry, the application of high-definition technologies such as 4K and even 8K is becoming more...

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Application Information

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IPC IPC(8): G06T7/33G06T7/13
Inventor 余何袁承宗宋锐李云松王养利王智卓
Owner XIDIAN UNIV
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