Shih_Image_Processing_and_Mathematical_Morpholo.pdf

xxii Author
Dr. Shih has contributed as a steering member, committee member, and
session chair for numerous professional conferences and workshops. He is
presently on the Technical Committee of the IEEE Circuits and Systems
Society in Visual Signal Processing & Communications and Multimedia
Systems & Applications. He was the recipient of the Research Initiation
Award from the National Science Foundation in 1991. He won the Honorable
Mention Award from the International Pattern Recognition Society for an
outstanding paper and also won the Best Paper Award at the International
Symposium on Multimedia Information Processing. Dr. Shih has received
numerous grants from the National Science Foundation, the navy and air
force, and industry. He has received several research merit awards at the
New Jersey Institute of Technology. He has served several times on the
Proposal Review Panel of the National Science Foundation.
Professor Shih was one of the researchers who initiated mathematical morphology
research with applications to image processing, feature extraction,
and object representation. His IEEE PAMI article “Threshold decomposition of
grayscale morphology into binary morphology” is a breakthrough paper that
solves the bottleneck problem in grayscale morphological processing. His
several IEEE Image Processing and IEEE Signal Processing articles detail innovations
that achieve fast exact Euclidean distance transformation, robust image
enhancement, edge linking, and image segmentation using the recursive soft
morphological operators, general sweep morphological operators, alternating
sequential morphological fi lters, and regulated morphological fi lters that
he developed.
In the fi eld of digital document processing, Professor Shih developed a
fuzzy model for unsupervised character classifi cation, fuzzy typographical
analysis for character classifi cation, an adaptive system for block segmentation
and classifi cation, and a rule-based character and logo recognition system.
In the fi eld of face recognition, Professor Shih developed automatic
extraction of head and face boundaries and facial features, a hybrid twophase
algorithm for face recognition, multiview face identifi cation and pose
estimation using B-spline interpolation, recognizing facial action units using
independent component analysis and a support vector machine, and facial
expression recognition.
In the fi eld of pattern recognition, Professor Shih developed improved feature
reduction in input and feature spaces, an improved incremental training
algorithm for support vector machines using active query, support vector
machine networks for multiclass classifi cation, improved adaptive resonance
theory networks, model-based partial shape recognition using contour
curvature and affi ne transformation, and a distance-based separator
representation for pattern classifi cation. In the fi eld of image segmentation,
Professor Shih developed automatic seeded region growing for color image
segmentation, locating object contours in complex backgrounds using an
improved snake model, a top-down region-dividing approach for image segmentation,
and edge linking by adaptive mathematical morphology.