Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering

A decision-theoretic generalization of on-line learning and an application to boosting

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Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)

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Neural network-based face detection

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Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories

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Object class recognition by unsupervised scale-invariant learning

Fergus, R., Perona, P., & Zisserman, A. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211479 · Cited by 2,030

Learning methods for generic object recognition with invariance to pose and lighting

LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324

Shape Matching and Object Recognition Using Low Distortion Correspondences

Berg, A. C., Berg, T. L., & Malik, J. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.320 · Cited by 844

Analyzing appearance and contour based methods for object categorization

Leibe, B., & Schiele, B. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211497 · Cited by 798

Sharing Visual Features for Multiclass and Multiview Object Detection

Torralba, A. B., Murphy, K. P., & Freeman, W. T. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1055 · Cited by 704

Fast Multi-view Face Detection

Jones, M., & Viola, P. A. (2003) · CTIT technical reports series · Cited by 615

Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data

Heimann, T., Mountney, P., John, M., & Ionasec, R. I. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.04.007 · Cited by 39

Constrained marginal space learning for efficient 3D anatomical structure detection in medical images

Zheng, Y., Georgescu, B., Ling, H., Zhou, S. K., Scheuering, M., & Comaniciu, D. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206807 · Cited by 39

Learning-based hypothesis fusion for robust catheter tracking in 2D X-ray fluoroscopy

Wu, W. H., Chen, T., Wang, P., Zhou, S. K., Comaniciu, D., Barbu, A., & Strobel, N. K. (2011) · doi:10.1109/cvpr.2011.5995553 · Cited by 38

Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information

Feng, S., Zhou, S. K., Good, S., & Comaniciu, D. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206527 · Cited by 36

A Benchmark for Breast Ultrasound Image Segmentation (BUSIS)Preprint

Xian, M., Zhang, Y., Cheng, H.-D., Xu, F., Huang, K., Zhang, B., Ding, J., Ning, C., & Wang, Y. (2018) · arXiv (Cornell University) · Cited by 36

Automatic ovarian follicle quantification from 3D ultrasound data using global/local context with database guided segmentation

Chen, T., Zhang, W., Good, S., Zhou, K. S., & Comaniciu, D. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459243 · Cited by 35

Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention

Chung, M., Lee, J., Park, S., Lee, C. E., Lee, J., & Shin, Y.-G. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102023 · Cited by 35

Semi Supervised Multi Kernel (SeSMiK) Graph Embedding: Identifying Aggressive Prostate Cancer via Magnetic Resonance Imaging and Spectroscopy

Tiwari, P., Kurhanewicz, J. V., Rosen, M. A., & Madabhushi, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15711-0_83 · Cited by 34

Precise Segmentation of Multiple Organs in CT Volumes Using Learning-Based Approach and Information Theory

Lu, C., Zheng, Y., Birkbeck, N., Zhang, J., Kohlberger, T., Tietjen, C., Boettger, T., Duncan, J. S., & Zhou, S. K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33418-4_57 · Cited by 34

Object categorization with sketch representation and generalized samples

Lin, L., Liu, X., Peng, S., Chao, H., Wang, Y., & Jiang, B. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.03.017 · Cited by 34

Human action detection by boosting efficient motion features

Yang, M., Lv, F., Xu, W., Yu, K., & Gong, Y. (2009) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2009.5457656 · Cited by 35

Efficient scale space auto-context for image segmentation and labeling

Jiang, J., & Zhuowen Tu, A. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206761 · Cited by 34

Multi-layer boosting sparse convolutional model for generalized nuclear segmentation from histopathology images

Song, J., Xiao, L., Molaei, M., & Lian, Z. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.03.031 · Cited by 31

Hierarchical Discriminative Framework for Detecting Tubular Structures in 3D Images

Breitenreicher, D., Sofka, M., Britzen, S., & Zhou, S. K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38868-2_28 · Cited by 33

Precise Lumen Segmentation in Coronary Computed Tomography Angiography

Lugauer, F., Zheng, Y., Joachim Hornegger, A., & Kelm, B. M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13972-2_13 · Cited by 33

High-Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts

Janowczyk, A. R., Chandran, S., Singh, R. R., Sasaroli, D., Coukos, G., Feldman, M. D., & Madabhushi, A. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2179546 · Cited by 33

Accurate polyp segmentation for 3D CT colongraphy using multi-staged probabilistic binary learning and compositional model

Lü, L., Barbu, A., Wolf, M., Liang, J., Salganicoff, M., & Comaniciu, D. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587423 · Cited by 33

Finding the weakest link in person detectors

Parikh, D., & Zitnick, C. L. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995450 · Cited by 32

Statistical shape model for manifold regularization: Gleason grading of prostate histology

Sparks, R. E., & Madabhushi, A. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.11.011 · Cited by 32

Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains

Fayaz, M., Meraj, G., Khader, S. A., Farooq, M., Kanga, S., Singh, S. K., Kumar, P., & Sahu, N. (2022) · Land · doi:10.3390/land11060884 · Cited by 32

Dynamic Model-Driven Quantitative and Visual Evaluation of the Aortic Valve from 4D CT

Ionasec, R. I., Georgescu, B., Gassner, E.-M., Vogt, S. K., Kutter, O., Scheuering, M., Navab, N., & Comaniciu, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_82 · Cited by 32

Semantic-based indexing of fetal anatomies from 3-D ultrasound data using global/semi-local context and sequential sampling

Carneiro, G., Amat, F., Georgescu, B., Good, S., & Comaniciu, D. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587358 · Cited by 32

A fast and accurate tracking algorithm of left ventricles in 3D echocardiography

Yang, L., Georgescu, B., Zheng, Y., Foran, D. J., & Comaniciu, D. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4540972 · Cited by 32

Locality-constrained Subcluster Representation Ensemble for lung image classification

Song, Y., Cai, W., Huang, H., Zhou, Y., Wang, Y., & Feng, D. D. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.03.003 · Cited by 31

A Supervised Framework for the Registration and Segmentation of White Matter Fiber Tracts

Mayer, A., Zimmerman‐Moreno, G., Shadmi, R., Batikoff, A., & Greenspan, H. K. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2067222 · Cited by 30

Automated landmarking and labeling of fully and partially scanned spinal columns in CT images

Major, D., Hladůvka, J., Schulze, F., & Bühler, K. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.07.005 · Cited by 30

Automatic Magnetic Resonance Image Prostate Segmentation Based on Adaptive Feature Learning Probability Boosting Tree Initialization and CNN-ASM Refinement

He, B., Xiao, D., Hu, Q., & Jia, F. (2017) · IEEE Access · doi:10.1109/access.2017.2781278 · Cited by 30

An Empirical Study of Object Category Recognition: Sequential Testing with Generalized Samples

Lin, L., Peng, S., Porway, J., Zhu, S. C., & Wang, Y. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408873 · Cited by 30

Contour detection and image segmentation

Malik, J., & Maire, M. (2009) · Cited by 29

Segmentation of Multiple Knee Bones from CT for Orthopedic Knee Surgery Planning

Wu, D., Sofka, M., Birkbeck, N., & Zhou, S. K. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10404-1_47 · Cited by 29

Image-based Co-Registration of Angiography and Intravascular Ultrasound Images

Wang, P., Ecabert, O., Chen, T., Wels, M., Rieber, J., Ostermeier, M., & Comaniciu, D. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2279754 · Cited by 28

Joint Sulcal Detection on Cortical Surfaces With Graphical Models and Boosted Priors

Shi, Y., Zhuowen Tu, A., Reiss, A. L., Dutton, R. A., Lee, A., Galaburda, A. M., Dinov, I. D., Thompson, P. M., & Toga, A. W. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2004402 · Cited by 28

Constrained Registration for Motion Compensation in Atrial Fibrillation Ablation Procedures

Brost, A., Wimmer, A., Liao, R. J., Bourier, F. J., Koch, M., Strobel, N. K., Kurzidim, K. W., & Joachim Hornegger, A. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2181184 · Cited by 27

A Probabilistic Model for Automatic Segmentation of the Esophagus in 3-D CT Scans

Feulner, J., Zhou, S. K., Hammon, M., Seifert, S., Huber, M. T., Comaniciu, D., Joachim Hornegger, A., & Cavallaro, A. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2112372 · Cited by 27

Cardiac Anchoring in MRI through Context Modeling

Lu, X., Georgescu, B., Jolly, M., Guehring, J., Young, A. A., Cowan, B. R., Littmann, A., & Comaniciu, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15705-9_47 · Cited by 26

Fast Automatic Detection of Calcified Coronary Lesions in 3D Cardiac CT Images

Mittal, S., Zheng, Y., Georgescu, B., Vega-Higuera, F., Zhou, S. K., Meer, P., & Comaniciu, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15948-0_1 · Cited by 26

Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach

Gao, Y., Yang, M., & Choudhary, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03348-3_17 · Cited by 26

AutoMPR: Automatic detection of standard planes in 3D echocardiography

Lu, X., Georgescu, B., Zheng, Y., Otsuki, J., & Comaniciu, D. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4541237 · Cited by 26

Collaborative regression-based anatomical landmark detection

Gao, Y., & Shen, D. G. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/24/9377 · Cited by 26

Integrating Spatio-Temporal Context With Multiview Representation for Object Recognition in Visual Surveillance

Liu, X., Lin, L., Yan, S., Jin, H., & Tao, W. (2010) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2010.2087570 · Cited by 25

Lymph node detection in 3-D chest CT using a spatial prior probability

Johannes Feulner, A., Zhou, S. K., Huber, M., Joachim Hornegger, A., Comaniciu, D., & Cavallaro, A. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540034 · Cited by 25

Multi-class Object Detection in Vision Systems Using a Hierarchy of Cascaded Classifiers

Kallenbach, I., Schweiger, R., Palm, G., & Löhlein, O. (2006) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2006.1689658 · Cited by 24

Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI

Viswanath, S. E., Bloch, B., Chappelow, J., Patel, P. K., Rofsky, N. M., Lenkinski, R. E., Genega, E. M., & Madabhushi, A. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.878312 · Cited by 24

Multiple object detection by sequential monte carlo and Hierarchical Detection Network

Sofka, M., Zhang, J., Zhou, S. K., & Comaniciu, D. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539842 · Cited by 24

Personalized Modeling and Assessment of the Aortic-Mitral Coupling from 4D TEE and CT

Ionasec, R. I., Voigt, I., Georgescu, B., Wang, Y., Houle, H., Joachim Hornegger, A., Navab, N., & Comaniciu, D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04271-3_93 · Cited by 24

Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

Lü, L., Devarakota, P., Vikal, S., Wu, D., Zheng, Y., & Wolf, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-05530-5_16 · Cited by 24

Complete Valvular Heart Apparatus Model from 4D Cardiac CT

Grbić, S., Ionasec, R. I., Vitanovski, D., Voigt, I., Wang, Y., Georgescu, B., Navab, N., & Comaniciu, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15705-9_27 · Cited by 24

Spatio-Temporal Constrained Online Layer Separation for Vascular Enhancement in X-Ray Angiographic Image Sequence

Song, S., Du, C., Ai, D., Huang, Y., Song, H., Wang, Y., & Yang, J. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2941659 · Cited by 23

Improving accuracy in coronary lumen segmentation via explicit calcium exclusion, learning-based ray detection and surface optimization

Lugauer, F., Zhang, J., Zheng, Y., Joachim Hornegger, A., & Kelm, B. M. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2043238 · Cited by 23

Joint Cascade Optimization Using A Product Of Boosted Classifiers

Lefakis, L., & Fleuret, F. (2010) · Cited by 23

Learning based coarse-to-fine image registration

Jiang, J., Zheng, S., Toga, A. W., & Tu, Z. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587396 · Cited by 22

L1-norm latent SVM for compact features in object detection

Tan, M., Pan, G., Wang, Y., Zhang, Y., & Wu, Z. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.09.054 · Cited by 22

Fast Automatic Segmentation of the Esophagus from 3D CT Data Using a Probabilistic Model

Johannes Feulner, A., Zhou, S. K., Cavallaro, A., Seifert, S., Joachim Hornegger, A., & Comaniciu, D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_32 · Cited by 22

A Learning Based Approach for 3D Segmentation and Colon Detagging

Zhuowen Tu, A., Zhou, X. S., Comaniciu, D., & Bogoni, L. (2006) · Lecture notes in computer science · doi:10.1007/11744078_34 · Cited by 22

Improved classification with allocation method and multiple classifiers

Karakatič, S., & Podgorelec, V. (2015) · Information Fusion · doi:10.1016/j.inffus.2015.12.006 · Cited by 22

Predictors of all-cause mortality among 514,866 participants from the Korean National Health Screening Cohort

Ahn, C., Hwang, Y., & Park, S. K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185458 · Cited by 21

Patch-Based Adaptive Background Subtraction for Vascular Enhancement in X-Ray Cineangiograms

Song, S., Frangi, A. F., Yang, J., Ai, D., Du, C., Huang, Y., Song, H., Zhang, L., Han, Y., & Wang, Y. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2892072 · Cited by 21

Noninvasive hemodynamic assessment, treatment outcome prediction and follow‐up of aortic coarctation from MR imaging

Ralovich, K., Lucian Mihai, I. T. U., Vitanovski, D., Sharma, P. K., Ionasec, R. I., Mihalef, V., Krawtschuk, W., Zheng, Y., Everett, A. D., Pongiglione, G., et al. (2015) · Medical Physics · doi:10.1118/1.4914856 · Cited by 21

Learning Exemplar-Based Categorization for the Detection of Multi-View Multi-Pose Objects

Shan, Y., Han, F., Sawhney, H., & Kumar, R. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.168 · Cited by 21

Fast and Automatic Heart Isolation in 3D CT Volumes: Optimal Shape Initialization

Zheng, Y., Vega-Higuera, F., Zhou, S. K., & Comaniciu, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15948-0_11 · Cited by 20

A discriminative model-constrained EM approach to 3D MRI brain tissue classification and intensity non-uniformity correction

Wels, M., Zheng, Y., Huber, M., Joachim Hornegger, A., & Comaniciu, D. (2011) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/11/007 · Cited by 20

Unsupervised sub-categorization for object detection: Finding cars from a driving vehicle

Wijnhoven, R. G. J., & de With, P. H. N. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130504 · Cited by 20

Fast AdaBoost training using weighted novelty selection

Seyedhosseini, M., Paiva, A. R. C., & Taşdizen, T. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033366 · Cited by 20

Implicit hierarchical boosting for multi-view object detection

Perrotton, X., Sturzel, M., & Roux, M. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540115 · Cited by 20

Robust object detection using marginal space learning and ranking-based multi-detector aggregation: Application to left ventricle detection in 2D MRI images

Zheng, Y., Lu, X., Georgescu, B., Littmann, A., Mueller, E., & Comaniciu, D. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206808 · Cited by 20

Boosting-Based Face Detection and Adaptation

Zhang, C. (2010) · Synthesis lectures on computer vision · doi:10.1007/978-3-031-01809-1 · Cited by 20

Supervised Action Classifier: Approaching Landmark Detection as Image Partitioning

Xu, Z., Huang, Q., Park, J., Chen, M., Xu, D., Yang, D., Liu, D. S., & Zhou, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_39 · Cited by 20

Automatic Fetal Measurements in Ultrasound Using Constrained Probabilistic Boosting Tree

Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2007) · Springer eBooks · doi:10.1007/978-3-540-75759-7_69 · Cited by 20

An integrated framework for image segmentation and perceptual grouping

Zhuowen Tu, A. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.36 · Cited by 20

Fast and Robust 3-D MRI Brain Structure Segmentation

Wels, M., Zheng, Y., Carneiro, G., Huber, M., Joachim Hornegger, A., & Comaniciu, D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04271-3_70 · Cited by 19

A probabilistic, hierarchical, and discriminant framework for rapid and accurate detection of deformable anatomic structure

Zhou, S. K., Guo, F., Park, J. H., Carneiro, G., Jackson, J. I., Brendel, M., Simopoulos, C., Otsuki, J., & Comaniciu, D. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409045 · Cited by 19

Kernel Discriminative Random Fields for land cover classification

Roscher, R., Waske, B., & Förstner, W. (2010) · IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) · doi:10.1109/prrs.2010.5742801 · Cited by 19

Learning to question

Das, M., Morales, G. D. F., Gionis, A., & Weber, I. G. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487653 · Cited by 19

Database-guided breast tumor detection and segmentation in 2D ultrasound images

Zhang, J., Zhou, S. K., Brunke, S. S., Lowery, C., & Comaniciu, D. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.844558 · Cited by 19

Graph-shifts: Natural image labeling by dynamic hierarchical computing

Corso, J. J., Yuille, A., & Zhuowen Tu, A. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587490 · Cited by 19

Automatic Delineation of Left and Right Ventricles in Cardiac MRI Sequences Using a Joint Ventricular Model

Lu, X., Wang, Y., Georgescu, B., Littmann, A., & Comaniciu, D. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21028-0_31 · Cited by 18

Volumetric Myocardial Mechanics from 3D+t Ultrasound Data with Multi-model Tracking

Wang, Y., Georgescu, B., Houle, H. C., & Comaniciu, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15835-3_19 · Cited by 18

A learning based hierarchical model for vessel segmentation

Socher, R., Barbu, A., & Comaniciu, D. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4541181 · Cited by 18

Hierarchy of Alternating Specialists for Scene Recognition

Kim, H. J., & Frahm, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_28 · Cited by 18

Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach

Wang, Y., Georgescu, B., Chen, T., Wu, W. H., Wang, P., Lu, X., Ionasec, R. I., Zheng, Y., & Comaniciu, D. (2012) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-94-007-5446-1_9 · Cited by 18

Image-Based Device Tracking for the Co-registration of Angiography and Intravascular Ultrasound Images

Wang, P., Chen, T., Ecabert, O., Prummer, S., Ostermeier, M., & Comaniciu, D. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23623-5_21 · Cited by 18

Fully automated segmentation of multiple sclerosis lesions in multispectral MRI

Wels, M., Huber, M., & Joachim Hornegger, A. (2008) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661808020235 · Cited by 18

Entanglement and Differentiable Information Gain Maximization

Montillo, A., Tu, J., Shotton, J., Winn, J. M., Iglesias, J. E., Metaxas, D., & Criminisi, A. (2013) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-4929-3_19 · Cited by 18

Normalized Autobinomial Markov Channels For Pedestrian Detection

Ţoca, C., Ciuc, M., & Pătraşcu, C. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.175 · Cited by 17

High-performance on-road vehicle detection in monocular images

Gabb, M., Löhlein, O., Wagner, R., Westenberger, A., Fritzsche, M., & Dietmayer, K. (2013) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2013.6728254 · Cited by 17

Discriminative Context Modeling Using Auxiliary Markers for LV Landmark Detection from a Single MR Image

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Marginal Space Deep Learning: Efficient Architecture for Detection in Volumetric Image Data

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