{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:28:09Z","timestamp":1777573689191,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2017,8,6]],"date-time":"2017-08-06T00:00:00Z","timestamp":1501977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61371175"],"award-info":[{"award-number":["61371175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The edge-based active contour model has been one of the most influential models in image segmentation, in which the level set method is usually used to minimize the active contour energy function and then find the desired contour. However, for infrared thermal pedestrian images, the traditional level set-based method that utilizes the gradient information as edge indicator function fails to provide the satisfactory boundary of the target. That is due to the poorly defined boundaries and the intensity inhomogeneity. Therefore, we propose a novel level set-based thermal infrared image segmentation method that is able to deal with the above problems. Specifically, we firstly explore the one-bit transform convolution kernel and define a soft mark, from which the target boundary is enhanced. Then we propose a weight function to adaptively adjust the intensity of the infrared image so as to reduce the intensity inhomogeneity. In the level set formulation, those processes can adaptively adjust the edge indicator function, from which the evolving curve will stop at the target boundary. We conduct the experiments on benchmark infrared pedestrian images and compare our introduced method with the state-of-the-art approaches to demonstrate the excellent performance of the proposed method.<\/jats:p>","DOI":"10.3390\/s17081811","type":"journal-article","created":{"date-parts":[[2017,8,9]],"date-time":"2017-08-09T06:32:14Z","timestamp":1502260334000},"page":"1811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Thermal Infrared Pedestrian Image Segmentation Using Level Set Method"],"prefix":"10.3390","volume":"17","author":[{"given":"Yulong","family":"Qiao","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwei","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1016\/j.optlastec.2008.11.007","article-title":"Detection and tracking of targets in infrared images using Bayesian techniques","volume":"41","author":"Shaik","year":"2009","journal-title":"Opt. Laser Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, J., Tang, Z., Cui, Y., and Wu, G. (2017). Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing. Sensors, 17.","DOI":"10.3390\/s17061364"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, R., Zhu, S., and Zhou, Q. (2016). A Novel Gradient Vector Flow Snake Model Based on Convex Function for Infrared Image Segmentation. Sensors, 16.","DOI":"10.3390\/s16101756"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/0021-9991(88)90002-2","article-title":"Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations","volume":"79","author":"Osher","year":"1988","journal-title":"J. Comput. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1023\/A:1007979827043","article-title":"Geodesic Active Contours","volume":"22","author":"Caselles","year":"1997","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF01385685","article-title":"A geometric model for active contours in image processing","volume":"66","author":"Caselles","year":"1993","journal-title":"Numer. Math."},{"key":"ref_7","unstructured":"Caselles, V., Kimmel, R., and Sapiro, G. (1995, January 20\u201323). Geodesic active contours. Proceedings of the Fifth International Conference on Computer Vision, Cambridge, MA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/34.368173","article-title":"Shape modeling with front propagation: A level set approach","volume":"17","author":"Malladi","year":"1995","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/BF00133570","article-title":"Snakes: Active contour models","volume":"1","author":"Kass","year":"1988","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/83.661186","article-title":"Snakes, shapes, and gradient vector flow","volume":"7","author":"Xu","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1002\/cpa.3160420503","article-title":"Optimal approximations by piecewise smooth functions and associated variational problems","volume":"42","author":"Mumford","year":"1989","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1023\/A:1020874308076","article-title":"A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model","volume":"50","author":"Vese","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1109\/TIP.2011.2146190","article-title":"A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI","volume":"20","author":"Li","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.infrared.2015.12.003","article-title":"An improved Chan\u2014Vese model by regional fitting for infrared image segmentation","volume":"74","author":"Zhou","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Arandjelovic, O. (2012, January 3\u20137). Object Matching Using Boundary Descriptors. Proceedings of the British Machine Vision Conference 2012, Surrey, UK.","DOI":"10.5244\/C.26.85"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TGRS.2012.2200689","article-title":"Automatic Rooftop Extraction in Nadir Aerial Imagery of Suburban Regions Using Corners and Variational Level Set Evolution","volume":"51","author":"Cote","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3466","DOI":"10.1016\/j.patcog.2015.04.011","article-title":"Efficient and accurate set-based registration of time-separated aerial images","volume":"48","author":"Pham","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/LSP.2015.2508039","article-title":"Robust Edge-Stop Functions for Edge-Based Active Contour Models in Medical Image Segmentation","volume":"23","author":"Pratondo","year":"2016","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.eswa.2016.02.048","article-title":"Active contours driven by Cuckoo Search strategy for brain tumour images segmentation","volume":"56","author":"Lindner","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.media.2016.05.009","article-title":"Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance","volume":"35","author":"Ngo","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rousson, M., Paragios, N., and Deriche, R. (2004, January 26\u201329). Implicit Active Shape Models for 3D Segmentation in MR Imaging. Proceedings of the Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2004, Saint-Malo, France.","DOI":"10.1007\/978-3-540-30135-6_26"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3233\/THC-150915","article-title":"An approach to analyze the breast tissues in infrared images using nonlinear adaptive level sets and Riesz transform features","volume":"23","author":"Prabha","year":"2015","journal-title":"Technol. Health Care"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.infrared.2013.08.014","article-title":"Background subtraction based level sets for human segmentation in thermal infrared surveillance systems","volume":"61","author":"Tan","year":"2013","journal-title":"Infrared Phys. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.infrared.2013.12.012","article-title":"Adaptive contour-based statistical background subtraction method for moving target detection in infrared video sequences","volume":"63","author":"Akula","year":"2014","journal-title":"Infrared Phys. Technol."},{"key":"ref_26","unstructured":"Li, C., Xu, C., Gui, C., and Fox, M.D. (2005, January 20\u201325). Level Set Evolution without Re-Initialization: A New Variational Formulation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3243","DOI":"10.1109\/TIP.2010.2069690","article-title":"Distance regularized level set evolution and its application to image segmentation","volume":"19","author":"Li","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1109\/TIP.2012.2214046","article-title":"Reinitialization-Free Level Set Evolution via Reaction Diffusion","volume":"22","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.infrared.2011.08.009","article-title":"Tensor diffusion level set method for infrared targets contours extraction","volume":"55","author":"Li","year":"2012","journal-title":"Infrared Phys. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9809","DOI":"10.1364\/AO.54.009809","article-title":"Guide filter-based gradient vector flow module for infrared image segmentation","volume":"54","author":"Zhao","year":"2015","journal-title":"Appl. Opt."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4722","DOI":"10.1109\/TIP.2012.2202674","article-title":"Efficient algorithm for level set method preserving distance function","volume":"21","author":"Estellers","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1109\/76.611181","article-title":"Low-complexity block-based motion estimation via one-bit transforms","volume":"7","author":"Natarajan","year":"1997","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/LSP.2006.882088","article-title":"Multiplication-Free One-Bit Transform for Low-Complexity Block-Based Motion Estimation","volume":"14","author":"Erturk","year":"2007","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1109\/LSP.2013.2274637","article-title":"Region of Interest Extraction in Infrared Images Using One-Bit Transform","volume":"20","author":"Erturk","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Davis, J.W., and Keck, M.A. (2005, January 5\u20137). A Two-Stage Template Approach to Person Detection in Thermal Imagery. Proceedings of the Seventh IEEE Workshops on Application of Computer Vision, Breckenridge, CO, USA.","DOI":"10.1109\/ACVMOT.2005.14"},{"key":"ref_36","unstructured":"Miezianko, R. (2017, August 03). Terravic Research Infrared Database. . Available online: http:\/\/vcipl-okstate.org\/pbvs\/bench\/."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.infrared.2014.02.005","article-title":"Thermal\u2014Visible registration of human silhouettes: A similarity measure performance evaluation","volume":"64","author":"Bilodeau","year":"2014","journal-title":"Infrared Phys. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.neucom.2016.05.094","article-title":"InfAR dataset: Infrared action recognition at different times","volume":"212","author":"Gao","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.neuroimage.2009.03.068","article-title":"Performance measure characterization for evaluating neuroimage segmentation algorithms","volume":"47","author":"Chang","year":"2009","journal-title":"Neuroimage"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sofian, H., Than, J.C.M., Noor, N.M., and Mohamad, S. (2017, January 5\u20137). Lumen boundary detection in IVUS medical imaging using structured element. Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication, Beppu, Japan.","DOI":"10.1145\/3022227.3022296"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compbiomed.2010.10.007","article-title":"Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation","volume":"41","author":"Li","year":"2011","journal-title":"Comput. Biol. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1109\/TCYB.2015.2409119","article-title":"A Level Set Approach to Image Segmentation With Intensity Inhomogeneity","volume":"46","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Cybern."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/8\/1811\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:41:33Z","timestamp":1760208093000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/8\/1811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,6]]},"references-count":42,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2017,8]]}},"alternative-id":["s17081811"],"URL":"https:\/\/doi.org\/10.3390\/s17081811","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,8,6]]}}}