Mathematical Morphology in Image Processing
By (author) Edward R. Dougherty
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Mathematical Morphology in Image Processing by Edward R. Dougherty
Book DescriptionPresents the statistical analysis of morphological filters and their automatic optical design, the development of morphological features for image signatures, and the design of efficient morphological algorithms. Extends the morphological paradigm to include other branches of science and mathematics.;This book is designed to be of interest to optical, electrical and electronics, and electro-optic engineers, including image processing, signal processing, machine vision, and computer vision engineers, applied mathematicians, image analysts and scientists and graduate-level students in image processing and mathematical morphology courses.
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Book DetailsISBN: 9780824787240
(279mm x 216mm x 34mm)
Imprint: Marcel Dekker Inc
Publisher: Taylor & Francis Inc
Publish Date: 25-Sep-1992
Country of Publication: United States
Books By Author Edward R. Dougherty
Error Estimation for Pattern Recognition, Hardback (June 2015)
This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification.
Epistemology of the Cell, Hardback (August 2011)
This text examines the place of biological knowledge within the framework of science as a whole and addresses issues focused on the specific nature of biology, how biology is studied, and how biological knowledge is translated into applications, in particular, with regard to medicine.
Probabilistic Boolean Networks, Paperback (January 2010)
The first comprehensive treatment of probabilistic Boolean networks, unifying different strands of current research and addressing emerging issues.
Genomic Signal Processing, Hardback (July 2007)» View all books by Edward R. Dougherty
Genomic signal processing (GSP) requires the development of both nonlinear dynamical models that represent genomic regulation, and diagnostic and therapeutic tools based on these models. This book facilitates these developments by providing mathematical definitions and propositions for the main elements of GSP.
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