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Digital Image Processing

By: Material type: TextTextPublication details: Chennai Pearson 2018Edition: 4thDescription: 1019pISBN:
  • 9789353062989
Subject(s): DDC classification:
  • 621.367 GON-D
Online resources:
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Holdings
Item type Current library Collection Call number Status Date due Barcode
Reference Book Reference Book Amity Central Library ASET CSE Reference 621.367 GON-D (Browse shelf(Opens below)) Not for loan 28540
Books Books Amity Central Library AIIT 621.367 GON-D (Browse shelf(Opens below)) Available 28541
Books Books Amity Central Library AIIT 621.367 GON-D (Browse shelf(Opens below)) Available 28542
Books Books Amity Central Library AIIT 621.367 GON-D (Browse shelf(Opens below)) Available 28543
Books Books Amity Central Library AIIT 621.367 GON-D (Browse shelf(Opens below)) Available 28544

Table of Content
"
Chapter 1 Introduction
Chapter 2 Digital Image Fundamentals
Chapter 3 Intensity Transformations and Spatial Filtering
Chapter 4 Filtering in the Frequency Domain
Chapter 5 Image Restoration and Reconstruction
Chapter 6 Wavelet and Other Image Transforms
Chapter 7 Color Image Processing
Chapter 8 Image Compression and Watermarking
Chapter 9 Morphological Image Processing
Chapter 10 Image Segmentation I: Edge Detection,
Chapter 11 Image Segmentation II: Active Contours: Snakes and Level Sets
Chapter 12 Feature Extraction
Chapter 13 Image Pattern Classification
"


Salient Features
"1. Coverage of graph cuts and their application to segmentation.
2. A discussion of superpixels and their use in region segmentation.
3. 425 new images, 135 new drawings, 220 new exercises and 120 MATLAB projects.
4. Two new chapters:
a. A chapter dealing with active contours for image segmentation, including snakes and level sets.
b. A chapter that brings together wavelets, several new transforms, and many of the image transforms that were scattered throughout the book.
5. A complete update of the image pattern recognition chapter to incorporate new material on deep neural networks, backpropagation, deep learning, and especially, deep convolutional neural networks.
6. Coverage of feature extraction, including the Scale Invariant Feature Transform (SIFT, maximally stable extremal regions (MSERs), and corner detection.
7. Coverage of the fundamentals of spatial filtering, image transforms, and finite differences with a focus on edge detection."

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