D. Cobzas

发表

A Survey and Tutorial on Shape and Reflectance Estimation Techniques

M. Jagersand, P. Sturm, D. Cobzas, 2022 .

3 D SSD Tracking with Estimated 3 D Planes CRV

M. Jagersand, P. Sturm, D. Cobzas, 2022 .

Depth and Scene Flow from a Single Moving Camera

Martin Jägersand, Neil Birkbeck, Martin Jägersand, 2010 .

Cognitive Implications of Deep Gray Matter Iron in Multiple Sclerosis

A H Wilman, A. Wilman, Hongfu Sun, 2017, American Journal of Neuroradiology.

Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients

J. Meyerhardt, M. Beg, V. Baracos, 2020, Journal of cachexia, sarcopenia and muscle.

Merging Panoramic Intensity and Range Maps for Robot Localization

M. Jagersand, D. Cobzas, Hong Zhang, 2022 .

Smooth and non-smooth wavelet basis for capturing and representing light

M. Jagersand, D. Cobzas, Cameron Upright, 2008 .

Computational models for computer vision

Radu Horaud, Philippe Martin, Rémi Ronfard, 2005 .

Low-Rank Plus Sparse Decomposition of fMRI Data With Application to Alzheimer's Disease

Linglong Kong, D. Cobzas, Chao Huang, 2022, Frontiers in Neuroscience.

Using Image-Based Panoramic Models for 2D Robot Localization

Dana Cobzas, Hansong Zhang, D. Cobzas, 2000 .

Body Composition Assessment in Axial CT Images Using FEM-Based Automatic Segmentation of Skeletal Muscle

Martin Jägersand, Karteek Popuri, Dana Cobzas, 2016, IEEE Transactions on Medical Imaging.

Tumor invasion margin on the Riemannian space of brain fibers

Martin Jägersand, Dana Cobzas, Albert Murtha, 2012, Medical Image Anal..

3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set

Martin Jägersand, Karteek Popuri, Dana Cobzas, 2012, International Journal of Computer Assisted Radiology and Surgery.

Developmental Hip Dysplasia Diagnosis at Three-dimensional US: A Multicenter Study.

J. Jaremko, S. Pasha, D. Cobzas, 2018, Radiology.

Automated segmentation of muscle and adipose tissue on CT images for human body composition analysis

Dana Cobzas, Howard Chung, Laura Birdsell, 2009, Medical Imaging.

End-to-end detection-segmentation network with ROI convolution

Min Tang, Martin Jägersand, Dornoosh Zonoobi, 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

Segmentation-by-detection: A cascade network for volumetric medical image segmentation

Min Tang, Martin Jägersand, Dana Cobzas, 2017, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

Developmental Hip Dysplasia Diagnosis at Three-dimensional US: A Multicenter Study

J. Jaremko, S. Pasha, D. Cobzas, 2018, Radiology.

Subcortical gray matter segmentation and voxel‐based analysis using transverse relaxation and quantitative susceptibility mapping with application to multiple sclerosis

Dana Cobzas, R Marc Lebel, Hongfu Sun, 2015, Journal of magnetic resonance imaging : JMRI.

Basis constrained 3D scene flow on a dynamic proxy

Martin Jägersand, Dana Cobzas, Neil Birkbeck, 2011, 2011 International Conference on Computer Vision.

Object Centered Stereo: Displacement Map Estimation Using Texture and Shading

Martin Jägersand, Dana Cobzas, Neil Birkbeck, 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

Recent methods for image-based modeling and rendering

Darius Burschka, Gregory D. Hager, Zachary Dodds, 2003, IEEE Virtual Reality, 2003. Proceedings..

On the Applicability of Registration Uncertainty

Sarah F. Frisken, Masashi Sugiyama, W. Wells, 2018, International Conference on Medical Image Computing and Computer-Assisted Intervention.

A Variational Formulation for Discrete Registration

Martin Jägersand, Karteek Popuri, Dana Cobzas, 2013, MICCAI.

Random Walks for Deformable Image Registration

Abhishek Sen, Dana Cobzas, D. Cobzas, 2011, MICCAI.

Stability effects of finite difference methods on a mathematical tumor growth model

Martin Jägersand, Dana Cobzas, Albert Murtha, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set

Mark W. Schmidt, Martin Jägersand, Dana Cobzas, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Biomedical image analysis competitions: The state of current participation practice

Anne L. Martel, Helena R. Torres, Bjoern H Menze, 2022, ArXiv.

A comparative analysis of geometric and image-based volumetric and intensity data registration algorithms

Martin Jägersand, Hong Zhang, Dana Cobzas, 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls.

Dana Cobzas, Hongfu Sun, Alan H. Wilman, 2019, Multiple sclerosis and related disorders.

Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects

Dana Cobzas, Hongfu Sun, Alan H Wilman, 2018, Journal of magnetic resonance imaging : JMRI.

Progressive iron accumulation across multiple sclerosis phenotypes revealed by sparse classification of deep gray matter

Dana Cobzas, Hongfu Sun, A. Wilman, 2017, Journal of magnetic resonance imaging : JMRI.

Increased discrimination in level set methods with embedded conditional random fields

Mark W. Schmidt, Dana Cobzas, D. Cobzas, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Variational Shape and Reflectance Estimation Under Changing Light and Viewpoints

Martin Jägersand, Dana Cobzas, Peter F. Sturm, 2006, ECCV.

View Dependent Texturing Using a Linear Basis

Martin Jägersand, Dana Cobzas, Neil Birkbeck, 2010, Image and Geometry Processing for 3-D Cinematography.

A Three-tier Hierarchical Model for Capturing and Rendering of 3D Geometry and Appearance from 2D Images

Dana Cobzas, Neil Birkbeck, Martin Jagersand, 2008 .

Modulating View-dependent Textures

Martin Jägersand, Dana Cobzas, Keith Yerex, 2004, Eurographics.