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Digital Image Processing, Global Edition

Digital Image Processing, Global Edition

Wydawnictwo Pearson Education
Data wydania 01/11/2017
Wydanie Czwarte
Liczba stron 1024
Forma publikacji książka w miękkiej oprawie
Poziom zaawansowania Dla szkół wyższych i kształcenia podyplomowego
Język angielski
ISBN 9781292223049
Kategorie Inżynieria elektryczna, Przetwarzanie obrazu
288.43 PLN (z VAT)
$78.69 / €68.03 / £59.84 /
Produkt na zamówienie
Przesyłka w 3-4 tygodnie
Do schowka

Opis książki

For courses in Image Processing and Computer Vision. Introduce your students to image processing with the industry's most prized text For 40 years, Image Processing has been the foundational text for the study of digital image processing. The book is suited for students at the college senior and first-year graduate level with prior background in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. As in all earlier editions, the focus of this edition of the book is on fundamentals. The 4th Edition, which celebrates the book's 40th anniversary, is based on an extensive survey of faculty, students, and independent readers in 150 institutions from 30 countries. Their feedback led to expanded or new coverage of topics such as deep learning and deep neural networks, including convolutional neural nets, the scale-invariant feature transform (SIFT), maximally-stable extremal regions (MSERs), graph cuts, k-means clustering and superpixels, active contours (snakes and level sets), and exact histogram matching. Major improvements were made in reorganizing the material on image transforms into a more cohesive presentation, and in the discussion of spatial kernels and spatial filtering. Major revisions and additions were made to examples and homework exercises throughout the book. For the first time, we added MATLAB projects at the end of every chapter, and compiled support packages for students and faculty containing, solutions, image databases, and sample code.

Digital Image Processing, Global Edition

Spis treści

1 Introduction

1.1 What is Digital Image Processing?

1.2 The Origins of Digital Image Processing

1.3 Examples of Fields that Use Digital Image Processing

Gamma-Ray Imaging

X-Ray Imaging

Imaging in the Ultraviolet Band

Imaging in the Visible and Infrared Bands

Imaging in the Microwave Band

Imaging in the Radio Band

Other Imaging Modalities

1.4 Fundamental Steps in Digital Image Processing

1.5 Components of an Image Processing System

2 Digital Image Fundamentals

2.1 Elements of Visual Perception

Structure of the Human Eye

Image Formation in the Eye

Brightness Adaptation and Discrimination

2.2 Light and the Electromagnetic Spectrum

2.3 Image Sensing and Acquisition

Image Acquisition Using a Single Sensing Element

Image Acquisition Using Sensor Strips

Image Acquisition Using Sensor Arrays

A Simple Image Formation Model

2.4 Image Sampling and Quantization

Basic Concepts in Sampling and Quantization

Representing Digital Images

Linear vs. Coordinate Indexing

Spatial and Intensity Resolution

Image Interpolation

2.5 Some Basic Relationships Between Pixels

Neighbors of a Pixel

Adjacency, Connectivity, Regions, and Boundaries

Distance Measures

2.6 Introduction to the Basic Mathematical Tools Used in Digital Image Processing

Elementwise versus Matrix Operations

Linear versus Nonlinear Operations

Arithmetic Operations

Set and Logical Operations

Basic Set Operations

Logical Operations

Fuzzy Sets

Spatial Operations

Single-Pixel Operations

Neighborhood Operations

Geometric Transformations

Image Registration

Vector and Matrix Operations

Image Transforms

Probability and Random Variables

3 Intensity Transformations and Spatial Filtering

3.1 Background

The Basics of Intensity Transformations and Spatial Filtering

About the Examples in this Chapter

3.2 Some Basic Intensity Transformation Functions

Image Negatives

Log Transformations

Power-Law (Gamma) Transformations

Piecewise Linear Transformation Functions

Contrast Stretching

Intensity-Level Slicing

Bit-Plane Slicing

3.3 Histogram Processing

Histogram Equalization

Histogram Matching (Specification)

Exact Histogram Matching (Specification)



Computing the neighborhood averages and extracting the K-tuples:

Exact Histogram Specification Algorithm

Local Histogram Processing

Using Histogram Statistics for Ima

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