{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T08:35:16Z","timestamp":1771058116922,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,13]],"date-time":"2018-11-13T00:00:00Z","timestamp":1542067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61663010"],"award-info":[{"award-number":["61663010"]}]},{"name":"National Nature Science Foundation of China","award":["61563014"],"award-info":[{"award-number":["61563014"]}]},{"name":"Nature Science Foundation of Jiangxi Province, China","award":["20161BAB202068"],"award-info":[{"award-number":["20161BAB202068"]}]},{"name":"the Key Research and Development Project of Jiangxi Province, China","award":["20171BBH80024"],"award-info":[{"award-number":["20171BBH80024"]}]},{"name":"Collaborative Innovation Center for Economics crime investigation and prevention technology, Jiangxi Province","award":["JXJZXTCX-005"],"award-info":[{"award-number":["JXJZXTCX-005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>RGB-D cameras offer both color and depth images of the surrounding environment, making them an attractive option for robotic and vision applications. This work introduces the BRISK_D algorithm, which efficiently combines Features from Accelerated Segment Test (FAST) and Binary Robust Invariant Scalable Keypoints (BRISK) methods. In the BRISK_D algorithm, the keypoints are detected by the FAST algorithm and the location of the keypoint is refined in the scale and the space. The scale factor of the keypoint is directly computed with the depth information of the image. In the experiment, we have made a detailed comparative analysis of the three algorithms SURF, BRISK and BRISK_D from the aspects of scaling, rotation, perspective and blur. The BRISK_D algorithm combines depth information and has good algorithm performance.<\/jats:p>","DOI":"10.3390\/s18113908","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T10:58:22Z","timestamp":1542193102000},"page":"3908","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A FAST-BRISK Feature Detector with Depth Information"],"prefix":"10.3390","volume":"18","author":[{"given":"Yanli","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9027-3261","authenticated-orcid":false,"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"given":"Hanlei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Information Engineering, East China Jiaotong University, Nanchang 330013, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0394-4635","authenticated-orcid":false,"given":"Neal N.","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,13]]},"reference":[{"key":"ref_1","first-page":"3438","article-title":"Depth-image based on 3D map reconstruction of indoor environment for mobile robots","volume":"34","author":"Zhang","year":"2014","journal-title":"J. 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