{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T02:53:55Z","timestamp":1768618435172,"version":"3.49.0"},"reference-count":82,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2015,4,29]],"date-time":"2015-04-29T00:00:00Z","timestamp":1430265600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"US Army Research Lab (ARL) and Army Research Office (ARO)","award":["W911NF-08-1-0293"],"award-info":[{"award-number":["W911NF-08-1-0293"]}]},{"name":"Oklahoma Center for the Advancement of Science and Technology (OCAST)","award":["HR12-30"],"award-info":[{"award-number":["HR12-30"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation).<\/jats:p>","DOI":"10.3390\/s150510118","type":"journal-article","created":{"date-parts":[[2015,4,29]],"date-time":"2015-04-29T11:02:35Z","timestamp":1430305355000},"page":"10118-10145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set"],"prefix":"10.3390","volume":"15","author":[{"given":"Liangjiang","family":"Yu","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiulu","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph","family":"Havlicek","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1109\/TAES.1986.310772","article-title":"Automatic target recognition: State of the art survey","volume":"AES-22","author":"Bhanu","year":"1986","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/62.240102","article-title":"Image understanding research for automatic target recognition","volume":"8","author":"Bhanu","year":"1993","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_3","first-page":"3","article-title":"An overview of automatic target recognition","volume":"6","author":"Dudgeon","year":"1993","journal-title":"Linc. Lab. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"072001-1","DOI":"10.1117\/1.3601879","article-title":"Review of current aided\/automatic target acquisition technology for military target acquisition tasks","volume":"50","author":"Ratches","year":"2011","journal-title":"Opt. Eng."},{"key":"ref_5","unstructured":"Military Sensing Information Analysis Center (SENSIAC). Available online: http:\/\/www.sensiac.org\/external\/index.jsf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yilmaz, A., Javed, O., and Shah, M. (2006). Object tracking: A survey. ACM Comput. Surv., 38.","DOI":"10.1145\/1177352.1177355"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Srinivas, U., Monga, V., and Riasati, V. (2012, January 7\u201311). A comparative study of basis selection techniques for automatic target recognition. Atlanta, GA, USA.","DOI":"10.1109\/RADAR.2012.6212230"},{"key":"ref_8","unstructured":"Bhatnagar, V., Shaw, A., and Williams, R. (1998, January 12\u201315). Improved automatic target recognition using singular value decomposition. Seattle, WA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/83.552100","article-title":"Automatic target recognition by matching oriented edge pixels","volume":"6","author":"Olson","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1117\/12.59913","article-title":"Wavelet and Gabor transforms for detection","volume":"31","author":"Casasent","year":"1992","journal-title":"Opt. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/34.709572","article-title":"Hilbert-Schmidt lower bounds for estimators on matrix Lie groups for ATR","volume":"20","author":"Grenander","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1117\/12.326795","article-title":"Automatic target recognition using neural networks","volume":"3466","author":"Wang","year":"1998","journal-title":"Proc. SPIE"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xiong, W., and Cao, L. (2009, January 24\u201326). Automatic target recognition based on rough set-support vector machine in SAR images. Sanya, Hainan, China.","DOI":"10.1109\/CSO.2009.27"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1364\/AO.50.001425","article-title":"Sparsity-motivated automatic target recognition","volume":"50","author":"Patel","year":"2011","journal-title":"Appl. Opt."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object detection with discriminatively trained part-based models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dong, L., Yu, X., Li, L., and Hoe, J. (2010, January 7\u201310). HOG based multi-stage object detection and pose recognition for service robot. Singapore.","DOI":"10.1109\/ICARCV.2010.5707916"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lowe, D. (1999, January 20\u201327). Object recognition from local scale-invariant features. Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/34.765655","article-title":"Using spin images for efficient object recognition in cluttered 3D scenes","volume":"21","author":"Johnson","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s11263-008-0139-3","article-title":"Learning an alphabet of shape and appearance for multi-class object detection","volume":"80","author":"Opelt","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/34.993558","article-title":"Shape matching and object recognition using shape contexts","volume":"24","author":"Belongie","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1007\/978-3-642-33786-4_30","article-title":"Semi-nonnegative matrix factorization for motion segmentation with missing data","volume":"Volume 7578","author":"Fitzgibbon","year":"2012","journal-title":"Computer Vision\u2014ECCV 2012"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TPAMI.2006.68","article-title":"A texture-based method for modeling the background and detecting moving objects","volume":"28","author":"Heikkila","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.3390\/s140203690","article-title":"Feature point descriptors: Infrared and visible spectra","volume":"14","author":"Ricaurte","year":"2014","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1109\/TPAMI.2012.186","article-title":"Support vector shape: A classifier-based shape representation","volume":"35","author":"Nguyen","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","unstructured":"Saidi, M.N., Toumi, A., Hoeltzener, B., Khenchaf, A., and Aboutajdine, D. (2009, January 12\u201316). Aircraft target recognition: A novel approach for features extraction from ISAR images. Bordeaux, France."},{"key":"ref_26","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_27","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_28","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/34.841758","article-title":"Geodesic active contours and level sets for the detection and tracking of moving objects","volume":"22","author":"Paragios","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1117\/12.421217","article-title":"Segmentation and target recognition in SAR imagery using a level-sets-multiscale-filtering technique","volume":"4391","author":"Unal","year":"2001","journal-title":"Proc. SPIE"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, M., and Cai, J. (2013). An on-line learning tracking of non-rigid target combining multiple-instance boosting and level set. Proc. SPIE.","DOI":"10.1117\/12.2031170"},{"key":"ref_31","unstructured":"Leventon, M., Grimson, W., and Faugeras, O. (2000, January 13\u201315). Statistical shape influence in geodesic active contours. Hilton Head Island, SC, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1109\/TMI.2002.808355","article-title":"A shape-based approach to the segmentation of medical imagery using level sets","volume":"22","author":"Tsai","year":"2003","journal-title":"IEEE Trans. Med. Imag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1687-6180-2011-124","article-title":"Automated target tracking and recognition using coupled view and identity manifolds for shape representation","volume":"2011","author":"Venkataraman","year":"2011","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lee, C., and Elgammal, A. (2007, January 14\u201321). Modeling View and Posture Manifolds for Tracking. Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4409030"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Prisacariu, V., and Reid, I. (2011, January 20\u201325). Nonlinear shape manifolds as shape priors in level set segmentation and tracking. Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995687"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Prisacariu, V., and Reid, I. (2011, January 6\u201313). Shared shape spaces. Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126547"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10124","DOI":"10.3390\/s140610124","article-title":"Joint target tracking, recognition and segmentation for infrared Imagery using a shape manifold-based level set","volume":"14","author":"Gong","year":"2014","journal-title":"Sensors"},{"key":"ref_38","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Perth, WA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1117\/12.243153","article-title":"Automatic target recognition using a multilayer convolution neural network","volume":"2755","author":"Mirelli","year":"1996","journal-title":"Proc. SPIE"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/83.704305","article-title":"Automatic target recognition using a feature-decomposition and data-decomposition modular neural network","volume":"7","author":"Wang","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chan, L., Nasrabadi, N., and Mirelli, V. (1996, January 18\u201320). Multi-stage target recognition using modular vector quantizers and multilayer perceptrons. San Francisco, CA, USA.","DOI":"10.1109\/CVPR.1996.517062"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1117\/1.602324","article-title":"Automatic target recognition using vector quantization and neural networks","volume":"38","author":"Chan","year":"1999","journal-title":"Opt. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"13778","DOI":"10.3390\/s140813778","article-title":"Automated detection and recognition of wildlife using thermal cameras","volume":"14","author":"Christiansen","year":"2014","journal-title":"Sensors"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust face recognition via sparse representation","volume":"31","author":"Wright","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","unstructured":"Chiang, C.K., Su, T.F., Yen, C., and Lai, S.H. (2013, January 22\u201326). Multi-attribute sparse representation with group constraints for face recognition under different variations. Shanghai, China."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"9451","DOI":"10.3390\/s140609451","article-title":"Sparse representation for infrared dim target detection via a discriminative over-complete dictionary learned online","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"11245","DOI":"10.3390\/s140611245","article-title":"Robust pedestrian tracking and recognition from FLIR video: A unified approach via sparse coding","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1117\/12.395067","article-title":"Bayesian filtering for tracking pose and location of rigid targets","volume":"4052","author":"Srivastava","year":"2000","journal-title":"Proc. SPIE"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s11263-006-9965-3","article-title":"Three-dimensional shape knowledge for joint image segmentation and pose tracking","volume":"73","author":"Rosenhahn","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Prisacariu, V., and Reid, I. (2009, January 7\u201310). PWP3D: Real-time segmentation and tracking of 3D objects. London, UK.","DOI":"10.5244\/C.23.47"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1117\/12.44888","article-title":"Automatic object recognition: critical issues and current approaches","volume":"1471","author":"Sadjadi","year":"1991","journal-title":"Proc. SPIE"},{"key":"ref_53","first-page":"195","article-title":"Real-time radar image understanding: A machine intelligence approach","volume":"5","author":"Aull","year":"1992","journal-title":"Linc. Lab. J."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Liebelt, J., Schmid, C., and Schertler, K. (2008, January 23\u201328). Viewpoint-independent object class detection using 3D Feature Maps. Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587614"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Khan, S., Cheng, H., Matthies, D., and Sawhney, H. (2010, January 13\u201318). 3D model based vehicle classification in aerial imagery. San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539835"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Toshev, A., Makadia, A., and Daniilidis, K. (2009, January 20\u201325). Shape-based object recognition in videos using 3D synthetic object models. Miami Beach, FL, USA.","DOI":"10.1109\/CVPR.2009.5206803"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"14106","DOI":"10.3390\/s140814106","article-title":"Relevance-based template matching for tracking targets in FLIR imagery","volume":"14","author":"Paravati","year":"2014","journal-title":"Sensors"},{"key":"ref_58","first-page":"69410B-1","article-title":"Human target identification and automated shape based target recognition algorithms using target silhouette","volume":"6941","author":"Chari","year":"2008","journal-title":"Proc. SPIE"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1117\/12.540892","article-title":"Automatic target recognition using multi-view morphing","volume":"5426","author":"Xiao","year":"2004","journal-title":"Proc. SPIE"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s00138-011-0400-6","article-title":"Learning the shape manifold to improve object recognition","volume":"24","author":"Chahooki","year":"2013","journal-title":"Mach. Vis. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/978-3-540-88688-4_61","article-title":"Robust real-time visual tracking using pixel-wise posteriors","volume":"Volume 5303","author":"Forsyth","year":"2008","journal-title":"Computer Vision\u2014ECCV 2008"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11263-006-8711-1","article-title":"A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape","volume":"72","author":"Cremers","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Jebara, T. (2003, January 13\u201316). Images as bags of pixels. Nice, France.","DOI":"10.1109\/ICCV.2003.1238352"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1109\/TPAMI.2006.161","article-title":"Dynamical statistical shape priors for level set-based tracking","volume":"28","author":"Cremers","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1007\/10983652_21","article-title":"Evaluation of particle swarm optimization effectiveness in classification","volume":"Volume 3849","author":"Bloch","year":"2006","journal-title":"Fuzzy Logic and Applications"},{"key":"ref_67","first-page":"1","article-title":"Dynamic particle swarm optimization for multimodal function","volume":"1","author":"Omranpour","year":"2012","journal-title":"IAES Int. J. Artif. Intell."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4111","DOI":"10.3390\/s140304111","article-title":"Nonlinearity analysis and parameters optimization for an inductive angle sensor","volume":"14","author":"Ye","year":"2014","journal-title":"Sensors"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"10361","DOI":"10.3390\/s140610361","article-title":"Defect profile estimation from magnetic flux leakage signal via efficient managing particle swarm optimization","volume":"14","author":"Han","year":"2014","journal-title":"Sensors"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Evers, G., and Ben Ghalia, M. (2009, January 11\u201314). Regrouping particle swarm optimization: A new global optimization algorithm with improved performance consistency across benchmarks. San Antonio, TX, USA.","DOI":"10.1109\/ICSMC.2009.5346625"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.asoc.2011.08.037","article-title":"A new gradient based particle swarm optimization algorithm for accurate computation of global minimum","volume":"12","author":"Noel","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/3-540-47969-4_30","article-title":"Multilinear analysis of image ensembles: TensorFaces","volume":"Volume 2350","author":"Heyden","year":"2002","journal-title":"Computer Vision\u2014ECCV 2002"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1013708715892","article-title":"Relaxed steepest descent and Cauchy-Barzilai-Borwein method","volume":"21","author":"Raydan","year":"2002","journal-title":"Comput. Optim. Appl."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1016\/j.patrec.2005.11.005","article-title":"Efficient adaptive density estimation per image pixel for the task of background subtraction","volume":"27","author":"Zivkovic","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Yu, L., Fan, G., Gong, J., and Havlicek, J. (2013, January 15\u201318). Simultaneous target recognition, segmentation and pose estimation. Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738547"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1364\/JOSAA.7.002032","article-title":"Contrast in complex images","volume":"7","author":"Peli","year":"1990","journal-title":"J. Opt. Soc. Am."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1109\/TPAMI.2008.182","article-title":"A statistical approach to material classification using image patch exemplars","volume":"31","author":"Varma","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","article-title":"Contour detection and hierarchical image segmentation","volume":"33","author":"Arbelaez","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_79","first-page":"409","article-title":"Comparison and evaluation of segmentation techniques for subcortical structures in brain MRI","volume":"Volume 5241","author":"Metaxas","year":"2008","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2008"},{"key":"ref_80","unstructured":"Ge, F., Wang, S., and Liu, T. (2006, January 17\u201322). Image-segmentation evaluation from the perspective of salient object extraction. New York, NY, USA."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1109\/TPAMI.2011.231","article-title":"CPMC: Automatic object segmentation using constrained parametric min-cuts","volume":"34","author":"Carreira","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.cviu.2013.10.002","article-title":"Joint View-Identity Manifold for Infrared Target Tracking and Recognition","volume":"118","author":"Gong","year":"2014","journal-title":"Comput. Vis. Image Underst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/5\/10118\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:45:33Z","timestamp":1760215533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/5\/10118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,4,29]]},"references-count":82,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2015,5]]}},"alternative-id":["s150510118"],"URL":"https:\/\/doi.org\/10.3390\/s150510118","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,4,29]]}}}