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Type of Document	Dissertation
Author	Munich, Mario Enrique
Author's Email Address	mariomu AT vision.caltech.edu
URN	etd-03152006-094551
Persistent URL	http://resolver.caltech.edu/CaltechETD:etd-03152006-094551
Title	Visual input for pen-based computers
Degree	PhD
Option	Electrical Engineering
Advisory Committee	Advisor Name Title Pietro Perona Committee Chair Demitri Psaltis Committee Member James Arvo Committee Member Michael C. Burl Committee Member Yaser S. Abu-Mostafa Committee Member
Keywords	none
Date of Defense	2000-01-21
Availability	unrestricted
Abstract The development of computer technology has had a parallel evolution of the interface between humans and machines, giving rise to interface systems inspired by human communication. Vision has been demonstrated to be the sense of choice for face recognition, gesture recognition, lip reading, etc. This thesis presents the design and implementation of a camera-based, human-computer interface for acquisition of handwriting. The camera focuses on the sheet of paper and images the hand writing; computer analysis of the resulting sequence of images enables the trajectory of the pen to be tracked and the times when the pen is in contact with the paper to be detected. The recovered trajectory is shown to have sufficient spatio-temporal resolution and accuracy to enable handwritten character recognition. Signatures can be acquired with the camera-based interface with enough resolution to perform verification. This thesis describes the performance of a visual-acquisition signature verification system, emphasizing the importance of the parameterization of the signature to achieving good classification results. The generalization error of the verification algorithm is estimated using a technique that overcomes the small number of example signatures and forgeries provided by the subjects. The problem of establishing correspondence and measuring the similarity of a pair of planar curves, in our case a pair of signatures, arises in many application in computer vision and pattern recognition. This thesis presents a new method for comparing planar curves and for performing matching at sub-sampling resolution. The analysis of the algorithm as well as its structural properties are described. The performance of the new technique is evaluated for the problem of signature verification and compared to the performance of the well-known Dynamic Programming Matching algorithm.
Files	Filename       Size       Approximate Download Time (Hours:Minutes:Seconds)     28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access    thesis.pdf 3.50 Mb 00:16:11 00:08:19 00:07:17 00:03:38 00:00:18   thesis.ps.gz 2.21 Mb 00:10:13 00:05:15 00:04:36 00:02:18 00:00:11