Results 251 to 260 of about 282,520 (286)

Corner Detection Algorithm with Improved Harris

2015
Traditional algorithm of Harris needs to select a parameter for computing interest values of pixels, and its recognition ability for some types of corners is poor. To solve this problem, this paper proposes a corner detection method which is based on local standard deviation and logarithmic computing.
Li Wan, Zhenming Yu, Qiuhui Yang
openaire   +1 more source

Optimization matching algorithm based on improved Harris and SIFT

2010 International Conference on Machine Learning and Cybernetics, 2010
In order to restrain the problem of low automation level of feature detector and high matching consuming as for conventional local matching algorithms, the paper proposes an optimization matching algorithm based on Harris and SIFT algorithm. In this algorithm, Harris corner detector based on the ideology image break raises automation level of feature ...
Jie Zhao, Lijuan Xue, Guozun Men
openaire   +1 more source

Optimization of Harris Corner Detection Algorithm

2012
Corner detection is often influenced by noise when Harris corner detection algorithm is applied to digital images. In this paper, the response function of Harris corner detection algorithm is optimized to avoid the influence of k in response function, thus the precision of feature response function of target pixels is improved.
Xiong Shunqing, Zhou Weihong, Xia Wei
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Improve Harris Hawkes optimizer algorithm via Laplace crossover

Journal of Ambient Intelligence and Humanized Computing
Seyed Taha Mousavi nasab, Laith Mohammad Abualigah   +1 more
exaly   +2 more sources

An Improved Corner Detection Algorithm Based on Harris

2018 Chinese Automation Congress (CAC), 2018
Aiming at the problems that the traditional Harris corner detection algorithm could extract more flase corner points and computational complexity when performing corner extraction on the image, an improved Harris corner detection algorithm is proposed.
Songqi Han, Weibo Yu, Hongtao Yang, Shicheng Wan   +3 more
openaire   +1 more source

Image Matching Algorithm Based on Improved Harris

2015 International Conference on Computational Intelligence and Communication Networks (CICN), 2015
An improved algorithm for image mosaic is proposed based on traditional Harris corner detection method. First, by modifying the corner detection area, and corner response function, you can detect the corners more accurately. Then use a method based on K-d tree and RANSAC to match the feature points and purify mismatching points.
Fei Lei, Wei Ren, Lu Lv
openaire   +1 more source

New Reliable Algorithm for Fractional Harry Dym Equation

2014
In this paper, a new reliable algorithm based on homotopy perturbation method using Laplace transform, named homotopy perturbation transform method (HPTM), is proposed to solve nonlinear fractional Harry Dym equation. The numerical solutions obtained by the HPTM show that the approach is easy to implement and computationally very attractive.
Devendra Kumar, Jagdev Singh
openaire   +1 more source

An Improved Harris Corner Detection Algorithm

2019
The traditional Harris corner detection algorithm is sensitive to noise, and Corner is prone to drift at different image resolution. Combined with the multi-scale features of wavelet transform, propose a corner detection algorithm based on the wavelet transform.
openaire   +1 more source

Object Tracking Algorithm Based on the Harris Detector

2020 Systems of Signals Generating and Processing in the Field of on Board Communications, 2020
The article is devoted to the development of prototype hardware and software for the automatic tracking of the object based on the Harris detector. An original algorithm is invariant to distortion of the form “zooming”, “picture rotation $\prime\prime$ , “illumination scene change”, and software implementation of the algorithm.
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Improved Harris algorithm within scale-invariance

5th International Conference on Computer Sciences and Convergence Information Technology, 2010
Based on the SIFT recognition method, this solution is to obtain the natural features in computer vision recognition and matching by means of the Gauss pyramid, DOG pyramid, their differential functions appoximated instead of σ2▽2G to achieve the scale invariance practice.
null Hao yongtao, null Yu ping
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