The Vision & Image Understanding (VIU) Lab is a part of the Electrical and Computer Engineering department in Johns Hopkins University. We focus on several theoretical and application aspects of computer vision and image understanding. 

Advancing research in computer vision is one of the most important aspects in developing robust artificial intelligent systems. We believe it is critical to develop strong reasoning abilities about the visual world and in that attempt, we work on several areas related to computer vision and image understanding such as recognition, detection, image restoration, user authentication, crowd analytics, cross spectrum face synthesis, domain adaptation, open set recognition, bio-medical image analysis, biometrics, etc. Specifically, we focus on developing a variety of novel machine learning techniques, such as end-to-end deep learning and neural networks for these applications.

Selected Publications

“Deep learning for understanding faces: machines may be just as good, or better, than humans”

R. Ranjan, S. Sankaranarayanan, A. Bansal, N. Bodla, J-C. Chen, V. M. Patel, C. D. Castillo and R. Chellappa

“HyperFace: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition”

R. Ranjan, V. M. Patel, and R. Chellappa

“Unconstrained still/video-based face verification with deep convolutional neural networks”

J-C. Chen, R. Ranjan, S. Sankaranarayanan, A. Kumar, C-H. Chen, V. M. Patel, C. D. Castillo, and R. Chellappa

“The robust sparse Fourier transform and its application in radar signal processing”

S. Wang, V. M. Patel and A. Petropulu
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