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Image File Formats
By
Dr. Rajeev Srivastava
1
Image File Formats
A typical
image file
format
contains
two fields
namely
Header and
Image data.
2
Dr. Rajeev Srivastava
Header field
They contains various information
related to image such as:
Format or vers
Digital Image Processing
and Machine Vision
Fundamentals
By
Dr. Rajeev Srivastava
Associate Professor
Dept. of Computer Sc. & Engineering, IIT(BHU),
Varanasi
Overview
In early days of computing, data was numerical.
Later, textual data became more common
EDGE DETECTION &
BOUNDARY EXTRACTION
BY: DR. RAJEEV SRIVASTAV
EDGE DETECTION
Edge detection is the most common approach for
detection meaningful discontinuity in grey level.
In this section we study first order & second order
derivatives of edge detection
Fundamentals of Digital Image
Processing & Machine Vision
By:
Dr. Rajeev Srivastava
Associate Professor, CSE, IIT(BHU), Varanasi
Fundamentals of Digital Image
Processing
Applications of image processing
What's an image?
A simple image model
Fundamenta
Image File Formats
By
Dr. Rajeev Srivastava
1
Image File Formats
A typical
image file
format
contains
two fields
namely
Header and
Image data.
2
Dr. Rajeev Srivastava
Header field
They contains various information
related to image such as:
Format or vers
Colour Space Models
By
Dr. Rajeev Srivastava
Characteristics of light
Visible light consists of a spectral distribution of
electromagnetic energy having wavelengths in
the range 400-700 nm.
The perceived colour of visible light is to some
extent a subje
Image Transformation Techniques
Dr. Rajeev Srivastava
Dept. of Computer Engineering, ITBHU, Varanasi
1. Introduction
The choice of a particular transform in a given application depends on the amount of
reconstruction error that can be tolerated and the co
Image Restoration: Noise Models
By
Dr. Rajeev Srivastava
Principle Sources of Noise
Image Acquisition
Image sensors may be affected by Environmental
conditions (light levels etc)
Quality of Sensing Elements (can be affected by
e.g. temperature)
Image
By
Dr. Rajeev Srivastava
CSE, IIT(BHU)
Dr. Rajeev Srivastava
1
Its Understanding
Wavelet means small wave.
So wavelet analysis is about analyzing signal with short
duration finite energy functions. They transform the signal
under investigation in to anoth
Image Enhancement in Spatial
Domain
By
Dr. Rajeev Srivastava
CONTENTS
Image Enhancement in Spatial Domain
Spatial Domain Methods
1. Point Processing Functions
A. Gray Level Transformation functions for
i. Contrast Enhancement
ii. Thresholding
B. Some ba
Performance Measurement of Image
Processing Algorithms
By
Dr. Rajeev Srivastava
ITBHU, Varanasi
Performance Measurement:
Image Reconstruction
Image Reconstruction
In various image applications, where an image is to be reconstructed, from its
degraded ver
Image Restoration:
Deterministic Approaches
By
Dr. Rajeev Srivastava
periodic in frequency
periodic in time
Periodic Extension of Signals: Wrong!
Periodic Extension of Signals: Correct!
Extension is
done by zeropadding
points
The eigenvalues of a circula
Image Enhancement:
Histogram Processing
By
Rajeev Srivastava
Histogram Equalisation in Discrete Form
Two Different Images-The Same Histogram
Histogram Equalisation
Histogram Equalisation
Histogram Equalisation
Histogram Equalisation
Histogram Equalisation
Objective Questions on Image enhancement and restoration
1.
2.
3.
4.
5.
6.
7.
8.
9.
A Grid of square which contains a single color is called
a. Image
b. Pixel value
c. Pixel
d. Color
A color Image have
a. 2 value per pixel
b. 3 value per pixel
c. 4 value
Image Restoration:
Fundamentals
By
Dr. Rajeev Srivastava
Image Restoration
In many applications the imaging system introduces a
slight distortion. These applications may include:
Satellite imaging, medical imaging, astronomical
imaging, poor-quality fami
Image Restoration:
Spatial Filtering
By
Dr. Rajeev Srivastava
1
Restoration of Noise Only- Spatial Filtering
2
Arithmetic Mean Filter
3
Geometric and Harmonic Mean Filter
4
Contra-Harmonic Mean Filter
5
Classification of Contra-Harmonic Filter Application
Image Restoration:
Iterative Methods
By
Dr. Rajeev Srivastava
ITERATIVE METHODS
new estimate=old estimate+function(old estimate)
There is no need to explicitly implement the inverse of an operator. The
restoration process is monitored as it progresses. T
Histogram Based Features of an Image
By
Dr. Rajeev Srivastava
IIT(BHU), Varanasi-221005
Histogram Features
Histogram Features are based on the
histogram of a region of the image.
suppose u be a random variable which
represents a gray level in a given re
Objective Questions: Image Transforms
1. The transform which possess the multi-resolution property in an image is:
a. Fast Fourier Transform (FFT)
b. Discrete Cosine Transform (DCT)
c. Short Term Fourier Transform (STFT)
d. Wavelet Transform
2. Parseval's
IMAGE TRANSFORMS
Questions: Set3
By: Dr. R. Srivastava
1. What is the need for image transforms?
2. What are the advantages of transforms?
3. What is meant by unitary transforms?
4. Check whether the following matrix is orthogonal or not?
cos
sin
sin
Objective Questions: Introduction
1. In image processing technique the input and output are:
a. Low quality image and improved quality image
b. Description and image
c. Image and description
d. Low quality image and image/description
2. In Computer Vision
1.Moment-Based Feature
Many shape feature can be represented in terms of moments.
Suppose we have any shape and that particular shape is
represented by a region that containing N pixels, we have
the following:
1. Center of mass
2. Orientation
3. Boundin
INTRODUCTION: DIGITAL IMAGE PROCESSING
Questions: Set-1
By: Dr. R. Srivastava
1. What is an image?
2. What is the goal of Digital image processing?
3. What is meant by resolution?
4. What are the ways in which images can be classified?
5. What are the cla
DIGITAL IMAGE REPRESENTATION
Questions: Set-2
By: Dr. R. Srivastava
1. List the typical values for illumination and reflectance components.
2. What is sampling and quantization?
3. When and where will you use non uniform sampling and quantization?
4. Comp
Geometry feature
Our ultimate aim is to measure some geometric attributes of
the object, in any image analysis problems.
Such as:
1. Perimeter
2. Area
3. Radii
4. Numbers of holes
5. Euler number
6. Corners
7. Bending energy
8. Roundness,or compactness
Objective Questions: Performance Metrics
1. For optimal performance, measured values of MSE and RMSE should be:
a. Negative
b. Small
c. Large
d. 1
2. For optimal performance, measured values of PSNR should be:
a. 0
b. 1
c. High
d. Low
3. Which of the foll
Analysis of Algorithms | Set 1 (Asymptotic Analysis)
Why performance analysis?
There are many important things that should be taken care of, like user friendliness, modularity, security, maintainability, etc. Why to worry about
performance?
The answer to
Given an array A[] and a number x, check for pair in A[] with sum as x
Write a C program that, given an array A[] of n numbers and another number x, determines whether or not there exist two elements in S whose
sum is exactly x.
METHOD 1 (Use Sorting)
Alg