Optimization method for image feature point extraction
An image feature point and optimization method technology, applied in the field of image processing, can solve the problems of SIFT algorithm with high complexity, large amount of calculation, poor real-time performance, etc., and achieve the effect of improving algorithm efficiency and small amount of calculation
Active Publication Date: 2020-01-17
WUHU INST OF TECH
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[0006] In view of this, the object of the present invention is to propose an optimization method for image feature point extraction, which solves the disadvantages of high complexity, large amount of calculation, and poor real-time performance of the SIFT algorithm when extracting feature points.
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[0040] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.
[0041] As one of the specific implementations of the present invention, as shown in the figure, an optimization method for extracting image feature points, the method includes:
[0042] Step 101, input image;
[0043] This step may also include: Gaussian filter preprocessing, specifically, performing a Gaussian operation on the first image in the scale space on the initial image, and performing an optimization algorithm operation based on this image.
[0044] Step 102, performing image gradient and second-order gradient calculation;
[0045] This step can also include:
[0046] Step 201, calculate the gradient value of the vertical and horizontal directions of the image, and the calculation result of each pixel is
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The invention discloses an optimization method for image feature point extraction. The optimization method comprises the following steps: inputting an image; performing image gradient and second-ordergradient calculation; calculating an image gradient factor P (m, n), and obtaining a P (T) region; establishing a Gaussian scale space and a Gaussian difference space according to the P (T) region; carrying out extreme point detection according to P (m, n); and generating a feature point descriptor. The optimization method calculates a factor Pi.j (m, n) according to the gradient image and the second-order gradient image of the image in the longitudinal direction and the transverse direction based on a certain rule, obtains a P (T) region, judges whether to participate in Gaussian scale spaceconstruction and feature point extraction according to the P (T) region, omits a large amount of calculation amount during Gaussian scale space construction and feature point extraction, is small inthe calculation amount additionally increased by the method, optimizes the algorithm in the feature point extraction stage, and improves the algorithm efficiency.
Description
technical field [0001] The invention relates to the technical field of image processing, in particular to an optimization method for extracting image feature points. Background technique [0002] SIFT, or Scale-invariant feature transform, is a description used in the field of image processing. This description has scale invariance, can detect key points in the image, and is a local feature descriptor. [0003] Image registration technology based on image features has been widely used in image processing, computer vision, pattern recognition and other fields. Because of its strong robustness in various scenarios, SIFT algorithm has become a research hotspot of image registration technology based on image features and has been widely used. [0004] However, the applicant has found that the existing image feature point extraction has at least the following problems: [0005] When the SIFT algorithm extracts feature points, it takes the most time to construct Gaussian scale ...
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Login to View More IPC IPC(8): G06K9/46
CPCG06V10/473G06V10/44
Inventor 马书香
Owner WUHU INST OF TECH