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

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Shape Matching and Object Recognition Using Low Distortion Correspondences

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Analyzing appearance and contour based methods for object categorization

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Sharing Visual Features for Multiclass and Multiview Object Detection

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Fast Multi-view Face Detection

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Multilevel image recognition using discriminative patches and kernel covariance descriptor

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Robust pigtail catheter tip detection in fluoroscopy

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A Tree-Structure Classifier Ensemble for Tracked Target Categorization

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Patient-Specific Modeling of the Heart: Applications to Cardiovascular Disease Management

Ionasec, R. I., Voigt, I., Mihalef, V., Grbić, S., Vitanovski, D., Wang, Y., Zheng, Y., Joachim Hornegger, A., Navab, N., Georgescu, B., et al. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15835-3_2 · Cited by 3

Object Categorization in Context Based on Probabilistic Learning of Classification Tree with Boosted Features and Co-occurrence Structure

Atsumi, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41914-0_41 · Cited by 3

Introduction: The Abstract Forest Model

Criminisi, A., & Shotton, J. (2013) · Advances in computer vision and pattern recognition · doi:10.1007/978-1-4471-4929-3_3 · Cited by 3

Generic Object Detection Based on Boosting Embedded with Bag-of-Words

Qiu, X. N., Liu, S. R., & Song, J. T. (2011) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.109.285 · Cited by 3

Clustering of Complex Data-Sets Using Fractal Similarity Measures and Uncertainties

Hoecker, M., Polsterer, K. L., Kügler, S. D., & Heuveline, V. (2015) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/cse.2015.35 · Cited by 3

A Probabilistic Framework for Multiple Organ Segmentation Using Learning Methods and Level Sets

Zhou, S. K., & Xu, D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802581-9.00008-1 · Cited by 3

Supervised Object Boundary Detection Based on Structured Forests

Meng, F., Qi, Z., Cui, L., Chen, Z., & Shi, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24474-7_13 · Cited by 3

Towards Automated Ultrasound Transesophageal Echocardiography and X-Ray Fluoroscopy Fusion Using an Image-Based Co-registration Method

Sun, S., Miao, S., Heimann, T., Chen, T., Kaiser, M., John, M., Girard, E. E., & Liao, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46720-7_46 · Cited by 3

Learning cardiac anatomy

Ghesu, F. C., Georgescu, B., Zhang, Y., Grbić, S., & Comaniciu, D. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817594-1.00014-0 · Cited by 3

Computer-Assisted Coronary CT Angiography Analysis : From Software Development to Clinical Application

Wang, C. (2011) · Cited by 3

Object Category Recognition Using Generative Template Boosting

Peng, S., Lin, L., Porway, J., Sang, N., & Zhu, S. C. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74198-5_16 · Cited by 3

Enhancing Ultrasound-Based Diagnosis of Unilateral Diaphragmatic Paralysis With a Visual Transformer-Based Model

Kalkanis, A., Bakalis, D., Testelmans, D., Buyse, B., Simos, Y. V., Tsamis, K. I., & Manis, G. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3580447 · Cited by 2

Hierarchical Cascade of Classifiers for Efficient Poselet Evaluation

Chen, B., & Bourdev, L. D. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.31 · Cited by 2

Automatic recognition of anatomical regions in three-dimensional medical images

Tóth, M. J., Ruskó, L., & Csébfalvi, B. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.06.018 · Cited by 3

Fully Automated Knowledge-Based Segmentation of the Caudate Nuclei in 3-D MRI

Wels, M., Huber, M., & Joachim Hornegger, A. (2007) · Cited by 2

Semantic Processing of Medical Data

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Probabilistic contour mapping using oriented gradient features and SVM-bagging

Aich, S., Lee, Y.-C., & Lee, C.-W. (2015) · Korea-Japan Joint Workshop on Frontiers of Computer Vision · doi:10.1109/fcv.2015.7103732 · Cited by 2

A New Algorithm of Electronic Cleansing for Weak Faecal-Tagging CT Colonography

Lü, L., Jian, B., Wu, D., & Wolf, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02267-3_8 · Cited by 2

Class-specific object contour detection by iteratively combining context information

Yao, C., Shen, W., Bai, X., & Liu, W. (2011) · International Conference on Information, Communications & Signal Processing (ICICS) · doi:10.1109/icics.2011.6174295 · Cited by 2

Anatomical landmark detection using multiple instance boosting with spatial regularization

Swoboda, P., Liu, D., & Zhou, S. K. (2013) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2013.6556451 · Cited by 2

Learning distance function for regression-based 4D pulmonary trunk model reconstruction estimated from sparse MRI data

Vitanovski, D., Tsymbal, A., Ionasec, R. I., Georgescu, B., Zhou, S. K., Joachim Hornegger, A., & Comaniciu, D. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.878195 · Cited by 2

Incorporating Domain Knowledge into Multistrategical Image Classification

Pan, H., Zhang, N., Han, Q., & Yin, G. (2010) · Database Technology and Applications · doi:10.1109/dbta.2010.5658960 · Cited by 2

Learning discriminative distance functions for valve retrieval and improved decision support in valvular heart disease

Voigt, I., Vitanovski, D., Ionasec, R. I., Tsymal, A., Georgescu, B., Zhou, S. K., Huber, M., Navab, N., Joachim Hornegger, A., & Comaniciu, D. (2010) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.843972 · Cited by 2

Improved Edge Detection Algorithm Based on Decision Tree

Cai, A. P. (2013) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.321-324.1080 · Cited by 2

Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner

Sun, P., & Zhou, J. (2013) · Cited by 2

Endocardium tracking by fusing optical flows in straightened images with learning based detections

Wang, P., Zhou, S. K., & Szucs, M. M. (2011) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2011.5872457 · Cited by 2

Semantic driven hierarchical learning for energy-efficient image classification

Panda, P., & Roy, K. (2017) · Design, Automation, and Test in Europe · doi:10.23919/date.2017.7927242 · Cited by 2

Visual object detection using deformable sparse coding model

Mei, X. (2016) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic.2016.7949489 · Cited by 2

Pedestrian detection using integral channel detection and ADABOOST algorithm

Wagh, K., & Kanade, S. S. (2017) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip.2017.8313745 · Cited by 2

Machine Learning for Brain Image Segmentation

Morra, J. H., Tu, Z., Toga, A. W., & Thompson, P. M. (2010) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-60566-956-4.ch005 · Cited by 2

Discriminative Learning for Anatomical Structure Detection and Segmentation

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Computational Decision Support for Percutaneous Aortic Valve Implantation

Voigt, I., Ionasec, R. I., Georgescu, B., Boese, J. M., Brockmann, G., Joachim Hornegger, A., & Comaniciu, D. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15699-1_26 · Cited by 2

Hierarchical Detection of Multiple Organs Using Boosted Features

Hugueny, S., & Rousson, M. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74272-2_40 · Cited by 2

A Cascade Learning Method for Liver Lesion Detection in CT Images

Wu, D., Liu, D., Suehling, M., Zhou, K. S., & Tietjen, C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36620-8_20 · Cited by 1

Multistrategical Approach in Visual Learning

Nomiya, H., & Uehara, K. (2007) · Springer eBooks · doi:10.1007/978-3-540-76386-4_47 · Cited by 1

Correcting Misalignment of Automatic 3D Detection by Classification: Ileo-Cecal Valve False Positive Reduction in CT Colonography

Lü, L., Wolf, M., Bi, J., & Salganicoff, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-18421-5_12 · Cited by 1

Nonrigid Object Segmentation: Application to Four-Chamber Heart Segmentation

Zheng, Y., & Comaniciu, D. (2014) · Springer eBooks · doi:10.1007/978-1-4939-0600-0_7 · Cited by 1

Sub-Category Optimization through Cluster Performance Analysis for Multi-View Multi-Pose Object Detection

Das, D., Kobayashi, Y., & Kuno, Y. (2011) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e94.d.1467 · Cited by 1

Simultaneous detection of landmarks and key-frame in cardiac perfusion MRI using a joint spatial-temporal context model

Lu, X., Xue, H., Jolly, M., Guetter, C., Kellman, P., Hsu, L., Arai, A. E., Zuehlsdorff, S., Littmann, A., Georgescu, B., et al. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.878529 · Cited by 1

A cost constrained boosting algorithm for fast lesion detection and segmentation

Militzer, A., Tietjen, C., & Joachim Hornegger, A. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.877944 · Cited by 1

Progressive Image Registration Based on Probability Boosting Tree

Guo, L., Yi, J., & Yang, X. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33478-8_52 · Cited by 1

Part-Based Object Detection and Segmentation

Zheng, Y., & Comaniciu, D. (2014) · Springer eBooks · doi:10.1007/978-1-4939-0600-0_5 · Cited by 1

Making a Shallow Network Deep: Growing a Tree from Decision Regions of a Boosting Classifier

Kim, T., Budvytis, I., & Cipolla, R. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.57 · Cited by 1

Robust Model-Based 3D/3D Fusion Using Sparse Matching for Minimally Invasive Surgery

Neumann, D., Grbić, S., John, M., Navab, N., Joachim Hornegger, A., & Ionasec, R. I. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40811-3_22 · Cited by 1

Morphologica l and Functional Modeling of the Heart Valves and Chambers

Ionasec, R. I., Vitanovski, D., & Comaniciu, D. (2011) · Studies in mechanobiology, tissue engineering and biomaterials · doi:10.1007/8415_2011_94 · Cited by 1

Integration and goal-guided scheduling of bottom-up and top-down computing processes in hierarchical models

Zhu, S. C., & Wu, T. (2011) · Cited by 1

Boosted learned kernels for data-driven vesselness measure

Grisan, E. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2250370 · Cited by 1

Multi-Path Marginal Space Learning for Object Detection

Barbu, A. (2014) · Academic press library in signal processing · doi:10.1016/b978-0-12-396501-1.00010-8 · Cited by 1

Optimal Mean Shape for Nonrigid Object Detection and Segmentation

Zheng, Y., & Comaniciu, D. (2014) · Springer eBooks · doi:10.1007/978-1-4939-0600-0_6 · Cited by 1

Comparison of Marginal Space Learning and Full Space Learning in 2D

Zheng, Y., & Comaniciu, D. (2014) · Springer eBooks · doi:10.1007/978-1-4939-0600-0_3 · Cited by 1

Identify Error-Sensitive Patterns by Decision Tree

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Periocular Region Classifiers

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Training a CNN for Guidewire Detection

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Implementation of a patient-specific cardiac model

Mihalef, V., Mansi, T., Rapaka, S., & Passerini, T. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817594-1.00012-7 · Cited by 1

Robust segmentation and object classification in natural and medical images

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Content-based Image Classification via Visual Learning

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Accurate Regression-Based 4D Mitral Valve Surface Reconstruction from 2D+t MRI Slices

Vitanovski, D., Tsymbal, A., Ionasec, R. I., Schmidt, M., Greiser, A., Mueller, E., Lu, X., Funka-Lea, G., Joachim Hornegger, A., & Comaniciu, D. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24319-6_35 · Cited by 1

Object Detection in Real ImagesPreprint

Prasad, D. K. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1302.5189 · Cited by 1

Probabilistic Cascade Random Fields for Man-Made Structure Detection

Zheng, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12304-7_56 · Cited by 1

Iris Indexing and Ear Classification

Gadde, R. B. (2012) · doi:10.33915/etd.4855 · Cited by 1

Object detection and segmentation using discriminative learning

McMillan, L., & Zhang, J. (2009) · Cited by 1

A quantitative data representation framework for structural and functional MR Imaging with application to prostate cancer detection

Viswanath, S. E. (2012) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t3hq3xvh · Cited by 1

Anatomical Landmark Detection

Liu, D., & Zhou, S. K. (2013) · Springer eBooks · doi:10.1007/978-1-4614-8498-1_27

A Framework of Wire Tracking in Image Guided Interventions

Wang, P., Meyer, A., Chen, T., Zhou, S. K., & Comaniciu, D. (2010) · Advances in computer vision and pattern recognition · doi:10.1007/978-0-85729-057-1_7

Progressive Data Transmission for Hierarchical Detection in a Cloud

Sofka, M., Ralovich, K., Zhang, J., Zhou, S. K., & Comaniciu, D. (2011)

A Theory of Probabilistic Boosting, Decision Trees and MatryoshkiPreprint

Etienne Grossmann, A. (2006) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0607110

Computer-Aided Comparative Assessment of Inflammatory Lesions in MRI of the Spine in Axial Spondyloarthritis Patients

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Using Low Level Gradient Channels for Computationally Efficient Object Detection and Its Application in Logo Detection

Chen, Y., & Thing, V. L. L. (2012) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2012.51

A general method for sensitive identification detection in the terrorist video

Luo, X., Feng, S., Lang, C., Du, X., & Zhang, C. G. (2015) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2808492.2808513

Model-based flow quantification using conditional random fields with global preferences

Wang, Y., Georgescu, B., Datta, S., Liu, S., Thavendiranathan, P., & Vannan, M. A. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235873

Robust tracking of a virtual electrode on a coronary sinus catheter for atrial fibrillation ablation procedures

Wu, W. H., Chen, T., Strobel, N. K., & Comaniciu, D. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.911079

Interactive 2D and volume image segmentation using level sets of probabilities

Liu, Y., & Yu, Y. (2011) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/2077378.2077432

Recognizing Shapes in Video Sequences Using Multi-class Boosting

Cuntoor, N. P., & Welborn, M. (2008) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2008.35

Sub-Category Optimization for Multi-view Multi-pose Object Detection

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On the Effects of Normalization in Adaptive MRF Hierarchies

Chen, A. Y. C., & Corso, J. J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12712-0_25

Quantitative integration of imaging and non-imaging data

Tiwari, P. (2012) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t3xp73gj

Scene labeling with supervised contextual models

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Landmark Detection Using Submodular Functions

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Integrated Detection Network for Multiple Object Recognition

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Object cutout from multiview images using level set of probabilities

Zhao, M., Hao, X., Cheng, C., & Zhang, Z. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2245144

Towards public events image content understanding

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Scalable pattern retrieval from videos using a random forest index

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