Digital Imaging Processing (CS705) · Department of Computer Science and Engineering. National Institute of Technology Jamshedpur Instructor Dr. Koushlendra Kumar Singh ... Unit Project

National Institute of Technology Jamshedpur

Digital Imaging Processing (CS705)

Department of Computer Science and Engineering


Instructor

Dr. Koushlendra Kumar Singh

Assistant ProfessorDepartment of Computer Science and Engineering

NIT JamshedpurCan catch at : Room No. SA-09

mail at: [email protected]: [email protected]

mailto:[email protected]


Weeks 1 & 2 3

Evaluation Pattern

1. End Semester and Mid semester as per Institute rule 2. Teacher Assessment will divided as follows

a) Unit Project : At the end of each unit student have to implement one small project (Total- 10 marks)

b) Course project: After mid semester each group (3 students) have toimplement a course project and every group have topresent the work. (10 marks)

c) Quiz, Involvement in class and Attendance – 10 marks


Weeks 1 & 2 4

Pre-requisites of the course

Basic concept of signal processing

High school mathematics

C/C++ programming skill, MatLab, Python etc

Probabilistic Methods

Basics of calculus

matrix and determinant


Weeks 1 & 2 5

Course Plan

Introduction to Image Processing

Image Processing and related field, Fundamental Steps in DIP, Components of an Image Processing System

Classification of Image Processing operationsDetails of Image processing operations, , basic relationship, distance metrics, Arithmetic Operations, Logical operation, Set operation, Statistical Operation, Sampling and Quantization

Intensity Transforms and Spatial filteringBackgrounds, basics of intensity transforms and spatial filtering, Intensity transform function, Image negative, Histogram processing for image enhancement, Smoothing spatial filters, The Laplacian, The Gradient,

Filtering in the frequency domainHotelling Transform, Fourier Transforms and properties, FFT (Decimation in Frequencyand Decimation in Time Techniques), Convolution, Correlation, 2-D sampling, Discrete CosineTransform, Frequency domain filtering

Wavelet and Mutiresolution ProcessingExpansion of functions, Multi-resolution analysis, Scaling functions, MRA refinement equation, Wavelet series expansion, Discrete Wavelet Transform (DWT), Continuous Wavelet Transform,


Weeks 1 & 2 6

Image restoration

Introduction to various noise models, restoration in presence of noise only, periodic noise

reduction, linear and position invariant degradation, estimation of degradation function

Mathematical morphology

Erosion and dilation, opening and closing, the Hit-or-Miss transformation, various

morphological algorithms for binary images

Image restoration

Introduction to various noise models, restoration in presence of noise only, periodic noise

reduction, linear and position invariant degradation, estimation of degradation function

Wavelets and multiresolution processing

Image pyramids, subband coding, multiresolution expansions, the Haar transform, wavelet

transform in one and two dimensions, discrete wavelet transform


Weeks 1 & 2 7

Course materials/ Text book/ Reference book

Gonzalez, R. C. and Woods, R. E., "Digital Image Processing", Prentice Hall, 3rd Ed.

Jain, A. K., "Fundamentals of Digital Image Processing", PHI Learning, 1st Ed.

Bernd, J., "Digital Image Processing", Springer, 6th Ed.

Scherzer, O., " Handbook of Mathematical Methods in Imaging", Springer

Burger, W. and Burge, M. J., "Principles of Digital Image Processing", Springer

Digital Signal Processing by Prof. P.K. Biswas Multi rate signal Processing by Prof. V. M. Gadre

The instructor will also provide the slides of course.


What is digital image processing???

Need of digital Image processing

Applications of digital Image Processing

One of the earliest applications of digital image was in the newspaper industry.

First pictures were sent by submarine cable between Londan and New York in 1920s.


Weeks 1 & 2 9


Weeks 1 & 2 10


Weeks 1 & 2 11


Weeks 1 & 2 12


Weeks 1 & 2 13


Weeks 1 & 2 14


Weeks 1 & 2 15

Image Acquisition Process


Weeks 1 & 2 16

Introduction

What is Digital Image Processing?

Digital Image— a two-dimensional function

x and y are spatial coordinates

The amplitude of f is called intensity or gray level at the point (x, y)

Digital Image Processing— process digital images by means of computer, it covers low-, mid-, and high-level processes

low-level: inputs and outputs are images

mid-level: outputs are attributes extracted from input images

high-level: an ensemble of recognition of individual objects

Pixel— the elements of a digital image

( , )f x y


Weeks 1 & 2 17

A Simple Image Formation Model

( , ) ( , ) ( , )

( , ) : intensity at the point ( , )

( , ) : illumination at the point ( , )

(the amount of source illumination incident on the scene)

( , ) : reflectance/transmissivity

f x y i x y r x y

f x y x y

i x y x y

r x y

at the point ( , )

(the amount of illumination reflected/transmitted by the object)

where 0 < ( , ) < and 0 < ( , ) < 1

x y

i x y r x y


Weeks 1 & 2 18

Some Typical Ranges of Reflectance

Reflectance

0.01 for black velvet

0.65 for stainless steel

0.80 for flat-white wall paint

0.90 for silver-plated metal

0.93 for snow


Weeks 1 & 2 19

Image Sampling and Quantization

Digitizing the coordinate values

Digitizing the amplitude values


Weeks 1 & 2 20

Image Sampling and Quantization


Weeks 1 & 2 21

Representing Digital Images

The representation of an M×N numerical array as

(0,0) (0,1) ... (0, 1)

(1,0) (1,1) ... (1, 1)( , )

... ... ... ...

( 1,0) ( 1,1) ... ( 1, 1)

f f f N

f f f Nf x y

f M f M f M N


Weeks 1 & 2 22


► The representation of an M×N numerical array as

0,0 0,1 0, 1

1,0 1,1 1, 1

1,0 1,1 1, 1

...

...

... ... ... ...

...

N

N

M M M N

a a a

a a aA

a a a


Weeks 1 & 2 23


►The representation of an M×N numerical array in MATLAB

(1,1) (1,2) ... (1, )

(2,1) (2,2) ... (2, )( , )

... ... ... ...

( ,1) ( , 2) ... ( , )

f f f N

f f f Nf x y

f M f M f M N


Weeks 1 & 2 24


► Discrete intensity interval [0, L-1], L=2k

► The number b of bits required to store a M × N digitized image

b = M × N × k


Weeks 1 & 2 25



What is a Digital Image? (cont…)

►Common image formats include: 1 sample per point (B&W or Grayscale)

3 samples per point (Red, Green, and Blue)

4 samples per point (Red, Green, Blue, and “Alpha”, a.k.a. Opacity)

►For most of this course we will focus on grey-scale images


Image processing

► An image processing operation typically defines a new image g in terms of an existing image f.

► We can transform either the range of f.

► Or the domain of f:

► What kinds of operations can each perform?


What is DIP? (cont…)►The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes

Low Level Process

Input: ImageOutput: Image

Examples: Noise removal, image sharpening

Mid Level Process

Input: Image Output: Attributes

Examples: Object recognition, segmentation

High Level Process

Input: Attributes Output:Understanding

Examples: Scene understanding, autonomous navigation


Key Stages in Digital Image Processing

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression


Image Acquisition

Image Acquisition

Image Restoration


Segmentation


Image Enhancement

Object Recognition

Problem Domain


Image CompressionIm

ages

taken f

rom

Gonza

lez

& W

oods,

Dig

ital Im

age P

roce

ssin

g (

2002)


Image Enhancement

Image Acquisition

Image Restoration


Segmentation


Image Enhancement

Object Recognition

Problem Domain


Image Compression

Images

taken f

rom

Gonza

lez

& W

oods,

Dig

ital Im

age P

roce

ssin

g (

2002)


Image Restoration

Image Acquisition

Image Restoration


Segmentation


Image Enhancement

Object Recognition

Problem Domain


Image Compression

Images

taken f

rom

Gonza

lez

& W

oods,

Dig

ital Im

age P

roce

ssin

g (

2002)



Image Acquisition

Image Restoration


Segmentation


Image Enhancement

Object Recognition

Problem Domain


Image Compression

Images

taken f

rom

Gonza

lez

& W

oods,

Dig

ital Im

age P

roce

ssin

g (

2002)


Segmentation

Image Acquisition

Image Restoration


Segmentation


Image Enhancement

Object Recognition

Problem Domain


Image Compression

Images

taken f

rom

Gonza

lez

& W

oods,

Dig

ital Im

age P

roce

ssin

g (

2002)


Object Recognition

Image Acquisition

Image Restoration


Segmentation


Image Enhancement

Object Recognition

Problem Domain


Image Compression

Images

taken f

rom

Gonza

lez

& W

oods,

Dig

ital Im

age P

roce

ssin

g (

2002)



Image Acquisition

Image Restoration


Segmentation


Image Enhancement

Object Recognition

Problem Domain


Image Compression

Images

taken f

rom

Gonza

lez

& W

oods,

Dig

ital Im

age P

roce

ssin

g (

2002)


Image Compression

Image Acquisition

Image Restoration


Segmentation


Image Enhancement

Object Recognition

Problem Domain


Image Compression



Image Acquisition

Image Restoration


Segmentation


Image Enhancement

Object Recognition

Problem Domain


Image Compression

Documents

Digital Imaging Processing (CS705) · Department of Computer Science and Engineering. National Institute of Technology Jamshedpur Instructor Dr. Koushlendra Kumar Singh ... Unit Project