Text and Sign Detection

Using a contextual model to eliminate isolated false positives and more fully cover all regions of a detected sign, we are able to robustly detect text and logos with arbitrary sizes and layouts in complex scenes. Read more ...

Papers: MLSP04, CVAVI05, Master's Thesis.

Robust Recognition

By integrating character appearance models more closely with statistical language and lexicon models, as well as a locally adaptive font model, we can more reliably recognize characters from signs in different fonts. Read more ...

Papers: CVPR06, ICDAR07, ICPR08, PAMI09, ICPR10.

Joint Detection and Recognition

By asking the "where?" and "what?" questions of finding and identifying text simultaneously during learning, we can be faster or more accurate than the usual method of learning these independently. Read more ...

Papers: Tech Report UM-CS-2006-054

Learning algorithms that rely on message-passing algorithms can be very slow when state spaces are large, as in most stereo disparity estimation problems. We improve stereo predictions by using an algorithm that can capture the appropriate amount of uncertainty during learning. Read more ...

Papers: Tech Report UM-CS-2007-054, ECCV08, IJCV10

By storing background images for a stationary robot with a moveable vision system, we can perform an efficient matching of the current view to known views with a local deformation model to overcome parallax. The robot learns to perform novel object detection in the presence of camera motion. Read more ...

Papers: ICRA07

The problems of noisy magnetic resonance (MR) images, ambiguous boundaries, along with unknown and irregular shape, must be overcome to accurately estimate the volume of brain tissue affected by ischemic stroke. We use multiple segmentation parameters to create a confidence map that can closely match physician segmentations. Read more ...

Papers: MICCAI03, Tech. Report UM-CS-2003-017.

• Nicholas Howe (Smith College)
• Jacqueline Feild (U. Mass)
• Allen Hanson (U. Mass)
• Erik Learned-Miller (U. Mass)
• Chris Pal (École Polytechnique de Montréal)
• Andrew McCallum (U. Mass)
• Daniel Scharstein (Middlebury College)
• Piyanuch Silapachote (U. Mass)
• Marwan A. Mattar (U. Mass)
• Dima Lisin (MathWorks)
• George Bissias (U. Mass)
• Sai Ravela (MIT)
• Edward M. Riseman (U. Mass)

Undergraduates