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Full-Text Articles in Physical Sciences and Mathematics
Bounding The Probability Of Error For High Precision Recognition, Andrew Kae, Gary B. Huang, Erik G. Learned-Miller
Bounding The Probability Of Error For High Precision Recognition, Andrew Kae, Gary B. Huang, Erik G. Learned-Miller
Erik G Learned-Miller
We consider models for which it is important, early in proces sing, to estimate some variables with high precision, but perhaps at relative ly low rates of recall. If some variables can be identified with near certainty, then th ey can be conditioned upon, allowing further inference to be done efficiently. Spe cifically, we consider optical character recognition (OCR) systems that can be boo tstrapped by identify- ing a subset of correctly translated document words with ver y high precision. This “clean set” is subsequently used as document-specific train ing data. While many current OCR systems produce measures of confidence …
Non-Parametric Curve Alignment, Marwan Mattar, Michael G. Ross, Erik G. Learned-Miller
Non-Parametric Curve Alignment, Marwan Mattar, Michael G. Ross, Erik G. Learned-Miller
Erik G Learned-Miller
Congealing is a flexible nonparametric data-driven framework for the joint alignment of data. It has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale MRI images, color images of cars and faces, and 3D brain volumes. This research enhances congealing to practically and effectively apply it to curve data. We develop a parameterized set of nonlinear transformations that allow us to apply congealing to this type of data. We present positive results on aligning synthetic and real curve data sets and conclude with a discussion on extending this work to simultaneous …