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!
Projects

Machine Learning

Sparse Subspace Clustering

We develop sparse representations of data points to cluster high-dimensional data into lower dimensional subspaces. These types of problems are highly applicable in the age of Big Data and specificially useful in the realm of computer vision.
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Computer Vision

Object Categorization and Segmentation

We combine category-level (top-down) and pixel-level (bottom-up) information to simultaneously categorize and segment objects in a 2D image using graph theory and sparse representation theory.
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Biomedical Imaging

Fiber Tracking in the Brain

We use diffusion MRIs to develop mathematical frameworks for registration, estimation, segmentation and tracking of white matter nerve fibers in order to discover biomarkers for neurological diseases.
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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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You can also check out the Vision Lab app iMixPics, developed by members of our lab, which allows users to overlay and combine multiple photos using interactive image segmentation techniques. This app is now available free at the iTunes App Store for iPhone, iPad, and iPod touch, under Johns Hopkins Mobile medicine.