This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
Abstract
Field of image processing has vast applications in medical, forensic, research etc., It includes various domains like enhancement,
classification, segmentation, etc., which are widely used for these applications. Image Enhancement is the pre processing step on
which the accuracy of the result lies. Image enhancement aims to improve the visual appearance of an image, without affecting
the original attributes (i.e.,) image contrast is adjusted and noise is removed to produce better quality image. Hence image
enhancement is one of the most important tasks in image processing. Enhancement is classified into two categories spatial domain
enhancement and frequency domain enhancement. Spatial domain enhancement acts upon pixel value whereas frequency domain
enhancement acts on the Fourier transform of the image. The enhancement techniques to be used depend on modality, climatic
and visual perspective etc., In this paper, we present a survey on various existing image enhancement techniques.
Keywords: Enhancement, Spatial domain enhancement, Frequency domain enhancement, Contrast, Modality.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
Abstract
Field of image processing has vast applications in medical, forensic, research etc., It includes various domains like enhancement,
classification, segmentation, etc., which are widely used for these applications. Image Enhancement is the pre processing step on
which the accuracy of the result lies. Image enhancement aims to improve the visual appearance of an image, without affecting
the original attributes (i.e.,) image contrast is adjusted and noise is removed to produce better quality image. Hence image
enhancement is one of the most important tasks in image processing. Enhancement is classified into two categories spatial domain
enhancement and frequency domain enhancement. Spatial domain enhancement acts upon pixel value whereas frequency domain
enhancement acts on the Fourier transform of the image. The enhancement techniques to be used depend on modality, climatic
and visual perspective etc., In this paper, we present a survey on various existing image enhancement techniques.
Keywords: Enhancement, Spatial domain enhancement, Frequency domain enhancement, Contrast, Modality.
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Introduction to Digital Image Processing Using MATLABRay Phan
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
Image Processing is any form of signal processing for which our input is an image, such as photographs or frames of videos and our output can be either an image or a set of characterstics related to the image
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram processing
Using histogram statistics for image enhancement
Uses for Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram Processing
Basics of Spatial Filtering
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
Introduction to Digital Image Processing Using MATLABRay Phan
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
Image Processing is any form of signal processing for which our input is an image, such as photographs or frames of videos and our output can be either an image or a set of characterstics related to the image
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram processing
Using histogram statistics for image enhancement
Uses for Histogram Processing
Histogram Equalization
Histogram Matching
Local Histogram Processing
Basics of Spatial Filtering
AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017Carol Smith
What is machine learning? Is UX relevant in the age of artificial intelligence (AI)? How can I take advantage of cognitive computing? Get answers to these questions and learn about the implications for your work in this session. Carol will help you understand at a basic level how these systems are built and what is required to get insights from them. Carol will present examples of how machine learning is already being used and explore the ethical challenges inherent in creating AI. You will walk away with an awareness of the weaknesses of AI and the knowledge of how these systems work.
Image De-Noising Using Deep Neural Networkaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
IMAGE DE-NOISING USING DEEP NEURAL NETWORKaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.
Image De-Noising Using Deep Neural Networkaciijournal
Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level
representations of input data which has been introduced to many practical and challenging learning
problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and
conduct training a large image database to get the experimental output. The result shows the robustness
and efficient our our algorithm.
AN EMERGING TREND OF FEATURE EXTRACTION METHOD IN VIDEO PROCESSINGcscpconf
Recently the progress in technology and flourishing applications open up new forecast and defy
for the image and video processing community. Compared to still images, video sequences
afford more information about how objects and scenarios change over time. Quality of video is
very significant before applying it to any kind of processing techniques. This paper deals with
two major problems in video processing they are noise reduction and object segmentation on
video frames. The segmentation of objects is performed using foreground segmentation based
and fuzzy c-means clustering segmentation is compared with the proposed method Improvised
fuzzy c – means segmentation based on color. This was applied in the video frame to segment
various objects in the current frame. The proposed technique is a powerful method for image
segmentation and it works for both single and multiple feature data with spatial information.
The experimental result was conducted using various noises and filtering methods to show which is best suited among others and the proposed segmentation approach generates good quality segmented frames.
Removal of Gaussian noise on the image edges using the Prewitt operator and t...IOSR Journals
Abstract: Image edge detection algorithm is applied on images to remove Gaussian noise that is present in the
image during capturing or transmission using a method which combines Prewitt operator and threshold
function technique to do edge detection on the image. This method is better than a method which combines
Prewitt operator and mean filtering. In this paper, firstly use mean filtering to remove initially Gaussian noise,
then use Prewitt operator to do edge detection on the image, and finally applied a threshold function technique
with Prewitt operator.
Keywords: Gaussian noise, Prewitt operator, edge detection, threshold function
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
2. Is this cool?
Meet Pranav Mistry
Father of Sixth Sense Technology
PhD, MIT Media Labs
M.Des, IIT Bombay
BE, Computer Science, Nirma Institute of Technology
3. Why Digital Image Processing?
Machine
Learning
Gesture
Control
Face
Recognition
Computer
Vision
Biomedical
Image Pro.
4. So, Let’s start: Lecture Overview
Lecture 1:
Monday, 28 Oct, 2013
Duration : 90 Mins
Lecture 2:
Tuesday, 29 Oct, 2013
Duration : 90 Mins
Topics:
Topics:
1. Basic Introduction and Matlab
5. Noise Filtering and Segmentation
2. Image Handling
6. Colour Image Analysis
3. Operations on Images
7. Gesture Recognition- Case Study
4. Sample Exercises
8. Textbook and beyond
Introduction To Digital Image Processing
10/25/2013
4
5. 1.Basic Introduction and Matlab
Digital Image: The digital image is essentially a fixed number of rows and columns of
pixels. Pixels are the smallest individual element in an image, holding
quantized values that represent the brightness of a given color at any
specific point.
-Monochrome/Grayscale Image has 1 value per pixel,
while Colour Image has 3 values per pixel. (R, G, B)
monochrome image= f( x, y)
-where x and y are spatial coordinates
-f gives amplitude of intensity
-Colour image= 3 R,G,B monochrome images
Introduction To Digital Image Processing
10/25/2013
5
6. Notation to represent a pixel :
where,
Pixel Location: p = (r , c)
Pixel Intensity Value: I(p) = I(r , c)
[ p, I(p)]
Note: Origin in case of Matlab Image Processing Toolbox is not p=(0,0) but p=(1,1)
7. The MATrix LABoratory
MATLAB is a dynamically typed language
-Means that you do not have to declare any variables
-All you need to do is initialize them and they are created
MATLAB treats all variables as matrices
-Scalar – 1 x 1 matrix. Vector – 1 x N or N x 1 matrix
-Why? Makes calculations a lot faster
These MATRICES can be manipulated in two ways:
Cleve Moler
Stanford University
1970
Introduction To Digital Image Processing
-Matrix Manipulation (usual matrix operations)
-Array Manipulation (using dot (.) operator as prefix)
-Vector Manipulation
10/25/2013
7
8. Basics of MATrix LABoratory
What is the best way to learn Matlab?
- Using the ‘Help’ file (sufficient for 90% operations)
- Practicing along with it.
Common Errors:
1. Select ‘Image Processing
Toolbox’ before starting to use
various image functions.
(only available with 2008b or newer)
2. Always make sure whether
current folder is same as desired.
3. Forgetting to use help command
Introduction To Digital Image Processing
10/25/2013
8
9. 2. Image Handling
Matlab Functions
-
function[output]=name(inputs)
Create and save new ‘.m’ file in the current directory
Some inbuilt functions (in Image Pro. Toolbox) to remember:
-
If a statement
doesn’t fit a
line, we use ‘…’
, to indicate it
continues
in
next line
help, clc, type
Imread(‘filename.ext’)
imwrite(g,‘filename.ext’,’compression’,’parameter’,’resolution’,[colores,rowres],‘quality’)
mat2gray(f,[fmin,fmax])
imshow(f(:,:,x))
figure
-for holding on to an image and displaying other (used as prefix with,)
whos
-for displaying size of all images currently being used in workspace
Question: How to find intensity of a given pixel?
Introduction To Digital Image Processing
9
10. Image Handling (Continued)
-Accessing subset of an image:
For monochromatic images: im2 = im(row1:row2,col1:col2);
For colour images:
im2 = im(row1:row2,col1:col2,:);
Methods to fill lacking data:
-Resizing an image:
1.’nearest’= as neighborhood
2.’bilinear’=linear interpolation
3.’bicubic’=cubic int. (*best)
out = imresize(im, scale, ‘method’); or
out = imresize(im, [r c], ‘method’);
-Rotating an image:
out = imrotate(im, angle_in_deg, ‘method’);
Introduction To Digital Image Processing
10/25/2013
10
11. 3. Operation on Images : Transformations
G( x, y) = T [f ( x, y)]
where, G= Processed Image
T= Operator
f=input image
-Brightness/Intensity Transformation: im2=c*im;
im2=im+c;
-Contrast Transformation:
If c > 1, c>0 increasing brightness
If c < 1, c<0 decreasing brightness
out = imadjust(im, [], [], gamma);
Contrast represents how the intensity changes from min to max value.
Introduction To Digital Image Processing
10/25/2013
11
12. Operations on Images: Spatial Filtering
a.k.a. neighborhood
processing
1. First we need to create an N x N matrix
called a mask, kernel, filter (or neighborhood).
2. The numbers inside the mask will help us
control the kind of operation we’re doing.
3. Different numbers allow us to
blur, sharpen, find edges, etc.
4. We need to master convolution first, and the
rest is easy!
G=
[
abc
def
ghi
]
Introduction To Digital Image Processing
H=
[
z yx
wvu
tsr
]
Mask
out = a*z + b*y + c*x + d*w + e*v + f*u + g*t + h*s + i*r,
10/25/2013
12
14. Application: Blurring
Blurring:
-
Reduces noise (high frequency)
Reduces edges (high frequency)
Is essentially a low pass filter (eliminates high f)
Can be done through averaging filter
For colour images, we can blur each
layer independently
mask = fspecial(‘average’, N);
out = imfilter(im, mask,’conv’);
More the
Mask size,
More blur in
Result
Introduction To Digital Image Processing
10/25/2013
14
15. Application: Edge Detection
What is an edge? – ‘A place of change’.
f’( x, y)= f( x-1, y) – f( x+1,y)
This is a Horizontal filter.
(puts more weight on central pixel)
EXERCISE: Use following masks in fspecial function
and find out what they do- Gaussian, Laplacian,
Laplacian of Gaussian (LoG)
Alternate way: Canny Edge Detector
(most powerful edge detector)
[ g, t]= edge (f, ‘canny’, T , sigma)
Introduction To Digital Image Processing
How do we do this in MATLAB?
1) Create our Prewitt or Sobel Horizontal Masks:
mask = fspecial(‘prewitt’); or mask = fspecial(‘sobel’);
2) Convolve with each mask separately
dX = imfilter(im, mask); dY = imfilter(im, mask2);
3)Find the overall magnitude
mag = sqrt(double(dX).^(2) + double(dY).^(2));
10/25/2013
15
16. 4. Noise Filtering
▪ In reality, pixel corruption takes place during
process of acquisition or transmission. There
is a need to remove(okay, ‘try to’) this noise.
▪ As an exercise, let’s add up artificial
noise in an image using function:
Gaussian
n = imnoise ( im, ‘salt & pepper’, density);
Use blurring filter against ‘Gaussian’ noise.
Use median filter against ‘salt & pepper noise’.
out = medfilt2( n , [M N]);
Salt & Pepper
Introduction To Digital Image Processing
Poisson
10/25/2013
16
17. Segmentation
division of an image into segments or parts (region of interests)
▪ This division is done mainly on the basis of :
(a) grey level
(b) texture
(d) depth
(c) motion
(e) colour
Can you think of ways in which this will prove useful?
Introduction To Digital Image Processing
10/25/2013
17
18. Segmentation Techniques
One way is already covered. Can you name that?
Thresholding : Simplest Segmentation Technique
Pixels are grouped into “Object” and “Background”
– Input: Gray scale image
– Output: Binary image
Implementing in Matlab:
output = im2bw(Image, k)
where, K=T/largest pixel size
Introduction To Digital Image Processing
Other Methods:
1. Region Growing: A method that
clubs similar property pixels. (Satellites)
2. Watershed Transform: grayscale
intensity is interpreted as distance.
(topographical use)
10/25/2013
18
19. Colour Image Analysis
Trichromacy theory :All colors found
in nature can naturally be decomposed
into Red, Green and Blue.
RGB Cube
Other models: CMY, NTSC, YCbCr,
HSI, CMYK, HSV
Introduction To Digital Image Processing
10/25/2013
19
20. Colour Image Analysis
▪ Basic Conversions:
Loss of information due
to size of palette
What is an indexed image?
An image having two components:
1. Data Matrix
2. Colour Map (a.k.a. ‘palette’)
What is its use?
1.To make display process fast
2.To reduce size of image
Introduction To Digital Image Processing
10/25/2013
20