This document discusses several topics in image enhancement and processing including:
1. Spatial filtering which involves applying a weighted mask over an image to replace pixel values.
2. Logarithmic transformation which expands darker pixel values more than brighter ones for enhancement.
3. Thresholding, logarithmic transformation, negative transformation, contrast stretching, and grey level slicing as common nonlinear image transformations.
4. Weighted average filtering which applies different coefficients to pixels to give more importance to some over others.
5. High boost filters and unsharp masking as types of high pass sharpening filters used to highlight fine image details.
Marker Controlled Segmentation Technique for Medical applicationRushin Shah
Medical image segmentation is a very important field for the medical science. In medical images, edge detection is an important work for object recognition of the human organs such as brain, heart or kidney etc. and it is an essential pre-processing step in medical image segmentation.
Medical images such as CT, MRI or X-Ray visualizes the various information’s of internal organs which is very important for doctors diagnoses as well as medical teaching, learning and research.
It is a tough job to locate the internal organs if images contains noise or rough structure of human body organs.
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
Marker Controlled Segmentation Technique for Medical applicationRushin Shah
Medical image segmentation is a very important field for the medical science. In medical images, edge detection is an important work for object recognition of the human organs such as brain, heart or kidney etc. and it is an essential pre-processing step in medical image segmentation.
Medical images such as CT, MRI or X-Ray visualizes the various information’s of internal organs which is very important for doctors diagnoses as well as medical teaching, learning and research.
It is a tough job to locate the internal organs if images contains noise or rough structure of human body organs.
Image segmentation is an important image processing step, and it is used everywhere if we want to analyze what is inside the image. Image segmentation, basically provide the meaningful objects of the image.
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
Presentation on deformable model for medical image segmentationSubhash Basistha
Introduction to Image Processing
Steps of Image Processing
Types of Image Processing
Introduction to Image Segmentation
Introduction to Medical Image Segmentation
Application of Image Segmentation
Example of Image Segmentation
Need for Deformable Model
What is Deformable Model??
Types of Deformable Model
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
An evaluation of two popular segmentation algorithms, the mean shift-based segmentation algorithm and a graph-based segmentation scheme. We also consider a hybrid method which combines the other two methods.
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
Presentation on deformable model for medical image segmentationSubhash Basistha
Introduction to Image Processing
Steps of Image Processing
Types of Image Processing
Introduction to Image Segmentation
Introduction to Medical Image Segmentation
Application of Image Segmentation
Example of Image Segmentation
Need for Deformable Model
What is Deformable Model??
Types of Deformable Model
Visual Quality for both Images and Display of Systems by Visual Enhancement u...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Comparative analysis of filters and wavelet based thresholding methods for im...csandit
Image Denoising is an important part of diverse image processing and computer vision
problems. The important property of a good image denoising model is that it should completely
remove noise as far as possible as well as preserve edges. One of the most powerful and
perspective approaches in this area is image denoising using discrete wavelet transform (DWT).
In this paper comparative analysis of filters and various wavelet based methods has been
carried out. The simulation results show that wavelet based Bayes shrinkage method
outperforms other methods in terms of peak signal to noise ratio (PSNR) and mean square
error(MSE) and also the comparison of various wavelet families have been discussed in this
paper.
Frequency Domain Blockiness and Blurriness Meter for Image Quality AssessmentCSCJournals
Image and video compression introduces distortions (artefacts) to the coded image. The most prominent artefacts added are blockiness and blurriness. Many existing quality meters are normally distortion-specific. This paper proposes an objective quality meter for quantifying the combined blockiness and blurriness distortions in frequency domain. The model first applies edge detection and cancellation, then spatial masking to mimic the characteristics of the human visual system. Blockiness is then estimated by transforming image into frequency domain, followed by finding the ratio of harmonics to other AC components. Blurriness is determined by comparing the high frequency coefficients of the reference and coded images due to the fact that blurriness reduces the high frequency coefficients. Then, both blockiness and blurriness distortions are combined for a single quality metric. The meter is tested on blocky and blurred images from the LIVE image database, with a correlation coefficient of 95-96%.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
2. Unit 2
2
Q.5 WRITE A NOTE ON IMAGE ENHANCEMENT USING SPATIAL FILTERS.
Spatial Filtering involves passing a weighted mask or kernel over the image and replacing the original
pixel values in the region corresponding to the kernel multiplied by kernel weights. (spatial filtering
and neighbourhood processing is same)
6. Unit 2
6
III. Logarithmic Transformation
Logarithmic transformation further contains two type of transformation.
Log transformation and inverse log transformation. o or o The log transformations can be defined
by this formula o s = c log(r + 1).
Where s and r are the pixel values of the output and the input image and c is a constant.
The value 1 is added to each of the pixel value of the input image questions marked in red: QB
questions because if there is a pixel intensity of 0 in the image, then log (0) is equal to infinity.
So 1 is added, to make the minimum value at least 1. o During log transformation, the dark pixels in
an image are expanded as compare to the higher pixel values.
The higher pixel values are kind of compressed in log transformation
7. Unit 2
7
1. EXPLAIN THE TERM
(A) THRESHOLDING (B) LOG TRANSFORMATION (C) NEGATIVE TRANSFORMATION (D) CONTRAST
STRETCHING (E) GREY LEVEL SLICING.
(A) Thresholding – covered above in non-linear transformations
(B) Log Transformation - covered above in non-linear transformations
(C) Negative Transformation – covered above in linear transformations
(D) Contrast Stretching Contrast stretching (often called normalization) is a simple image
enhancement technique that attempts to improve the contrast in an image by `stretching' the range
of intensity values it contains to span a desired range of values, e.g. the the full range of pixel values
that the image type concerned allows. It differs from the more sophisticated histogram equalization
in that it can only apply a linear scaling function to the image pixel values. As a result the
`enhancement' is less harsh. (Most implementations accept a graylevel image as input and produce
another graylevel image as output.)
(E) Grey Level Slicing - covered above in non-linear transformations
Q.22
Q.2) EXPLAIN THE TERMS: (A)SMOOTHING (B) SHARPENING
(A) Smoothing/Low pass filtering: Low pass filtering as the name suggests removes the high
frequency content from the image. It is used to remove noise present in the image. Noise, is
8. Unit 2
8
normally a high frequency signal and low pass filtering eliminates the noise. Smoothing is used to
remove noise from image
(B) Sharpening/High pass filtering: o Sharpening is used for highlighting fine details in an image.
• Low Pass Filters: 1. Mean Filter/Averaging Filter/Low Pass Filter
Weighted Average Filter
Q.7
WRITE A NOTE ON WEIGHTED AVERAGE FILTERS. GIVE EXAMPLE.
This mask yields a so-called weighted average, terminology used to indicate that pixels are multiplied
by different coefficients, thus giving more importance (weight) to some pixels at the expense of
others. In the mask the pixel at the center of the mask is multiplied by a higher value than any other,
thus giving this pixel more importance in the calculation of the average.
Weighted Filter mask is as follows:
9. Unit 2
9
17.EXPLAIN BIT PLANE SLICING WITH SUITABLE EXAMPLE.
19.JUSTIFY: ”BUTTERWORTH LOW PASS FILTER IS PREFERRED TO IDEAL LOW PASS FILTER
The ringing effects due to the sharp cut-offs in the ideal filter and to get rid of ringing effects,
elimination of sharp cut-offs is necessary. This exactly happens in butterworth low pass filters. The
transfer function of the butterworth low pass filter of order n and the cut off frequency at a distance
D0 from the origin is defined as
10. Unit 2
10
8. WHAT ARE HIGH BOOST FILTERS? HOW ARE THEY USED? EXPLAIN. or 15.WHAT ARE
SHARPENING FILTERS? GIVE EXAMPLES. EXPLAIN ANY ONE IN DETAIL
Types of High Pass Filters:
1. High-boost Filtering
11. Unit 2
11
2. Unsharp Masking
16.EXPLAIN VARIOUS TECHNIQUES OF IMAGE ARITHMETIC.
32. LIST AND EXPLAIN FIVE ARITHMETIC OPERATIONS ALONG WITH THEIR MATHEMATICAL
REPRESENTATION.
list:
1. Image Addition
2. Image Subtraction
3. Image Multiplication
4. Image Division
5. Alpha Blending
14. Unit 2
14
4. WHAT IS STRUCTURING ELEMENT? WHAT IS THE USE OF IT IN MORPHOLOGICAL
OPERATION?
1. Morphological techniques probe the image with a small shape or template called a
structuring element.
2. Structuring element is positioned at all possible locations in the image and its compared with
the corresponding neighbourhood of pixels.
3. A morphological operation on a binary image creates a new binary image in which the pixel
has a non-zero value only if the test is successful at that location in an input image.
4. The structuring element is a small binary image i.e a small matrix of pixels, each with a value
of zero or one.
5. The matrix dimensions specify the size of the structuring element
6. The patterns of ones and zeroes specifies the shape of the structuring element.
7. An origin of the structuring element is usually one of its pixels.
11.EXPLAIN THE MORPHOLOGICAL IMAGE OPERATIONS ON AN IMAGE. STATE ITS
AAPLICATIONS
15. Unit 2
15
3. EXPLAIN DILATION AND EROSION AND EXPLAIN HOW OPENING AND CLOSING ARE
RELATED WITH THEM.
The basic morphological operations are dilation and erosion. They are expressed by a kernel
operating on an input binary image, X, where white pixels denote uniform regions and black
pixels denote region boundaries.
Erosion and dilation work conceptually by translating a structuring element, B, over the image
points and examining the difference between the translated kernel coordinates and image
coordinates.
Dilation and Erosion Based operations:
9. Applications of dilation and erosion:
Morphological operations are useful in many applications. To list a few they are used in hole
filling, boundary extraction of objects, extraction of connected components, Thinning and
thickening and so on. Among these applications the boundary extraction is shown below. For
comparison it is done with Sobel edge extraction
10.EXPLAIN EUCLIDEAN DISTANCE, CITY BLOCK DISTANCE, CHESS BOARD DISTANCE.
17. Unit 2
17
18.DISCUSS VARIOUS COLOUR MODELS USED IN IMAGE PROCESSING.
37. LIST ANY FIVE COLOR MODELS AND EXPLAIN ANY TWO IN DETAILS.
21.EXPLAIN RGB COLOUR MODEL TO REPRESENT A DIGITAL IMAGE.