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The Computer Vision Laboratory was established in the Computer Science Department at the University of Massachusetts in 1974 with the goal of investigating the scientific principles underlying the construction of integrated vision systems and the application of vision to problems of real-world importance. The emphasis of our work is on vision systems that are capable of functioning flexibly and robustly in complex changing environments.

Click on the panels below to learn more about our latest work:

Multi-view CNN for 3D Shape Recognition, Hang Su, Subhransu Maji, Evangelos Kalogerakis and Erik Learned-Miller, ICCV 2015
Bilinear CNN Models for Fine-grained Visual Recognition, Tsung-Yu Lin, Aruni Roy Chowdhury, Subhransu Maji, ICCV 2015
Tracking with Distribution Fields, Laura Sevilla-Lara and Erik Learned-Miller, CVPR 2012
Background Modeling using Adaptive Pixelwise Kernel Variances in a Hybrid Feature Space, Manjunath Narayana, Allen Hanson, and Erik Learned-Miller, CVPR 2012
Learning Hierarchical Representations for Face Verification with Convolutional Deep Belief Networks, Gary B. Huang, Honglak Lee, and Erik Learned-Miller, CVPR 2012
Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling
Andrew Kae*, Kihyuk Sohn*, Honglak Lee, and Erik Learned-Miller,CVPR 2013
*indicates equal contribution.
Improving Open-Vocabulary Scene Text Recognition, Jacqueline Feild, Erik Learned-Miller, ICDAR 2013
Scene Text Segmentation via Inverse Rendering
Yahan Zhou, Jacqueline Feild, Rui Wang, Erik Learned-Miller, ICDAR 2013