The Vision, Dynamics and Learning Lab is a research lab in the Department of Biomedical Engineering at Johns Hopkins University. We are a member of the Center for Imaging Science (CIS) and of the Whitaker Institute of Biomedical Engineering.

Our research spans a wide range of areas in biomedical imaging, computer vision, dynamics and controls, machine learning and robotics. In particular, we are interested in inference problems involving geometry, dynamics, photometry and statistics, such as (1) inferring models from images (image/video segmentation and structure from motion), static data (generalized PCA) or dynamic data (identification of hybrid systems), and (2) using such models to accomplish a complex mission (land a helicopter, pursue a team of evaders, follow a formation). Please feel free to contact any member of this lab if you have any questions or comments!
News

Jun 17

Spring Reading Group Schedule

Aug 10

Workshop on Dynamical Vision Program

Aug 10

Workshop on Computer Vision for Developing Regions Program

July 07

Dr.Vidal's Research in the Engineering Magazine

July 29

Dr. Vidal's Research in the Whiting School of Engineering Website
Projects

Biomedical Imaging

Heart Motion Analysis

Given multiple cardiac MRI images, we develop algorithms for building and updating real-time 3D model of the heart to be used by the surgeon in MRI-guided heart surgery.
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Computer Vision

Segmentation of Dynamic Scenes

We develop algorithms for fitting multiple 2D and 3D motion models to a video sequence of a scene containing multiple moving objects, without knowing which pixels move according to which model.
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Machine Learning

Generalized Principal Component Analysis (GPCA)

Given a set of points lying in multiple linear subspaces, we develop algebraic-geometric algorithms for learning the number of susbpaces, a basis for each subspace, and the segmentation of the data.
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Robotics

Landing an aerial vehicle

We develop algorithms for vision-based landing of an Unmanned Aerial Vehicle (UAV) on a moving landing deck, using multiple view geometry.
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Hybrid Systems

Hybrid System Identification

Given input output data generated by a dynamical system with both continuous and discrete dynamics, we look at the problem of identifying the model parameters and the mode sequence.
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