Average Reviews:
(More customer reviews)>>Update on 02/11/2010
I spent a few more weeks on this book recently with a bit more emphasis on the programming side. I find the programming part of this book is quite readable and it gives me much insight into the image processing topic. I hereby raise my rate to four stars.
===========
>>Original review in Oct.2009
I borrowed this book from the university library mainly because it provided with Matlab examples. This book turned out to be somewhat disappointing:
pros:
1. example Matlab codes, provided in context
2. that is all
cons:
Editing is a nightmare. I don't know why the authors can't spend some more hours proofreading the pre-print they have already spent hundreds of times more hours on.
1. Typos and mistakes are literally on every page.
2. Lots of illustrations other than figures generated by Matlab look like being drawn using paint.exe in Windows. Imaging the quality.
3. Equations are edited in a weird improportionate fashion that the symbols are ugly. Dear lord please use Tex while editing equations. I bet even the equation editor integrated with M$ office 2007 can do better than this.I think it is only worth 1 star at most, as a science and technology book. Since my first intention of borrowing this book is the matlab code, so 2 stars.
Click Here to see more reviews about: Image Processing with MATLAB: Applications in Medicine and Biology (MATLAB Examples)
Image Processing with MATLAB: Applications in Medicine and Biology explains complex, theory-laden topics in image processing through examples and MATLAB algorithms. It describes classical as well emerging areas in image processing and analysis. Providing many unique MATLAB codes and functions throughout, the book covers the theory of probability and statistics, two-dimensional fast Fourier transform, nonlinear diffusion filtering, and partial differential equation (PDE)-based image denoising techniques. It presents intensity-based image segmentation methods, including thresholding techniques as well as K-means and fuzzy C-means clustering techniques. The authors also explore Markov random field (MRF)-based image segmentation, boundary and curvature analysis methods, and parametric and geometric deformable models. The final chapters focus on three specific applications of image processing and analysis.Reducing the need for the trial-and-error way of solving problems, this book helps readers understand advanced concepts by applying algorithms to real-world problems in medicine and biology.A solutions manual is available for instructoes wishing to convert this reference to classroom use.
Get 28% OFF
No comments:
Post a Comment