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Rene Vidal

Photo of Dr. Rene Vidal

Director, Vision Dynamics and Learning Lab

Research Interests: Medical learning; Computer vision; Biomedical image analysis; Dynamical systems; Robotics; Signal processing

Background

Dr. René Vidal is a professor of biomedical engineering at the Whiting School of Engineering and the Johns Hopkins School of Medicine. His research focuses on biomedical imaging, computer vision and machine learning. Dr. Vidal serves as the director of the Vision Dynamics and Learning Lab in the Center for Imaging Science.

He is currently engaged in the development of mathematical methods for the interpretation of high-dimensional data, such as images, videos and biomedical data.

Dr. Vidal received his B.S. degree in electrical engineering (valedictorian) from the Pontificia Universidad Catolica de Chile in 1997. He earned his M.S. and Ph.D. degrees in electrical engineering and computer sciences from the University of California, Berkeley in 2000 and 2003, respectively. He was a research fellow at the National ICT Australia in 2003. He joined the Johns Hopkins faculty in 2004. 

Dr. Vidal is the recipient of numerous awards for his work, including the 2012 J.K. Aggarwal Prize, the 2012 Best Paper Award in Medical Robotics and Computer Assisted Interventions (with Benjamin Bejar and Luca Zappella) and the 2011 Best Paper Award Finalist at the Conference on Decision and Control (with Roberto Tron and Bijan Afsari).

He is chair of the advisory board of the Computer Vision Foundation and associate editor of the journals Computer Vision and Image Understanding, Medical Image Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence and SIAM Journal on Imaging Sciences.

Dr. Vidal is a fellow of the International Association of Pattern Recognition and the Institute of Electrical and Electronics Engineers.

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Titles

  • Director, Vision Dynamics and Learning Lab

Research & Publications

Research Summary

As director of the Center for Imaging Science’s Vision Dynamics and Learning Lab, Dr. Vidal’s research focuses on several key areas:

  • Biomedical image analysis: surgical gesture and skill recognition, analysis of high angular resolution diffusion imaging (HARDI), classification of stem cell derived cardiac myocites, interactive medical image segmentation
  • Computer vision: camera sensor networks, activity recognition, dynamic texture segmentation and recognition, 3D motion segmentation, non-rigid shape and motion analysis, structure from motion and multiple view geometry, omnidirectional vision
  • Machine learning: manifold clustering, kernels on dynamical systems, GPCA, kernel GPCA, dynamic GPCA
  • Dynamical systems: observability, identification, realization, metrics and topology for hybrid systems
  • Robotics: formation control of teams of non-holonomic robots, coordination and control of multiple autonomous vehicles for pursuit-evasion games, multiple view motion estimation and control for landing an unmanned aerial vehicle
  • Signal processing: consensus on manifolds, distributed optimization, compressive sensing

 

Keen focus on the development of computational methods includes: 1) inferring models from images (image/video segmentation, motion segmentation), static data (subspace clustering) or dynamic data (identification of hybrid systems); and 2) using these models to accomplish a complex task, such as tracking fibers in the brain, recognizing actions in videos, landing a helicopter on a moving platform, pursuing a team of evaders or following a formation.

Lab

Selected Publications

View all on Pubmed

Li CG, You C, Vidal R. "Structured sparse subspace clustering: a joint affinity learning and subspace clustering framework." IEEE Trans Image Process. 2017 Apr 6. doi: 10.1109/TIP.2017.2691557. [Epub ahead of print]

Tsakiris M, Vidal R. "Algebraic clustering of affine subspaces." IEEE Trans Pattern Anal Mach Intell. 2017 Mar 6. doi: 10.1109/TPAMI.2017.2678477. [Epub ahead of print]

Ahmidi N, Tao L, Sefati S, Gao Y, Lea C, Bejar B, Zappella L, Khudanpur S, Vidal R, Hager GD. "A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery." IEEE Trans Biomed Eng. 2017 Jan 4. doi: 10.1109/TBME.2016.2647680. [Epub ahead of print]

Nassif M, Valenzuela V, Rojas-Rivera D, Vidal R, Matus S, Castillo K, Fuentealba Y, Kroemer G, Levine B, Hetz C. "Pathogenic role of BECN1/Beclin 1 in the development of amyotrophic lateral sclerosis." Autophagy. 2014 Jul;10(7):1256-71. doi: 10.4161/auto.28784. Epub 2014 May 12.

Valdes P, Mercado G, Vidal RL, Molina C, Parsons G, Court FA, Martinez A, Galleguillos D, Armentano D, Schneider BL, Hetz C. "Control of dopaminergic neuron survival by the unfolded protein response transcription factor XBP1." Proc Natl Acad Sci U S A. 2014 May 6;111(18):6804-9. doi: 10.1073/pnas.1321845111. Epub 2014 Apr 21.

Activities & Honors

Honors

  • Outstanding Reviewer Award, IEEE Conference on Computer Vision and Pattern Recognition, 2015
  • Outstanding Reviewer Award, IEEE Conference on Computer Vision and Pattern Recognition, 2013
  • Best Paper Award for “Efficient Object Localization and Pose Estimation with 3D Wireframe Models", IEEE Workshop on 3D Representation and Recognition, 2013
  • Best Paper Award for “Joint Dictionary Learning for Categorization of Images using a Max-Margin Framework”, Pacific-Rim Symposium on Image and Video Technology, 2013
  • Best Paper Award for "Intrinsic Consensus on SO(3) with Almost-Global Convergence", IEEE Conference on Decision and Control, 2012
  • J. K. Aggarwal Prize "for outstanding contributions to generalized principal component analysis (GPCA) and subspace clustering in computer vision and pattern recognition", 2012
  • Best Paper Award in Medical Robotics and Computer Assisted Interventions for “Surgical Gesture Classification from Video Data”, MICCAI, 2012
  • Best Paper Award Runner Up for “Average Consensus on Riemannian Manifolds with Bounded Curvature”, 50th IEEE Conference on Decision and Control, 2011
  • General Chairs’ Recognition Award for Interactive Papers, 48th IEEE Conference on Decision and Control, 2009
  • Outstanding Reviewer Award, IEEE International Conference on Computer Vision, 2009
  • Outstanding Reviewer Award, IEEE Conference on Computer Vision and Pattern Recognition, 2009
  • Young Investigator Award, Office of Naval Research, 2009
  • Sloan Research Fellowship, Alfred P. Sloan Foundation, 2009
  • Outstanding Reviewer Award, IEEE Conference on Computer Vision and Pattern Recognition, 2008
  • VIBOT Fellowship in Vision and Robotics, 2006 - 2007

Memberships

  • International Association of Pattern Recognition
  • Institute of Electrical and Electronics Engineers
  • Association for Computing Machinery
  • Society for Industrial and Applied Mathematics

Professional Activities

  • Chair, Advisory Board, Computer Vision Foundation, 2016
  • Chair, J.K Aggarwal Prize Committee, 2016
  • Member, J.K. Aggarwal Prize Committee, 2014
  • Program Chair, IEEE International Conference on Computer Vision, 2015
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