{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T03:46:53Z","timestamp":1766720813189,"version":"3.48.0"},"reference-count":19,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The dynamic characteristics of flotation foam, such as velocity and breakage rate, are critical factors that influence mineral separation efficiency. However, challenges inherent in foam images, including weak textures, severe deformations, and motion blur, present significant technical hurdles for dynamic monitoring. These issues lead to a fundamental conflict between the efficiency and accuracy of traditional feature matching algorithms. This paper introduces a novel progressive framework for dynamic feature matching in flotation foam images, termed \u201cstable extraction, efficient coarse screening, and precise matching.\u201d This framework first employs the Accelerated-KAZE (AKAZE) algorithm to extract robust, scale- and rotation-invariant feature points from a non-linear scale-space, effectively addressing the challenge of weak textures. Subsequently, it innovatively incorporates the Grid-based Motion Statistics (GMS) algorithm to perform efficient coarse screening based on motion consistency, rapidly filtering out a large number of obvious mismatches. Finally, the Progressive Sample and Consensus (PROSAC) algorithm is used for precise matching, eliminating the remaining subtle mismatches through progressive sampling and geometric constraints. This framework enables the precise analysis of dynamic foam characteristics, including displacement, velocity, and breakage rate (enhanced by a robust \u201cfoam lifetime\u201d mechanism). Comparative experimental results demonstrate that, compared to ORB-GMS-RANSAC (with a Mean Absolute Error, MAE of 1.20 pixels and a Mean Relative Error, MRE of 9.10%) and ORB-RANSAC (MAE: 3.53 pixels, MRE: 27.36%), the proposed framework achieves significantly lower error rates (MAE: 0.23 pixels, MRE: 2.13%). It exhibits exceptional stability and accuracy, particularly in complex scenarios involving low texture and minor displacements. This research provides a high-precision, high-robustness technical solution for the dynamic monitoring and intelligent control of the flotation process.<\/jats:p>","DOI":"10.3390\/jimaging12010007","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T02:07:58Z","timestamp":1766714878000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AKAZE-GMS-PROSAC: A New Progressive Framework for Matching Dynamic Characteristics of Flotation Foam"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhen","family":"Peng","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihong","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"},{"name":"Key Laboratory of Particle Technology of Jiangxi Province, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengcheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaipin","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China"},{"name":"Key Laboratory of Particle Technology of Jiangxi Province, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.mineng.2012.02.010","article-title":"The use of machine vision to predict flotation performance","volume":"36\u201338","author":"Morar","year":"2012","journal-title":"Miner. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jiang, X., Zhao, H., Liu, J., Ma, S., and Hu, M. (2023). Research on Multi-Scale Feature Extraction and Working Condition Classification Algorithm of Lead-Zinc Ore Flotation Froth. Appl. Sci., 13.","DOI":"10.3390\/app13064028"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"120690","DOI":"10.1109\/ACCESS.2022.3221817","article-title":"Intelligent Detection Method for Froth Flotation Based on YOLOv5","volume":"10","author":"Guan","year":"2022","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.5194\/isprs-archives-XLVIII-1-W2-2023-1215-2023","article-title":"A SuperPoint Neural Network Implementation for Accurate Feature Extraction in Unstructured Environments","volume":"XLVIII-1\/W2-2023","author":"Petrakis","year":"2023","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wu, X., Liu, Z., Zhang, J., Yue, H., and Chen, W. (2024, January 5\u20138). CrossTR-Raft: Dense optical flow estimation based on cross attention mechanisms. Proceedings of the 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway.","DOI":"10.1109\/ICIEA61579.2024.10664897"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"36","DOI":"10.59247\/csol.v3i1.170","article-title":"Understanding Generative Adversarial Networks (GANs): A Review","volume":"3","author":"Purwono","year":"2025","journal-title":"Control Syst. Optim. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.3906\/elk-1204-91","article-title":"Flow velocity measurement and analysis based on froth image SIFT features and Kalman filter for froth flotation","volume":"21","author":"Liu","year":"2013","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107823","DOI":"10.1016\/j.mineng.2022.107823","article-title":"Recent advances in flotation froth image analysis","volume":"188","author":"Aldrich","year":"2022","journal-title":"Miner. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shao, S. (2023, January 29\u201331). A Monocular SLAM System Based on the ORB Features. Proceedings of the 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China.","DOI":"10.1109\/ICPECA56706.2023.10075945"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8149","DOI":"10.1007\/s11760-024-03457-x","article-title":"A matching method of underwater panoramic image based on ORB-GMS, Signal","volume":"18","author":"Wang","year":"2024","journal-title":"Image Video Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zheng, J., and Li, K. (2022, January 17\u201319). The Logistics Barcode ID Character Recognition Method Based on AKAZE Feature Localization. Proceedings of the 2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC54216.2022.9836597"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bhatlawande, S., Shilaskar, S., Anantwar, V., and Avhad, L. (2023, January 28\u201330). Integrated System for Vision-Based SLAM Implementation and Object Detection using AKAZE Feature Descriptor. Proceedings of the 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Greater Noida, India.","DOI":"10.1109\/CISES58720.2023.10183571"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Reddy, R.A., Al-Jawahry, H.M., Shruthi, B.S., Heswaran, K.M., and Pani, A.K. (2024, January 17\u201318). Robust Fingerprint MinutiSDE Extraction and Matching using Improved AKAZE. Proceedings of the 2024 Second International Conference on Data Science and Information System (ICDSIS), Hassan, India.","DOI":"10.1109\/ICDSIS61070.2024.10594081"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"086301","DOI":"10.1088\/1361-6501\/ad44c0","article-title":"Realtime 3D reconstruction algorithm based on improved AKAZE and multi-layer feature detection network","volume":"35","author":"Pan","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4158","DOI":"10.1109\/ACCESS.2025.3525471","article-title":"Feature Point Matching Method of Weak Texture Environment in Farmland Based on Improved GMS-PROSAC Fusion Algorithm","volume":"13","author":"Li","year":"2025","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1007\/s11263-019-01280-3","article-title":"GMS: Grid-Based Motion Statistics for Fast, Ultra-robust Feature Correspondence","volume":"128","author":"Bian","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1080\/21642583.2018.1477635","article-title":"An improved GMS-PROSAC algorithm for image mismatch elimination","volume":"6","author":"Zhao","year":"2018","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"23879","DOI":"10.1109\/JIOT.2025.3553879","article-title":"GMS-YOLO: A Lightweight Real-Time Object Detection Algorithm for Pedestrians and Vehicles Under Foggy Conditions","volume":"12","author":"Chen","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9429","DOI":"10.1007\/s11042-018-6475-6","article-title":"SC-RANSAC: Spatial consistency on RANSAC","volume":"78","author":"Fotouhi","year":"2019","journal-title":"Multimed. Tools Appl."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/1\/7\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T02:43:47Z","timestamp":1766717027000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/1\/7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,25]]},"references-count":19,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["jimaging12010007"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12010007","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,25]]}}}