{"id":11,"date":"2015-11-03T15:53:38","date_gmt":"2015-11-03T20:53:38","guid":{"rendered":"https:\/\/test.eng.ufl.edu\/faculty-site\/?page_id=11"},"modified":"2026-04-06T17:12:41","modified_gmt":"2026-04-06T21:12:41","slug":"publications","status":"publish","type":"page","link":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\n

Under Review<\/a> | 2026<\/a> | 2025<\/a> | 2024<\/a> | 2023<\/a> | 2022<\/a> | 2021<\/a> | 2020<\/a> | 2019<\/a> | 2018<\/a> | 2017<\/a> | 2016<\/a> | 2015<\/a> | 2014<\/a> | 2013<\/a> | 2012<\/a> | 2011<\/a> | 2010<\/a> | 2009<\/a> | 2008<\/a> | 2007<\/a> | 2006<\/a> | 2004<\/a> | Preprint<\/a><\/p>\n\n\n\n

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Under Review \/ In Preparation<\/h2>\n\n\n\n

A. Hampton, D. Stewart, A. Zare, and J. Keller, “Comparison of unsupervised quantitative metrics for superpixel segmentation,” Computer Vision and Image Understanding, Elsevier, 2025. Under Review<\/p>\n\n\n\n

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2026<\/h2>\n\n\n\n

A. Hampton, K. Chhapariya, Y. Itoh, A. Zare, and M. Parente, \u201cIntroducing a Ground-Truthed Multi-Resolution Drone-Based Hyperspectral Dataset for Unmixing Tasks,\u201d<\/a> in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS)<\/em>. 2026. ACCEPTED!<\/p>\n\n\n\n

M. Zhou and A. Zare, \u201cTask-Guided Multi-Annotation Triplet Learning for Remote Sensing Representations,\u201d<\/a> in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS)<\/em>. 2026. ACCEPTED!<\/p>\n\n\n\n

M. Zhou and A. Zare, \u201cBeyond Task-Driven Features for Object Detection,\u201d<\/a> in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS)<\/em>. 2026. ACCEPTED!<\/p>\n\n\n\n

R. Dutt, G. Zheng, Z. C. Curran, R. Fick, N. Nezlin, M. Medina, W. He, P. M. DiGiacomo, A. Zare, and Z. Jiang, \u201cA machine learning approach for chlorophyll-a estimation in coastal waters from top-of-atmosphere VIIRS satellite data,\u201d<\/a> IEEE Transactions on Geoscience and Remote Sensing<\/em>, 2026.<\/p>\n\n\n\n

H. Schuhl, K. E. Brown, H. Sheng, P. K. Bhatt, J. Gutierrez, D. Schneider, A. L. Casto, L. Acosta-Gamboa, J. G. Ballenger, F. Barbero, J. Braley, A. M. Brown, L. Chavez, S. Cunningham, M. Dilhara, A. M. Dimech, J. G. Duenwald, A. Fischer, J. M. Gordon, C. Hendrikse, G. L. Hernandez, J. G. Hodge, M. Huber, B. M. Hurr, S. Jarolmasjed, K. M. Jimenez, S. Kenney, G. Konkel, A. Kutschera, S. Lama, M. Lohbihler, A. Lorence, C. Luebbert, N. Ly, H. K. Manching, A. Marrano, S. Meerdink, N. M. Miklave, P. Mudrageda, K. M. Murphy, J. D. Peery, R. Pierik, S. Polydore, C. Robey, T. Rogers, T. J. Schultz, E. Seigel, D. Srivastava, S. Summerer, J. Sumner, C. Teng, A. E. Thompson, J. C. Tovar, T. van Daalen, M. Watson, J. J. Wheeler, M. C. Wilson, K. R. Ying, A. Zare, Y. Zhou, M. A. Gehan, and N. Fahlgren, \u201cPlantCV v4: Image analysis software for high-throughput plant phenotyping,\u201d<\/a> The Plant Phenome Journal<\/em>, vol. 9, no. 1, p. e70065, 2026, doi: 10.1002\/ppj2.70065.<\/p>\n\n\n\n

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2025<\/h2>\n\n\n\n

P. D. Milligan, J. Rossiter, A. Zare, T. M. Palmer, J. Lemboi, G. M. Mizell, J. Mosiany, C. Riginos, J. R. Goheen, and E. G. Pringle, \u201cMutualism, herbivory, and invasive ants as seasonally dependent drivers of root surface area in a foundational savanna ant\u2013plant,\u201d<\/a> New Phytologist<\/em>, vol. 249, no. 2, pp. 777\u2013791, 2026.<\/p>\n\n\n\n

J. Peeples, S. A. Kharsa, L. Saleh and A. Zare, “Histogram Layers for Neural \u201cEngineered\u201d Features,”<\/a> in IEEE Transactions on Artificial Intelligence,<\/em> July 2025, doi: 10.1109\/TAI.2025.3593445.<\/p>\n\n\n\n

C. Messina, J. Garcia-Abadillo, O. Powell, S. Tomura, A. Zare, B. Ganapathysubramanian, and M. Cooper, \u201cToward a general framework for AI-enabled prediction in crop improvement,\u201d<\/a> Theor. Appl. Genet., vol. 138, no. 7, p. 151, 2025.<\/p>\n\n\n\n

S. J. Graves, R. Chowdhry, M. Zhou, I. Harmon, B. Weinstein, S. K. M. Ernest, A. Zare, E. P. White, and S. A. Bohlman, \u201cFacilitating macrosystem biology with organismal-scale airborne remote sensing: Challenges and opportunities,\u201d<\/a> Funct. Ecol., 2025.<\/p>\n\n\n\n

M. Zhou, A. Dutt, and A. Zare, “Multi-Task Learning with Multi-Annotation Triplet Loss for Improved Object Detection,”<\/a> in IGARSS 2025 – 2025 IEEE International Geoscience and Remote Sensing Symposium,<\/em> 2025.<\/p>\n\n\n\n

A. Dutt, “A MultiModal Alignment Network for Domain Translation and Fusion,”<\/a> Ph.D Thesis, Gainesville, FL, 2025.<\/p>\n\n\n\n

M. Cook, \u201cCompetency Awareness Using Null Space Projections,\u201d<\/a> Ph.D Thesis, Gainesville, FL, 2025.<\/p>\n\n\n\n

S. J. Chang, \u201cEnhancing Semantic Segmentation Using Locally Learned Histogram Features,\u201d<\/a> Ph.D Thesis, Gainesville, FL, 2025.<\/p>\n\n\n\n

X. Guo, A. Zare, L. Anthony, and F. B. Fritschi, “Interactive segmentation with prototype learning for few-shot root annotation,”<\/a> IEEE Transactions on Geoscience and Remote Sensing,<\/em> pp. 1\u20131, 2025, doi: 10.1109\/TGRS.2025.3556799.<\/p>\n\n\n\n

A. K. Sharma, L. Zotarelli, A. Zare, and L. K. Sharma, “Automated potato tuber mass estimation and grading with multiangle 2D images,”<\/a> Smart Agricultural Technology<\/em>, vol. 10, p. 100832, 2025. doi: 10.1016\/j.atech.2025.100832.<\/p>\n\n\n\n

M. Lary, R. Samuelson, A. Wilentz, A. Zare, M. Klawonn, and J. P. Fairbanks, \u201cDiagrammaticLearning: A graphical language for compositional training regimes,\u201d<\/a> arXiv preprint arXiv:2501.01515<\/em>, 2025.<\/p>\n\n\n\n

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2024<\/h2>\n\n\n\n

D.-Y. Kim, F. Getachew, B. L. Tillman, B. Zurweller, W. M. Hammond, A. Zare, R. Holton, and Z. Brym, \u201cDeveloping statistical models of aflatoxin risk in peanuts using historical weather data,\u201d<\/a> Agronomy Journal<\/em>, vol. 116, no. 5, pp. 2346\u20132361, 2024.<\/p>\n\n\n\n

J. J. Pitts, \u201cThe Future of Coffee and Peanut Cultivation Under Further Climate Change: Drought and Environmental Impacts on Crop Physiology, Resilience, Disease Formation and Stress Detection,\u201d<\/a> Ph.D Thesis, Gainesville, FL, 2024.<\/p>\n\n\n\n

B. G. Weinstein, S. Marconi, A. Zare, S. A. Bohlman, A. Singh, S. J. Graves, L. Magee, D. J. Johnson, S. Record, V. E. Rubio, N. G. Swenson, P. Townsend, T. T. Veblen, R. A. Andrus, and E. P. White, “Individual canopy tree species maps for the National Ecological Observatory Network,”<\/a> PLoS Biology, vol. 22, no. 7, p. e3002700, Jul. 2024, doi: 10.1371\/journal.pbio.3002700.<\/p>\n\n\n\n

S. J. Chang, R. Chowdhry, Y. Song, T. Mejia, A. Hampton, S. Kucharski, T. M. Sazzad, Y. Zhang, S. J. Koppal, C. H. Wilson, and others, “HyperPRI: A dataset of hyperspectral images for underground plant root study,”<\/a> Computers and Electronics in Agriculture<\/em>, vol. 225, p. 109307, 2024.<\/p>\n\n\n\n

A. K. Sahoo, H. Geldimuradov, A. Zygala, Y. Cui, M. Lotfollahi, K. Gong, K. Smith, A. Zare, and S. M. Weisberg, Deep learning approaches to associate neural structure with spatial navigation function. Poster presentation at the 65th Annual Meeting of the Psychonomic Society. New York, NY. 2024.<\/p>\n\n\n\n

R. Gupta, S. K. Pothapragada, W. Xu, P. K. Goel, M. A. Barrera, M. S. Saldanha, J. B. Harley, K. T. Morgan, A. Zare, and L. Zotarelli, “Estimating soil mineral nitrogen from data-sparse field experiments using crop model-guided deep learning approach,”<\/a> Computers and Electronics in Agriculture, vol. 225, p. 109355, 2024.<\/p>\n\n\n\n

Z. Tian, J. Upchurch, G. A. Simon, J. Dubeux, A. Zare, C. Zhao, and J. B. Harley, “Quantifying Heterogeneous Ecosystem Services with Multi-Label Soft Classification,”<\/a> in IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium, pp. 427\u2013431, 2024.<\/p>\n\n\n\n

Y. Song, G. Sapes, S. Chang, R. Chowdhry, T. Mejia, A. Hampton, S. Kucharski, T. Sazzad, Y. Zhang, S Koppal, and A. Zare, “Hyperspectral signals in the soil: plant-soil hydraulic connections as mechanisms of drought tolerance and rapid recovery,”<\/a> in Plant, Cell & Environment, 2024.<\/p>\n\n\n\n

B. G. Weinstein, S. Marconi, A. Zare, S. A. Bohlman, A. Singh, S. J. Graves, L. Magee, D. J. Johnson, S. Record, V. E. Rubio, and others, “Individual canopy tree species maps for the National Ecological Observatory Network,”<\/a> PLOS Biology, vol. 22, no. 7, p. e3002700, 2024.<\/p>\n\n\n\n

S. Meerdink, D. Hiatt, S. Flory, A. Zare, “Dealing with imperfect data for invasive species detection using multispectral imagery,”<\/a> in Ecological informatics<\/em>, vol. 79, p. 102432, 2024, doi: doi.org\/10.1016\/j.ecoinf.2023.102432.<\/p>\n\n\n\n

R. Castillo-Argaez, G. Sapes, N. Mallen, A. Lippert, G. John, A. Zare and W. Hammond, “Spectral ecophysiology: hyperspectral pressure–volume curves to estimate leaf turgor loss,”<\/a> in New Phytologist<\/em>, vol. 242, no. 3, pp. 935-946, 2024, doi: 10.1111\/nph.19669.<\/p>\n\n\n\n

X. Guo, Q. Li, S. Morrison-Smith, L. Anthony, A. Zare, Y. Song, “Elicitating Challenges and User Needs Associated with Annotation Software for Plant Phenotyping,”<\/a> in Proceedings of the 29th International Conference on Intelligent User Interfaces<\/em>, pp. 431-443, 2024, doi: doi.org\/10.1145\/3640543.3645178.<\/p>\n\n\n\n

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2023<\/h2>\n\n\n\n

X. Guo, \u201cInteractive Segmentation With Deep Metric Learning,\u201d<\/a> Ph.D Thesis, Gainesville, FL, 2023.<\/p>\n\n\n\n

Y. Cui, \u201cWeakly Supervised Point Cloud Semantic Segmentation With Graph Convolutional Networks,\u201d<\/a> Ph.D Thesis, Gainesville, FL, 2023.<\/p>\n\n\n\n

W. Xu, \u201cDeep Morph-Convolutional Neural Network: Combining Morphological Transform and Convolution in Deep Neural Networks,\u201d<\/a> Ph.D Thesis, Gainesville, FL, 2023.<\/p>\n\n\n\n

H. D. Smith, J. C. B. Dubeux, A. Zare, and C. H. Wilson, \u201cAssessing transferability of remote sensing pasture estimates using multiple machine learning algorithms and evaluation structures,\u201d<\/a> Remote Sensing, vol. 15, no. 11, p. 2940, 2023.<\/p>\n\n\n\n

M. Cook, A. Khoury, A. Zare and P. Gader, “Null Space Analysis for Detecting Unknown Objects During Automatic Target Recognition Tasks in Sonar Data,”<\/a> OCEANS 2023 – MTS\/IEEE U.S. Gulf Coast, Biloxi, MS, USA, 2023, pp. 1-5, doi: 10.23919\/OCEANS52994.2023.10337218.<\/p>\n\n\n\n

C. H. McCurley and A. Zare, “Segmentation Pseudo-label Generation using the Multiple Instance Learning Choquet Integral,”<\/a> in IEEE Transactions on Fuzzy Systems, doi: 10.1109\/TFUZZ.2023.3338049.<\/p>\n\n\n\n

S. Graves, S. Marconi, D. Stewart, I. Harmon, B. G. Weinstein, Y. Kanazawa, V. M Scholl, M. B. Joseph, J. McClinchy, L. Browne, M. K. Sullivan, S. Estrada-Villegas, D. Zhe Wang, A. Singh, S. A. Bohlman, A. Zare, E. P. White, “Data science competition for cross-site delineation and classification of individual trees from airborne remote sensing data.”<\/a> PeerJ, 2023, doi: 10.7717\/peerj.16578.<\/p>\n\n\n\n

X. Guo, C. Tseung, A. Zare, T. Liu. “Hyperspectral image analysis for the evaluation of chilling injury in avocado fruit during cold storage,”<\/a> Postharvest Biology and Technology, vol. 206, p.112558, ISSN 0925-5214, 2023.<\/p>\n\n\n\n

B. G. Weinstein, S. Marconi, S. J. Graves, A. Zare, A. Singh, S. A. Bohlman, L. Magee, D. J. Johnson, P. A. Townsend, E. P. White. “Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model.”<\/a> Remote Sensing in Ecology and Conservation, vol. 9, pp. 656-670, 2023, doi: 10.1002\/rse2.335.<\/p>\n\n\n\n

D. Stewart, A. Kreulach, S. F. Johnson and A. Zare, “Image-to-Height Domain Translation for Synthetic Aperture Sonar,”<\/a> in IEEE Transactions on Geoscience and Remote Sensing<\/em>, vol. 61, pp. 1-13, 2023, Art no. 4201113, doi: 10.1109\/TGRS.2023.3236473.<\/p>\n\n\n\n

J. Peeples, W. Xu, R. Gloaguen, D. Rowland, A. Zare, and Z. Brym, “Spatial and Texture Analysis of Root System distribution with Earth mover\u2019s Distance (STARSEED),”<\/a> in Plant Methods, vol. 19, pp. 1-15, ISSN 1746-4811. 2023, Art no. 2, doi: 10.1186\/s13007-022-00974-z.<\/p>\n\n\n\n

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2022<\/h2>\n\n\n\n

X. Guo, Y. Ahlawat, A. Zare and T. Liu, “Evaluation of Postharvest Senescence in Broccoli via Hyperspectral Imaging.”<\/a> Plant Phenomics. 2022. doi: 10.34133\/2022\/9761095.<\/p>\n\n\n\n

A. Starke, K. Nagaraj, C. Ruben, N. Aljohani, S. Zou, A. Bretas, J. McNair and A. Zare, “Cross-Layered Distributed Data-Driven Framework for Enhanced Smart Grid Cyber-Physical Security.”<\/a> in IET Smart Grid. vol. 5. issue 6. pages 398-416. ISSN 2515-2947. 2022.<\/p>\n\n\n\n

I. Harmon, S. Marconi, B. Weinstein, S. Graves, D.Z. Wang, S. Bohlman, A. Zare, A. Singh, and E. White, “Injecting Domain Knowledge Into Deep Neural Networks for Tree Crown Delineation,”<\/a> in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-19, 2022, Art no. 4415419, doi: 10.1109\/TGRS.2022.3216622.<\/p>\n\n\n\n

A. Dutt, A. Zare and P. Gader, “Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion With Missing Data,”<\/a> in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 9439-9456, 2022, doi: 10.1109\/JSTARS.2022.3217485.<\/p>\n\n\n\n

C. McCurley, “Discriminative Feature Learning with Imprecise, Uncertain, and Ambiguous Data,”<\/a> Ph.D Thesis, Gainesville, FL, 2022.<\/p>\n\n\n\n

J. Peeples, “Connecting the Past and the Present : Histogram Layers for Texture Analysis,”<\/a> Ph.D Thesis, Gainesville, FL, 2022.<\/p>\n\n\n\n

D. Stewart, “Domain Translation and Image Registration for Multi-Look Synthetic Aperture Sonar Scene Understanding,”<\/a> Ph.D Thesis, Gainesville, FL, 2022.<\/p>\n\n\n\n

R. Fick, “Robust GANs for Semi-Supervised Classification,”<\/a> Ph.D Thesis, Gainesville, FL, 2022.<\/p>\n\n\n\n

S. Marconi, B.G. Weinstein, S. Zou, S.A. Bohlman, A. Zare, A. Singh, D. Stewart, I. Harmon, A. Steinkraus, and E.P. White, “Continental-scale hyperspectral tree species classification in the United States National Ecological Observatory Network.”<\/a> Remote Sensing of Environment, vol. 282, p.113264., ISSN 0034-4257, 2022.<\/p>\n\n\n\n

C. McCurley, D. Rodriguez, C. Trousdale, A. Stevens, A. Baldino, E. Li, I. Perlmutter, and A. Zare “Bag-level classification network for infrared target detection”<\/a>, in Proc. SPIE 12096, Automatic Target Recognition XXXII, 1209603, doi: 10.1117\/12.2618325, 2022.<\/p>\n\n\n\n

Maggie M. Hantak, Robert P. Guralnick, A. Zare, Brian J. Stucky, “Computer vision for assessing species color pattern variation from web-based community science images”<\/a>, pp. 104784, ISSN 2589-0042, https:\/\/doi.org\/10.1016\/j.isci.2022.104784, in iScience, 2022.<\/p>\n\n\n\n

J. Peeples, J. Jameson, N. Kotta, J. Grasman, W. Stoppel, and A. Zare, \u201cJointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation\u201d<\/a> in BME Frontiers, vol. 2022, doi: 10.34133\/2022\/9854084.<\/p>\n\n\n\n

S.K. Meerdink, J. Bocinsky, A. Zare, N. Kroeger, C. H. McCurley, D. Shats and P.D. Gader, “Multi-Target Multiple Instance Learning for Hyperspectral Target Detection,”<\/a> in IEEE Transaction on Geoscience and Remote Sensing (TGRS),<\/em> vol. 60, pp. 1-14, Art no. 5502814, doi: 10.1109\/TGRS.2021.3060966, 2022.<\/p>\n\n\n\n

J. Peeples, S. Walker, C. McCurley, A. Zare, J. Keller and and W. Xu, “Divergence Regulated Encoder Network for Joint Dimensionality Reduction and Classification.”<\/a>, in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 3511305, doi: 10.1109\/LGRS.2022.3156532.<\/p>\n\n\n\n

W. Xu, G. Yu, Y. Cui, R. Gloaguen, A. Zare, J. Bonnette, J. Reyes-Cabrera, A. Rajurkar, D. Rowland, R. Matamala, J. Jastrow, T. Juenger, and F. Fritschi. \u201cPRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study.\u201d<\/a> in AI for Agriculture and Food Systems (AIAFS)Workshops at the AAAI conference on artificial intelligence. February, 2022.<\/p>\n\n\n\n

J. Peeples, C. McCurley, S. Walker, D. Stewart and A. Zare, “Learnable Adaptive Cosine Estimator (LACE) for Image Classification.<\/a>” in WACV, 2022.<\/p>\n\n\n\n

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2021<\/h2>\n\n\n\n

G. Yu, “Weakly Supervised Image Segmentation with Multiple Instance Learning Neural Network,”<\/a> Ph.D. Thesis, Gainesville, FL, 2021.<\/p>\n\n\n\n

J. Peeples, W. Xu and A. Zare, “Histogram Layers for Texture Analysis.”<\/a> in IEEE Transactions on Artificial Intelligence, DOI 10.1109\/TAI.2021.3135804, 2021.<\/p>\n\n\n\n

D. Stewart, A. Zare, S. Marconi, B. Weinstein, E. White, S. Graves, S. Bohlman and A. Singh, “RandCrowns: A Quantitative Metric for Imprecisely Labeled Tree Crown Delineation.”<\/a>, vol. 14, pp. 11229-11239, DOI 10.1109\/JSTARS.2021.3122345, 2021.<\/p>\n\n\n\n

T. Zou, N. Aljohani, K. Nagaraj, S. Zou, C. Ruben, A. Bretas, A. Zare and J. McNair, “A Network Parameter Database FDI Correction Physics-based Model: A Machine Learning Synthetic Measurement based Approach.”<\/a> in Applied Sciences Journal, vol. 11, 2021.<\/p>\n\n\n\n

V. R. Merenda, J. Ruiz-Munoz, A. Zare and R. C. Chebel, “Predictive models to identify Holstein cows at risk of metritis and clinical cure and reproductive\/productive failure following antimicrobial treatment,”<\/a> in Preventive Veterinary Medicine<\/em>, vol. 194, pp. 105431, 2021.<\/p>\n\n\n\n

B. Weinstein, S. Graves, S. Marconi, A. Singh, A. Zare, D. Stewart, S. Bohlman and E. P. White, “A benchmark dataset for individual tree crown delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network,”<\/a> in PLOS Computational Biology, vol. 2, pp. 1-18, DOI 10.1371\/journal.pcbi.1009180, 2021.<\/em><\/p>\n\n\n\n

S. Zou, “Classification with Multi-Imprecise Labels,”<\/a> Ph.D. Thesis, Gainesville, FL, 2021.<\/p>\n\n\n\n

C. Jiao, C. Chen, S. Gou, D. Hai, B. Su, M. Skubic, L. Jiao, A. Zare and K.C. Ho, “Non-Invasive Heart Rate Estimation from Ballistocardiograms using Bidirectional LSTM Regression,”<\/a> in IEEE Journal of Biomedical and Health Informatics,<\/em> 2021.<\/p>\n\n\n\n

R. A. Borsoi, T. Imbiriba, J.C.M. Bermudez, C. Richard, J. Chanussot, L. Drumetz, J. Tourneret, A. Zare and C. Jutten, “Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review,”<\/a> in IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 4, pp. 223-270, Dec. 2021, doi: 10.1109\/MGRS.2021.3071158.<\/em><\/p>\n\n\n\n

D. Prioleau, K. Alikhademi, A. Roberts, J. Peeples, A. Zare and J. Gilbert, “Application of Divisive Clustering for Reducing Bias in Imbalanced Data,”<\/a> in 2021 International Conference on Machine Learning and Data Mining (MLDM), P-ISSN 1864-9734, E-ISSN 2699-5220, ISBN 978-3-942952-81-1, pp. 115-129, 2021<\/em>.<\/p>\n\n\n\n

D. Stewart, A. Hampton, A. Zare, J. Dale and J. Keller, “The Weakly-Labeled Rand Index,”<\/a> in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2313-2316, doi: 10.1109\/IGARSS47720.2021.9553182, 2021.<\/em><\/p>\n\n\n\n

S. Walker, J. Peeples, J. Dale, J. Keller and A. Zare, “Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery,”<\/a> in IEEE International Geoscience and Remote Sensing Symposium (IGARSS)<\/em>, pp. 2835-2838, doi: 10.1109\/IGARSS47720.2021.9554901, 2021.<\/p>\n\n\n\n

K. Nagaraj, N. Aljohani, S. Zou, T. Zou, A. Bretas, J. McNair and A. Zare, \u201cSmart FDI Attack Design and Detection with Data Transmutation Framework for Smart Grids,\u201d in 2021 IEEE PES General Meeting<\/em>, July 25-29, 2021, Washington, DC, USA.<\/p>\n\n\n\n

B. G. Weinstein, S. Marconi, S. A. Bohlman, A. Zare, A. Singh, S. J. Graves and E. P. White. “A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network,”<\/a> in eLife,<\/em> 2021.<\/p>\n\n\n\n

K. Nagaraj, N. Aljohani, S. Zou, C. Ruben,  A. Bretas, A. Zare and J. McNair, “State Estimator and Machine Learning Analysis of Residual Differences to Detect and Identify FDI and Parameter Errors in Smart Grids,”<\/a> in 2020 North American Power Symposium (NAPS), 2021<\/em>.<\/p>\n\n\n\n

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2020<\/h2>\n\n\n\n

C. Jiao, X. Du, A. Zare, “Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis.”<\/a> in: Prasad, S., Chanussot, J. (eds) Hyperspectral Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, Cham, 2020.<\/p>\n\n\n\n

G. Yu, A. Zare, W. Xu, R. Matamala, J. Reyes-Cabrera, F. B. Fritschi and T. E. Juenger, “Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM,”<\/a> in the 16th European Conference on Computer Vision (ECCV) Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP 2020),<\/em> 2020.<\/p>\n\n\n\n

A. Seethepalli, H. Guo, X. Liu, M. Griffiths, H. Almtarfi, Z. Li, S. Liu, A. Zare, F. Fritschi, E. Blancaflor, X. Ma, and L. York, \u201cRhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping<\/a>,\u201d in Plant Phenomics<\/em>,vol. 2020, Article ID 3074916, 15 pages, 2020.<\/p>\n\n\n\n

K. Nagaraj, S. Zou, C. Ruben, S. C. Dhulipala, A. Starke, A. Bretas, A. Zare and J. McNair, “Ensemble CorrDet with Adaptive Statistics for Bad Data Detection<\/a>,” in IET Smart Grid.<\/em>  pp. 572 \u2013 580, vol. 3, 2020.<\/p>\n\n\n\n

C. A. Smith, Y. Cui, B. D. Miler, D. D. Keiser, A. Zare and A. Aitkaliyeva, “New approach for measuring interconnectivity of fission gas pores in nuclear fuels from 2D micrographs,”<\/a> in Journal of Materials Science,<\/em> 2020.<\/p>\n\n\n\n

M. Cook, A. Zare and P. Gader, “Outlier Detection through Null Space Analysis of Neural Networks,<\/a>” presented at the 37th International Conference on Machine Learning<\/em> (ICML 2020) Workshop on Uncertainty and Robustness in Deep Learning (UDL),<\/em> Virtual Conference, 2020.<\/p>\n\n\n\n

J.F. Ruiz-Munoz, J.K. Nimmagadda, T.G. Dowd, J.E. Baciak and A. Zare, “Super Resolution for Root Imaging,”<\/a> in Applications in Plant Sciences (APPS), vol. 8, num. 7, pp. e11374, 2020<\/em>.<\/p>\n\n\n\n

P. S. Soltis, <\/span> G. Nelson,<\/span> A. Zare<\/span>, and E. K. Meineke, “<\/span>Plants meet machines: Prospects in machine learning for plant biology<\/a>,<\/span>” in Applications in Plant Sciences,<\/em> vol. 8, num.<\/span> 6, pp.<\/span> e11371, 2020.<\/p>\n\n\n\n

G. Yu, A. Zare, H. Sheng, R. Matamala, J. Reyes-Cabrera, F. B. Fritschi and T.E. Juenger, \u201cRoot Identification in Minirhizotron Imagery with Multiple Instance Learning<\/a>,\u201d in Machine Vision and Applications, <\/em>vol. 31, 2020.<\/p>\n\n\n\n

W. Xu, G. Yu, A. Zare, B. Zurweller, D. Rowland, J. Reyes-Cabrera, F. Fritschi, R. Matamala and T. Juenger, \u201cOvercoming Small Minirhizotron Datasets Using Transfer Learning<\/a>,\u201d in Computers and Electronics in Agriculture, vol. 175, 2020<\/em>.<\/p>\n\n\n\n

B. Weinstein, S. Marconi, S. Bohlman, A. Zare, E. White, “Cross-site learning in deep learning RGB tree crown detection,”<\/a> in Ecological Informatics, vol. 56, 2020<\/em>.<\/p>\n\n\n\n

X. Du and A. Zare, \u201cMulti-resolution Multi-modal Sensor Fusion For Remote Sensing Data with Label Uncertainty<\/a>,\u201d in IEEE Trans. on Geoscience and Remote Sensing (TGRS), vol. 58, no. 4, pp. 2755-2769, April 2020.<\/em><\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2019<\/h2>\n\n\n\n

H. Sheng, \u201cSwitchgrass Genotype Classification using Hyperspectral Imagery,\u201d<\/a> M.Sc. Thesis, Gainesville, FL, 2019.<\/p>\n\n\n\n

P. Lyons, \u201cAnomaly and Target Detection in Synthetic Aperture Sonar,\u201d<\/a> M.Sc. Thesis, Gainesville, FL, 2019.<\/p>\n\n\n\n

C. Ruben, S. Dhulipala, K. Nagaraj, S. Zou. A. Starke, A. Bretas, A. Zare, and J. McNair, “Hybrid data-driven physics model-based framework for enhanced cyber-physical smart grid security,”<\/a> in IET Smart Grid Special Issue: Machine Learning in Power Systems<\/em>. Dec 2019.<\/p>\n\n\n\n

X. Du, A. Zare and D. Anderson, “Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels,”<\/a> The 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI). Xiamen, China. Dec. 2019. pp. 1154-1162.<\/p>\n\n\n\n

S. Meerdink, J. Bocinsky, E. Wetherley, A. Zare, C. McCurley, and P. Gader, \u201cDeveloping Spectral Libraries Using Multiple Target Multiple Instance Adaptive Cosine\/Coherence Estimator,\u201d<\/a>  10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1-5, Sept. 2019.<\/p>\n\n\n\n

R. Fick, P. Gader, A. Zare, and S. Meerdink, “Temporal mapping of hyperspectral data”<\/a>, 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1-4, Sept. 2019.<\/p>\n\n\n\n

S. Zou, Y. Tseng, A. Zare, D. Rowland, B. Tillman and S. Yoon, \u201cPeanut Maturity Classification using Hyperspectral Imagery,\u201d<\/a> in Biosystems Engineering<\/em>, vol. 188, pp. 165-177, 2019.<\/p>\n\n\n\n

S. K. Meerdink, D. A. Roberts, K. L. Roth, J. Y. King, P. D. Gader, and A. Koltunov, “Classifying California plant species temporally using airborne hyperspectral imagery,”<\/a> in Remote Sensing of Environment<\/em>, vol. 232 (111308), Oct 2019.<\/p>\n\n\n\n

S. K. Meerdink, S. J. Hook, D. A. Roberts, and E. A. Abbott, “The ECOSTRESS spectral library version 1.0,”<\/a> in Remote Sensing of Environment<\/em>, vol. 230 (111196), Sept. 2019.<\/p>\n\n\n\n

R. H. J. Heim, A. J. Carnegie, and P. J. Zarco-Tejada, “Breaking down barriers between remote sensing and plant pathology,”<\/a> in Tropical Plant Pathology,<\/em> vol. 44, pp. 398\u2013400, July 2019.<\/p>\n\n\n\n

S. Meerdink,  D. Roberts, G. Hulley, P. Gader,  J. Pisek, K. Adamson, and S. J. Hook,  “Plant species\u2019 spectral emissivity and temperature using the hyperspectral thermal emission spectrometer (HyTES) sensor,”<\/a> in Remote Sensing of Environment<\/em>, vol. 224, pp. 421\u2013435, Apr 2019.<\/p>\n\n\n\n

B. Weinstein, S. Marconi, S. Bohlman, A. Zare and E. White, \u201cIndividual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks<\/a>,\u201d in Remote Sensing<\/em>. 2019. vol 11. issue 11. pp 1309. <\/p>\n\n\n\n

A. Galusha, J. Keller, A. Zare, \u201cDeep Convolutional Neural Network Target Classification for Under- water Synthetic Aperture Sonar Imagery,\u201d<\/a> SPIE Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, Baltimore, MD, April 2019.<\/p>\n\n\n\n

J. Dale, A. Galusha, J. Keller, A. Zare, \u201cEvaluation of Image Features for Discriminating Targets from False Positives in Synthetic Aperture Sonar Imagery,\u201d<\/a> SPIE Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, Baltimore, MD, April 2019.<\/p>\n\n\n\n

J. Bocinsky, \u201cLearning Multiple Target Concepts from Uncertain, Ambiguous Data Using the Adaptive Cosine Estimator and Spectral Match Filter<\/a>,\u201d M.Sc. Thesis, Gainesville, FL, 2019.<\/p>\n\n\n\n

J. Bocinsky, C. McCurley, D. Shats and A. Zare, \u201cInvestigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator<\/a>,\u201d in Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019.<\/p>\n\n\n\n

J. Peeples, M. Cook, D. Suen, A. Zare and J. Keller, \u201cComparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation<\/a>,\u201d in Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019.<\/p>\n\n\n\n

C. McCurley, J. Bocinsky, A. Zare, \u201cComparison of Hand-held WEMI Target Detection Algorithms<\/a>,\u201d in Proc. SPIE 11012, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, 2019.<\/p>\n\n\n\n

X. Du and A. Zare, \u201cMultiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications<\/a>,\u201d IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 2741-2753, May 2019.<\/p>\n\n\n\n

S. Zou, P. Gader and A. Zare, “Hyperspectral Tree Crown Classification Using the Multiple Instance Adaptive Cosine Estimator<\/a>,\u201d PeerJ 7:e6405, Feb. 2019.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2018<\/h2>\n\n\n\n

S. Avusali, \u201cThree Dimensional Reconstruction of Plant Roots via Low Energy X-ray Computed Tomography<\/a>,\u201d M.Sc. Thesis, Columbia, MO, 2018.<\/p>\n\n\n\n

W. Chen, S. Gou, X. Wang, L. Jiao, C. Jiao, A. Zare, \u201cComplex Scene Classification of PoLSAR Imagery Based on a Self-Paced Learning Approach<\/a>,\u201d IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing<\/em>, vol. 11, pp. 4818 – 4825, Dec. 2018.<\/p>\n\n\n\n

C. Jiao, C. Chen, R. McGarvey, S. Bohlman, L. Jiao and A. Zare, \u201cMultiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection<\/a>,\u201dISPRS Journal of Photogrammetry and Remote Sensing<\/em>, vol. 146, pp. 235 – 250, Dec. 2018.<\/p>\n\n\n\n

T. Liu, A. Abd-Elrahman, A. Zare, B. Dewitt, L. Flory, and S. Smith \u201cA Fully Learnable Context-driven Object-based Model for Mapping Land Cover Using Multi-view Data from Unmanned Aircraft Systems<\/a>,\u201d Remote Sensing of Environment<\/em>, vol. 216, pp. 328-344, Oct. 2018.<\/p>\n\n\n\n

A. Starke, J. McNair, R. Trevizan, A. Bretas, J. Peeples, and A. Zare \u201cToward Resilient Smart Grid Communications Using Distributed SDN with ML-based Anomaly Detection,\u201d in The 16th International Conference on Wired\/Wireless Internet Communications, Boston, MA, Jun. 2018.<\/p>\n\n\n\n

S. Zou, H. Sun, and A. Zare \u201cHyperspectral Unmixing with Endmember Variability Using Semi-supervised Partial Membership Latent Dirichlet Allocation,\u201d in American Association of Geographers, New Orleans, LA, Apr. 2018. Abstract and Presentation Only.<\/p>\n\n\n\n

D. Stewart, A. Zare, and J. T. Cobb, \u201cQuantitative Evaluation Metrics for Superpixel Segmentation<\/a>\u201d in Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.<\/p>\n\n\n\n

J. Baciak, S. Cui, H. Gardiner, S. Kovaleski, and A. Zare, \u201cUse of Pencil Beam Backscatter Radiography to Image Buried Root Structures,” in Symp. on Radiation Measurements and Applications XVII, Ann Arbor, MI, June 2018.<\/p>\n\n\n\n

T. Nabelek, J. Keller, A. Zare, \u201cFractal Analysis of Seafloor Textures for Target Detection in Synthetic Aperture Sonar Imagery<\/a>,\u201d in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.<\/p>\n\n\n\n

A. Galusha, G. Galusha, J. Keller, A. Zare, \u201cA Target Classification Algorithm for Underwater Synthetic Aperture Sonar Imagery<\/a>,\u201d in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.<\/p>\n\n\n\n

B. Alvey, D. Ho, A. Zare, \u201cSample Spacing Variations on the Feature Performance for Subsurface Object Detection Using Handheld Ground Penetrating Radar<\/a>,\u201d in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.<\/p>\n\n\n\n

P. Lyons, D. Suen, A. Galusha, A. Zare, and J. Keller, \u201cComparison of Prescreening Algorithms for Target Detection in Synthetic Aperture Sonar Imagery<\/a>,\u201d in Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.<\/p>\n\n\n\n

J. Peeples, D. Suen, A. Zare, and J. Keller, \u201cPossibilistic Fuzzy Local Information C-means with Automated Feature Selection for Seafloor Segmentation<\/a>,\u201d in Proc. SPIE 10628, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, 2018.<\/p>\n\n\n\n

C. Jiao, B. Su, P. Lyons, A. Zare, K. C. Ho and M. Skubic, \u201cMultiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms<\/a>,\u201d IEEE Trans. Biomed. Eng.<\/em>, vol. 65, pp. 2634 – 2648, Nov. 2018.<\/p>\n\n\n\n

A. Zare, C. Jiao, and T. Glenn, \u201cDiscriminative Multiple Instance Hyperspectral Target Characterization<\/a>,\u201d IEEE Trans. Pattern Anal. Mach. Intell.<\/em>, vol. 40, pp. 2342 – 2354, Oct. 2018.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2017<\/h2>\n\n\n\n

X. Du, \u201cMultiple Instance Choquet Integral For Multi-Resolution Sensor Fusion<\/a>,\u201d Ph.D. Thesis, Columbia, MO, 2017.<\/p>\n\n\n\n

C. Jiao, \u201cTarget Concept Learning From Ambiguously Labeled Data<\/a>,\u201d Ph.D. Thesis, Columbia, MO, 2017.<\/p>\n\n\n\n

A. Zare, N. Young, D. Suen, T. Nabelek, A. Galusha, J. Keller, \u201cPossibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation<\/a>,\u201d in IEEE Symp. Series on Computational Intelli.<\/em>, Honolulu, Hi, 2017.<\/p>\n\n\n\n

C. Chen, A. Zare, H. Trinh, G. Omotara, J. T. Cobb, and P. Lagaunne, \u201cPartial Membership Latent Dirichlet Allocation<\/a>,\u201d IEEE Trans. Image Process.<\/em>, vol. 26, pp. 5590-5602, Dec. 2017.<\/p>\n\n\n\n

A. J. Pinar, D. T. Anderson, T. C. Havens, A. Zare and T. Adeyeba, \u201cMeasures of the Shapley Index for Learning Lower Complexity Fuzzy Integrals<\/a>\u201d Granular Computing (2017): 1-17<\/p>\n\n\n\n

R. E. Smith, D. T. Anderson, J. E. Ball, A. Zare and B. Alvey, \u201cAggregation of Choquet Integrals in GPR and EMI for Handheld Platform-based Explosive Hazard Detection<\/a>\u201d in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.<\/p>\n\n\n\n

S. Harris, B. Alvey, D. Ho and A. Zare, \u201cLBP Features for Hand-Held Ground Penetrating Radar<\/a>\u201d in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.<\/p>\n\n\n\n

B. Alvey, A. Zare and D. Ho, \u201cFourier Features for Explosive Hazard Detection Using a Wideband Electromagnetic Induction Sensor<\/a>\u201d in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.<\/p>\n\n\n\n

D. T. Anderson, M. A. Islam, R. King, N. H. Younan, J. R. Fairley, S. Howington, F. Petry, P. Elmore and A. Zare, \u201cBinary Fuzzy Measures and Choquet Integration for Multi-Source Fusion<\/a>\u201d Intl. Conf. on Military Technologies, 2017.<\/p>\n\n\n\n

R. Smith, D. Anderson, A. Zare, J. Ball, B. Alvey, J. Fairley, and S. Howington, \u201cGenetic Programming Based Choquet Integral for Multi-Source Fusion <\/a>\u201d in IEEE Int. Conf. Fuzzy Syst. <\/em>, Naples, Italy, 2017, pp. 1-8.<\/p>\n\n\n\n

X. Du, A. Seethepalli, H. Sun, A. Zare and J. T. Cobb, \u201cEnvironmentally-Adaptive Target Recognition for SAS Imagery <\/a>\u201d in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.<\/p>\n\n\n\n

J. T. Cobb, X. Du, A. Zare, and M. Emigh, \u201cMultiple-instance Learning-based Sonar Image Classification <\/a>\u201d in Proc. SPIE 10182, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, 2017.<\/p>\n\n\n\n

C. Jiao and A. Zare, \u201cMultiple Instance Hybrid Estimator for Learning Target Signatures<\/a>\u201d in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS)<\/em>, Fort Worth, TX, 2017<\/p>\n\n\n\n

H. Sun and A. Zare, \u201cMap-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps<\/a>\u201d in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS)<\/em>, Fort Worth, TX, 2017<\/p>\n\n\n\n

S. Zou and A. Zare, \u201cHyperspectral Unmixing with Endmember Variability Using Partial Membership Latent Dirichlet Allocation<\/a>.\u201d in IEEE Int. Conf. Acoust, Speech and Signal Process. (ICASSP)<\/em>, New Orleans, LA, 2017, pp. 6200-6204.<\/p>\n\n\n\n

S. Zou, H. Sun, and A. Zare, \u201cHyperspectral Unmixing with Endmember Variability Using Semi-supervised Partial Membership Latent Dirichlet Allocation,<\/a>\u201d in CoRR<\/em>. vol abs\/1703.06151. 2017.  <\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2016<\/h2>\n\n\n\n

A. Seethepalli, \u201cMulti-camera High-throughput Plant Root Phenotyping System<\/a>,\u201d M.Sc. Thesis, Columbia, MO, 2016.<\/p>\n\n\n\n

H. Sun, \u201cMap-guided Hyperspectral Image Superpixel Segmentation Using Semi-supervised Partial Membership Latent Dirichlet Allocation<\/a>,\u201d M.Sc. Thesis, Columbia, MO, 2016.<\/p>\n\n\n\n

S. Zou, \u201cSemi-supervised Interactive Unmixing for Hyperspectral Image Analysis<\/a>,\u201d M.Sc. Thesis, Columbia, MO, 2016.<\/p>\n\n\n\n

B. Alvey, A. Zare, M. Cook, and D. Ho, \u201cAdaptive Coherence Estimator (ACE) for Explosive Hazard Detection Using Wideband Electromagnetic Induction (WEMI)<\/a>,\u201d in Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 2016.<\/p>\n\n\n\n

J. Bezdek, X. Ye, M. Popescu, J. Keller and A. Zare, “Random Projection Below the JL Limit<\/a>,” 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada, 2016, pp. 2414-2423.<\/p>\n\n\n\n

C. Chen, \u201cPartial Membership Latent Dirichlet Allocation<\/a>,\u201d PhD Thesis, Columbia, MO, 2016.<\/p>\n\n\n\n

C. Chen, A. Zare, and J. T. Cobb, \u201cPartial Membership Latent Dirichlet Allocation for Image Segmentation<\/a>,\u201d in Int. Conf. Pattern Recognition (ICPR), 2016.<\/p>\n\n\n\n

M. Cook, A. Zare, and D. Ho, \u201cBuried Object Detection Using Handheld WEMI with Task-driven Extended Functions of Multiple Instances<\/a>,\u201d in Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 2016.<\/p>\n\n\n\n

X. Du, A. Zare, J. Keller, and D. Anderson, \u201cMultiple Instance Choquet Integral for Classifier Fusion<\/a>,\u201d in IEEE Congr. Evol. Computation (CEC)<\/em>, Vancouver, BC, 2016, pp. 1054-1061.<\/p>\n\n\n\n

S. Harris, D. Ho, and A. Zare, \u201cOn the Use of Log-gabor Features for Subsurface Object Detection Using Ground Penetrating radar<\/a>,\u201d in Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 2016.<\/p>\n\n\n\n

R. Heylen, P. Scheunders, A. Zare, and P. Gader, \u201cAlternating Angle Minimization Based Unmixing with Endmember Variability<\/a>,\u201d in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS)<\/em>, Beijing, 2016, pp. 6974-6977.<\/p>\n\n\n\n

R. Heylen, A. Zare, P. Gader, and P. Scheunders, \u201cHyperspectral Unmixing With Endmember Variability via Alternating Angle Minimization<\/a>,\u201d IEEE Trans. Geosci. Remote Sens.<\/em>, vol. 54, pp. 4983-4993, Aug. 2016.<\/p>\n\n\n\n

C. Jiao, P. Lyons, A. Zare, L. Rosales, and M. Skubic, \u201cHeart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances<\/a>,\u201d in Proc. EMBC, 2016.<\/p>\n\n\n\n

C. Jiao and A. Zare, \u201cMultiple Instance Dictionary Learning using Functions of Multiple Instances<\/a>,\u201d in Int. Conf. Pattern Recognition (ICPR), 2016.<\/p>\n\n\n\n

S. Zou and A. Zare, \u201cInstance Influence Estimation for Hyperspectral Target Signature Characterization Using Extended Functions of Multiple Instances<\/a>,\u201d in Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 2016.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2015<\/h2>\n\n\n\n

C. Chen, A. Zare, and J. T. Cobb, \u201cSand Ripple Characterization Using an Extended Synthetic Aperture Sonar Model and Parallel Sampling Method<\/a>,\u201d IEEE Trans. Geosci. Remote Sens.<\/em>, vol. 53, pp. 5547-5559, Oct. 2015.<\/p>\n\n\n\n

M. Cook, \u201cTask Driven Extended Functions of Multiple Instances<\/a>,\u201d Master Thesis, Columbia, MO, 2015.<\/p>\n\n\n\n

X. Du, A. Zare, and J. T. Cobb, \u201cPossibilistic context identification for SAS imagery<\/a>,\u201d in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015.<\/p>\n\n\n\n

T. C. Glenn, A. Zare, and P. Gader, \u201cBayesian Fuzzy Clustering<\/a>,\u201d IEEE Trans. Fuzzy Syst.<\/em>, vol. 23, pp. 1545-1561, Oct. 2015.<\/p>\n\n\n\n

T. C. Glenn and A. Zare, \u201cEstimating Target Signatures with Diverse Density<\/a>,\u201d in 7th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015.<\/p>\n\n\n\n

K. C. Ho, S. Harris, A. Zare, and M. Cook, \u201cAnomaly detection of subsurface objects using handheld ground-penetrating radar<\/a>,\u201d in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015.<\/p>\n\n\n\n

C. Jiao and A. Zare, \u201cFunctions of Multiple Instances for Learning Target Signatures<\/a>,\u201d IEEE Trans. Geosci. Remote Sens.<\/em>, vol. 53, pp. 4670-4686, Aug. 2015.<\/p>\n\n\n\n

M. Popescu, J. Keller, J. Bezdek, and A. Zare, \u201cRandom Projections Fuzzy C-means (RPFCM) for Big Data Clustering<\/a>,\u201d in IEEE Int. Conf. Fuzzy Syst. (FUZZ-IEEE)<\/em>, Istanbul, 2015, pp. 1-6.<\/p>\n\n\n\n

A. Zare and C. Jiao, \u201cFunctions of Multiple Instances for Sub-pixel Target Characterization in Hyperspectral Imagery<\/a>,\u201d in Proc. SPIE 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI, 2015.<\/p>\n\n\n\n

A. Zare, M. Cook, B. Alvey, and D. Ho, \u201cMultiple Instance Dictionary Learning for Subsurface Object Detection Using Handheld EMI<\/a>,\u201d in Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 2015.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2014<\/h2>\n\n\n\n

A. Buck, A. Zare, J. Keller, and M. Popescu, \u201cEndmember Representation of Human Geography Layers<\/a>,\u201d in IEEE Symp. Computational Intell. Big Data (CIBD)<\/em>, Orlando, FL, 2014, pp. 1-6.<\/p>\n\n\n\n

C. Chen, A. Zare, and J. T. Cobb, \u201cInvariant Parameter Estimation Across Varying Seabeds in Synthetic Aperture Sonar Imagery<\/a>,\u201d in 3rd Int. Conf. on SAS and SAR, 2014.<\/p>\n\n\n\n

J. T. Cobb and A. Zare, \u201cBoundary Detection and Superpixel Formation in Synthetic Aperture Sonar Imagery<\/a>,\u201d in 3rd Int. Conf. SAS and SAR, 2014.<\/p>\n\n\n\n

X. Du, A. Zare, P. Gader, and D. Dranishnikov, \u201cSpatial and Spectral Unmixing Using the Beta Compositional Model<\/a>,\u201d IEEE J. Sel. Topics. Appl. Earth Observ.<\/em>, vol. 7, pp. 1994-2003, June, 2014.<\/p>\n\n\n\n

J. Keller, A. Buck, A. Zare, and M. Popescu, \u201cA Human Geospatial Predictive Analytics Framework with Application to Finding Medically Underserved Areas<\/a>,\u201d in IEEE Symp. Computational Intell. Big Data (CIBD)<\/em>, Orlando, FL, 2014, pp. 1-6.<\/p>\n\n\n\n

P. Khopkar, \u201cHyperspectral Unmixing and Band Weighting for Multiple Endmember Sets<\/a>,\u201d Master Thesis, Columbia, MO, 2014.<\/p>\n\n\n\n

P. Massoudifar, A. Rangarajan, A. Zare, and P. Gader, \u201cAn Integrated Graph Cuts Segmentation and Piece-wise Convex Unmixing Approach for Hyperspectral Imaging<\/a>,\u201d in 6th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014.<\/p>\n\n\n\n

Y. Wang, Y. Li, K. C. Ho, A. Zare, and M. Skubic, \u201cSparsity Promoted Non-negative Matrix Factorization for Source Separation and Detection<\/a>,\u201d in 19th Int. Conf. Digital Signal Proc. (DSP), 2014, pp. 640-645.<\/p>\n\n\n\n

A. Zare and D. T. Anderson, \u201cEarth Movers Distance-based Simultaneous Comparison of Hyperspectral Endmembers and Proportions<\/a>,\u201d IEEE J. Sel. Topics. Appl. Earth Observ.<\/em>, vol. 7, pp. 1910-1921, June 2014.<\/p>\n\n\n\n

A. Zare and K. C. Ho, \u201cEndmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing<\/a>,\u201d IEEE Signal Process. Mag.<\/em>, vol. 31, pp. 95-104, Jan. 2014.<\/p>\n\n\n\n

A. Zare and C. Jiao, \u201cExtended Functions of Multiple Instances for Target Characterization<\/a>,\u201d in 6th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014.<\/p>\n\n\n\n

 <\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2013<\/h2>\n\n\n\n

D. T. Anderson, A. Zare, and S. Price, \u201cComparing Fuzzy, Probabilistic, and Possibilistic Partitions Using the Earth Mover\u2019s Distance<\/a>,\u201d IEEE Trans. Fuzzy Syst.<\/em>, vol. 21, pp. 766-775, Aug. 2013.<\/p>\n\n\n\n

J. T. Cobb and A. Zare, \u201cMulti-Image Texton Selection for Sonar Image Seabed Co-segmentation<\/a>,\u201d in Proc. SPIE 8709, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 2013.<\/p>\n\n\n\n

D. Dranishnikov, P. Gader, A. Zare, and T. Glenn, \u201cUnmixing Using a Combined Microscopic and Macroscopic Mixture Model with Distinct Endmembers<\/a>,\u201d in 21st European Signal Proc. Conf. (EUSPICO), 2013.<\/p>\n\n\n\n

X. Du, \u201cAccounting for Spectral Variability in Hyperspectral Unmixing Using Beta Endmember Distribution<\/a>,\u201d Master Thesis, Columbia, MO, 2013.<\/p>\n\n\n\n

J. Dula, A. Zare, D. Ho, and P. Gader, \u201cLandmine Classification Using Possibilistic K-nearest Neighbors with Wideband Electromagnetic Induction Data<\/a>,\u201d in Proc. SPIE 8709 Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVIII, 2013.<\/p>\n\n\n\n

T. Glenn, D. Dranishnikov, P. Gader, and A. Zare, \u201cSubpixel Target Detection in Hyperspectral Imagery Using Piece-wise Convex Spatial-spectral Unmixing, Possibilistic and Fuzzy Clustering, and Co-registered LiDAR<\/a>,\u201d in IEEE Int. Geosci. and Remote Sensing Symp.(IGARSS))<\/em>, Melbourne, VIC, 2013, pp. 1063-1066.<\/p>\n\n\n\n

P. Khopkar and A. Zare, \u201cSimultaneous Band-weighting and Spectral Unmixing for Multiple Endmember Sets<\/a>,\u201d in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS)<\/em>, Melbourne, VIC, 2013, pp. 2164-2167.<\/p>\n\n\n\n

H. Kwon, X. Hu, J. Theiler, A. Zare, and P. Gurram, \u201cEditorial: Algorithms for Multispectral and Hyperspectral Image Analysis<\/a>,\u201d J. Electr. Computer Eng., vol. 2013, 2013.<\/p>\n\n\n\n

M. Popescu, J. Keller, and A. Zare, \u201cA Framework for Computing Crowd Emotions Using Agent Based Modeling<\/a>,\u201d in IEEE Symp. Computational Intelli. Creativity and Affective Computing (CICAC)<\/em>, Singapore, 2013, pp. 25-31.<\/p>\n\n\n\n

A. Zare, P. Gader, D. Dranishnikov, and T. Glenn, \u201cSpectral Unmixing Using the Beta Compositional Model<\/a>,\u201d in 5th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013.<\/p>\n\n\n\n

A. Zare, P. Gader, O. Bchir, and H. Frigui, \u201cPiecewise Convex Multiple-model Endmember Detection and Spectral Unmixing<\/a>,\u201d IEEE Trans. Geosci. Remote Sens.<\/em>, vol. 51, pp. 2853-2862, May 2013.<\/p>\n\n\n\n

A. Zare, P. Gader, and G. Casella, \u201cSampling Piecewise Convex Unmixing and Endmember Extraction<\/a>,\u201d IEEE Trans. Geosci. Remote Sens.<\/em>, vol. 51, pp. 1655-1665, Mar. 2013.<\/p>\n\n\n\n

A. Zare and J. T. Cobb, \u201cSand Ripple Characterization Using an Extended Synthetic Aperture Sonar Model and MCMC Sampling Methods<\/a>,\u201d in IEEE OCEANS – San Diego, CA, 2013, pp. 1-7.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2012<\/h2>\n\n\n\n

D. T. Anderson and A. Zare, \u201cSpectral Unmixing Cluster Validity Index for Multiple Sets of Endmembers<\/a>,\u201d IEEE J. Sel. Topics. Appl. Earth Observ.<\/em>, vol. 5, pp. 1282-1295, Aug. 2012.<\/p>\n\n\n\n

R. Close, P. Gader, A. Zare, J. Wilson, and D. Dranishnikov, \u201cEndmember Extraction Using the Physics-based Multi-mixture Pixel Model<\/a>,\u201d in Proc. SPIE 8515, Imaging Spectrometry XVII, 2012.<\/p>\n\n\n\n

R. Close, P. Gader, J. Wilson, and A. Zare, \u201cUsing Physics-based Macroscopic and Microscopic Mixture Models for Hyperspectral Pixel Unmixing<\/a>,\u201d in Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 2012.<\/p>\n\n\n\n

J. Dula, \u201cLandmine Classification Using Possibilistic K-nearest Neighbors with Wideband Electromagnetic Induction Data<\/a>,\u201d Master Thesis, Columbia, MO, 2012.<\/p>\n\n\n\n

P. Gader, D. Dranishnikov, A. Zare, and J. Chanussot, \u201cA Sparsity Promoting Bilinear Unmixing Model<\/a>,\u201d in 4th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012.<\/p>\n\n\n\n

N. Longbotham, F. Pacifici, T. Glenn, A. Zare, M. Volpi, D. Tuia, E. Christophe, J. Michel, J. Inglada, J. Chanussot, and Q. Du, \u201cMulti-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 Data Fusion Contest<\/a>,\u201d IEEE J. Sel. Topics Appl. Earth Observ.<\/em>, vol. 5, pp. 331-342, Feb. 2012.<\/p>\n\n\n\n

A. Zare, Z. Fields, J. Keller, and J. Horton, \u201cAgent-based Rumor Spreading Models for Human Geography Applications<\/a>,\u201d in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS)<\/em>, Munich, 2012, pp. 5394-5397.<\/p>\n\n\n\n

A. Zare, P. Gader, T. Allgire, D. Drashnikov, and R. Close, \u201cBootstrapping for Piece-wise Convex Endmember Distribution Detection<\/a>,\u201d in 4th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012.<\/p>\n\n\n\n

A. Zare, P. Gader, and K. S. Gurumoorthy, \u201cDirectly Measuring Material Proportions Using Hyperspectral Compressive Sensing<\/a>,\u201d IEEE Geosci. Remote Sens. Lett.<\/em>, vol. 9, pp. 323-327, May 2012.<\/p>\n\n\n\n

A. Zare, O. Bchir, H. Frigui, and P. Gader, \u201cHyperspectral Image Analysis with Piece-wise Convex Endmember Estimation and Spectral Unmixing<\/a>,\u201d in 19th IEEE Int. Conf. Image Proc. (ICIP)<\/em>, Orlando, FL, 2012, pp. 2681-2684.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2011<\/h2>\n\n\n\n

A. Zare and P. Gader, \u201cMulticlass Subpixel Target Detection Using Functions of Multiple Instances<\/a>,\u201d in Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 2011.<\/p>\n\n\n\n

A. Zare and P. Gader, \u201cPiece-wise Convex Spatial-spectral Unmixing of Hyperspectral Imagery Using Possibilistic and Fuzzy Clustering<\/a>,\u201d in IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE)<\/em>, Taipei, 2011, pp. 741-746.<\/p>\n\n\n\n

A. Zare, M. Weiss, and P. Gader, \u201cRebuilding the Injured Brain: Use of MRS in Clinical Regenerative Medicine<\/a>,\u201d in Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 2011.<\/p>\n\n\n\n

A. Zare, \u201cSpatial-spectral Unmixing Using Fuzzy Local Information<\/a>,\u201d in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS)<\/em>, Vancouver, BC, 2011, pp. 1139-1142.<\/p>\n\n\n\n

A. Zare, P. Gader, J. Bolton, S. Yuksel, T. Dubroca, R. Close, and R. Hummel, \u201cSub-pixel Target Spectra Estimation and Detection Using Functions of Multiple Instances<\/a>,\u201d in 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2011.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2010<\/h2>\n\n\n\n

O. Bchir, H. Frigui, A. Zare, and P. Gader, \u201cMultiple Model Endmember Detection Based on Spectral and Spatial Information<\/a>,\u201d in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010.<\/p>\n\n\n\n

A. Zare, O. Bchir, H. Frigui, and P. Gader, \u201cA Comparison of Deterministic and Probabilistic Approaches to Endmember Representation<\/a>,\u201d in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010.<\/p>\n\n\n\n

A. Zare and P. Gader, \u201cAn Investigation of Likelihoods and Priors for Bayesian Endmember Estimation<\/a>,\u201d in Proc. MaxEnt 2010: 30th Int. Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 2010, pp. 311-318.<\/p>\n\n\n\n

A. Zare and P. Gader, \u201cL1-endmembers: A Robust Endmember Detection and Spectral Unmixing Algorithm<\/a>,\u201d in Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 2010.<\/p>\n\n\n\n

A. Zare and P. Gader, \u201cPattern Recognition Using Functions of Multiple Instances<\/a>,\u201d in 20th Int. Conf. Pattern Recognition (ICPR), 2010, pp. 1092-1095.<\/p>\n\n\n\n

A. Zare and P. Gader, \u201cPCE: Piecewise Convex Endmember Detection<\/a>,\u201d IEEE Trans. Geosci. Remote Sens.<\/em>, vol. 48, pp. 2620-2632, June 2010.<\/p>\n\n\n\n

A. Zare, M. Silvious, R. Close, and P. Gader, \u201cQuantifying the Benefit of Airborne and Ground Sensor Fusion for Target Detection<\/a>,\u201d in Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 2010.<\/p>\n\n\n\n

A. Zare and P. Gader, \u201cRobust Endmember Detection Using L1 Norm Factorization<\/a>,\u201d in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS)<\/em>, Honolulu, HI, 2010, pp. 971-974.<\/p>\n\n\n\n

A. Zare, O. Bchir, H. Frigui, and P. Gader, \u201cSpatially-smooth Piece-wise Convex Endmember Detection<\/a>,\u201d in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2009<\/h2>\n\n\n\n

A. Zare and P. Gader, \u201cContext-based Endmember Detection for Hyperspectral Imagery<\/a>,\u201d in 1st IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2009.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2008<\/h2>\n\n\n\n

A. Zare and P. Gader, \u201cEndmember Detection Using the Dirichlet Process<\/a>,\u201d in 19th Int. Conf. Pattern Recognition (ICPR), 2008.<\/p>\n\n\n\n

A. Zare and P. Gader, \u201cHyperspectral Band Selection and Endmember Detection Using Sparsity Promoting Priors<\/a>,\u201d IEEE Geosci. Remote Sens. Lett.<\/em>, vol. 5, pp. 256-260, Apr. 2008.<\/p>\n\n\n\n

A. Zare, \u201cHyperspectral endmember detection and band selection using bayesian methods<\/a>,\u201d PhD Thesis, Gainesville, FL, 2008.<\/p>\n\n\n\n

A. Zare, J. Bolton, P. Gader, and M. Schatten, \u201cVegetation Mapping for Landmine Detection Using Long-wave Hyperspectral Imagery<\/a>,\u201d IEEE Trans. Geosci. Remote Sens.<\/em>, vol. 46, pp. 172-178, Jan. 2008.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2007<\/h2>\n\n\n\n

A. Zare and P. Gader, \u201cSparsity Promoting Iterated Constrained Endmember Detection with Integrated Band Selection<\/a>,\u201d in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS)<\/em>, Barcelona, 2007, pp. 4045-4048.<\/p>\n\n\n\n

A. Zare and P. Gader, \u201cSparsity Promoting Iterated Constrained Endmember Detection in Hyperspectral Imagery<\/a>,\u201d IEEE Geosci. Remote Sens. Lett.<\/em>, vol. 4, pp. 446-450, July 2007.<\/p>\n\n\n\n

A. Zare and P. Gader, “SPICE: A Sparsity Promoting Iterated Constrained Endmember Extraction Algorithm with Applications to Landmine Detection from Hyperspectral Imagery<\/a>,\u201d in Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 2007.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

2006<\/h2>\n\n\n\n

M. Schatten, P. Gader, J. Bolton, A. Zare, and A. Mendez-Vasquez, \u201cSensor Fusion for Airborne Landmine Detection<\/a>,\u201d in Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 2006.<\/p>\n\n\n\n

2004<\/h2>\n\n\n\n

<\/a> P. Gader, A. Mendez-Vasquez, K. Chamberlin, J. Bolton, and A. Zare, \u201cMulti-sensor and Algorithm Fusion with the Choquet Integral: Applications to Landmine Detection<\/a>,\u201d in IEEE Int. Geosci. and Remote Sensing Symp. (IGARSS)<\/em>, Anchorage, AK, 2004, pp. 1605-1608 vol.3.<\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

Preprint<\/h2>\n\n\n\n

A. Seethepalli, L. York, H. Almtarfi, F. Fritschi and A. Zare, \u201cA Novel Multi-perspective Imaging Platform (M-PIP) for Phenotyping Soybean Root Crowns in the Field Increases Throughput and Separation Ability of Genotype Root Properties<\/a>,\u201d in bioRxiv,<\/em> <\/span>309161<\/span>.<\/span> <\/p>\n\n\n\n

R. Fick, P. Gader and A. Zare, \u201cRobust Semi-Supervised Classification using GANs with Self-Organizing Maps\u201d<\/a><\/p>\n\n\n\n

A. Hampton, D. Stewart, A. Zare, and J. Keller, \u201cComparison of unsupervised quantitative metrics for superpixel segmentation,\u201d<\/a> Available at SSRN 4744767<\/em>.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Under Review | 2026 | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2004 | Preprint Under Review \/ In Preparation A. Hampton, […]<\/p>\n","protected":false},"author":468,"featured_media":0,"parent":0,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"page-templates\/page-sidebar-none.php","meta":{"_acf_changed":false,"inline_featured_image":false,"featured_post":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-11","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/pages\/11","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/users\/468"}],"replies":[{"embeddable":true,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/comments?post=11"}],"version-history":[{"count":20,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/pages\/11\/revisions"}],"predecessor-version":[{"id":16345,"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/pages\/11\/revisions\/16345"}],"wp:attachment":[{"href":"https:\/\/faculty.eng.ufl.edu\/machine-learning\/wp-json\/wp\/v2\/media?parent=11"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}