{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:24:59Z","timestamp":1760239499788,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T00:00:00Z","timestamp":1605744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Feature point detection is the basis of computer vision, and the detection methods with geometric invariance and illumination invariance are the key and difficult problem in the field of feature detection. This paper proposes an illumination-invariant feature point detection method based on neighborhood information. The method can be summarized into two steps. Firstly, the feature points are divided into eight types according to the number of connected neighbors. Secondly, each type of feature points is classified again according to the position distribution of neighboring pixels. The theoretical deduction proves that the proposed method has lower computational complexity than other methods. The experimental results indicate that, when the photometric variation of the two images is very large, the feature-based detection methods are usually inferior, while the learning-based detection methods performs better. However, our method performs better than the learning-based detection method in terms of the number of feature points, the number of matching points, and the repeatability rate stability. The experimental results demonstrate that the proposed method has the best illumination robustness among state-of-the-art feature detection methods.<\/jats:p>","DOI":"10.3390\/s20226630","type":"journal-article","created":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T06:23:52Z","timestamp":1605767032000},"page":"6630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Illumination-Invariant Feature Point Detection Based on Neighborhood Information"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3543-3688","authenticated-orcid":false,"given":"Ruiping","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangcai","family":"Zeng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6383-7663","authenticated-orcid":false,"given":"Shiqian","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Robotics and Intelligent Systems, Wuhan University of Science and Technology, Wuhan 430081, China"},{"name":"School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelvin","family":"Wong","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide 5005, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15751","DOI":"10.1007\/s11042-018-7031-0","article-title":"Object detection and classification: A joint selection and fusion strategy of deep convolutional neural network and SIFT point features","volume":"78","author":"Rashid","year":"2019","journal-title":"Multimed. 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