Books > Professional & Technical > Energy technology & engineering > Electrical engineering
|
Buy Now
Image Segmentation and Compression Using Hidden Markov Models (Paperback, Softcover reprint of the original 1st ed. 2000)
Loot Price: R4,189
Discovery Miles 41 890
|
|
Image Segmentation and Compression Using Hidden Markov Models (Paperback, Softcover reprint of the original 1st ed. 2000)
Series: The Springer International Series in Engineering and Computer Science, 571
Expected to ship within 10 - 15 working days
|
In the current age of information technology, the issues of
distributing and utilizing images efficiently and effectively are
of substantial concern. Solutions to many of the problems arising
from these issues are provided by techniques of image processing,
among which segmentation and compression are topics of this book.
Image segmentation is a process for dividing an image into its
constituent parts. For block-based segmentation using statistical
classification, an image is divided into blocks and a feature
vector is formed for each block by grouping statistics of its pixel
intensities. Conventional block-based segmentation algorithms
classify each block separately, assuming independence of feature
vectors. Image Segmentation and Compression Using Hidden Markov
Models presents a new algorithm that models the statistical
dependence among image blocks by two dimensional hidden Markov
models (HMMs). Formulas for estimating the model according to the
maximum likelihood criterion are derived from the EM algorithm. To
segment an image, optimal classes are searched jointly for all the
blocks by the maximum a posteriori (MAP) rule. The 2-D HMM is
extended to multiresolution so that more context information is
exploited in classification and fast progressive segmentation
schemes can be formed naturally. The second issue addressed in the
book is the design of joint compression and classification systems
using the 2-D HMM and vector quantization. A classifier designed
with the side goal of good compression often outperforms one aimed
solely at classification because overfitting to training data is
suppressed by vector quantization. Image Segmentation and
Compression Using Hidden Markov Models is an essential reference
source for researchers and engineers working in statistical signal
processing or image processing, especially those who are interested
in hidden Markov models. It is also of value to those working on
statistical modeling.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.