its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
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
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
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
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
Study on Contrast Enhancement with the help of Associate Regions Histogram Eq...IJSRD
Histogram equalization is an uncomplicated and extensively used image distinction enhancement technique. The crucial drawback of histogram equalization is it transforms the brightness of the image. To overcome this drawback, different histogram Equalization methods have been projected. These methods protect the brightness on the result image but, do not have a usual look. Therefore this paper is an attempt to bridge the gap and results after the processed Associate regions are collected into one image. The mock-up result explains that the algorithm can not only improve image information successfully but also remain the imaginative image luminance well enough to make it likely to be used in video arrangement directly.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
Design Approach of Colour Image Denoising Using Adaptive WaveletIJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
digital image processing is Basic concepts, Examples of fields that use Digital Image
Processing, Fundamental steps in Digital Image Processing, Components of
an Image Processing System.
Digital Image Fundamentals: Elements of visual perception, Image sensing
and acquisition, Image sampling and quantization, Some basic relationships
between pixels.
9
2 Image Enhancement in Spatial domain: Some Basic Intensity
Transformation functions, Histogram Processing, Fundamentals of Spatial
Filtering, Smoothing and Sharpening Spatial Filtering.
Self Study:
Image Enhancement In Frequency Domain: Introduction to Fourier
Transform, Smoothing and Sharpening frequency domain filtersBasic concepts, Examples of fields that use Digital Image
Processing, Fundamental steps in Digital Image Processing, Components of
an Image Processing System.
Digital Image Fundamentals: Elements of visual perception, Image sensing
and acquisition, Image sampling and quantization, Some basic relationships
between pixels.
9
2 Image Enhancement in Spatial domain: Some Basic Intensity
Transformation functions, Histogram Processing, Fundamentals of Spatial
Filtering, Smoothing and Sharpening Spatial Filtering.
Self Study:
Image Enhancement In Frequency Domain: Introduction to Fourier
Transform, Smoothing and Sharpening frequency domain filtersBasic concepts, Examples of fields that use Digital Image
Processing, Fundamental steps in Digital Image Processing, Components of
an Image Processing System.
Digital Image Fundamentals: Elements of visual perception, Image sensing
and acquisition, Image sampling and quantization, Some basic relationships
between pixels.
9
2 Image Enhancement in Spatial domain: Some Basic Intensity
Transformation functions, Histogram Processing, Fundamentals of Spatial
Filtering, Smoothing and Sharpening Spatial Filtering.
Self Study:
Image Enhancement In Frequency Domain: Introduction to Fourier
Transform, Smoothing and Sharpening frequency domain filtersBasic concepts, Examples of fields that use Digital Image
Processing, Fundamental steps in Digital Image Processing, Components of
an Image Processing System.
Digital Image Fundamentals: Elements of visual perception, Image sensing
and acquisition, Image sampling and quantization, Some basic relationships
between pixels.
9
2 Image Enhancement in Spatial domain: Some Basic Intensity
Transformation functions, Histogram Processing, Fundamentals of Spatial
Filtering, Smoothing and Sharpening Spatial Filtering.
Self Study:
Image Enhancement In Frequency Domain: Introduction to Fourier
Transform, Smoothing and Sharpening frequency domain filtersBasic concepts, Examples of fields that use Digital Image
Processing, Fundamental steps in Digital Image Processing, Components of
an Image Processing System.
Digital Image Fundamentals: Elements of visual perception, Image sensing
and acquisition, Image sampling and quantization, Some basic relationships
between pixels.
9
2 Basic
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
2. INTRODUCTION
Homomorphic filtering is a generalized technique for signal and image processing, involving a
nonlinear mapping to a different domain in which linear filter techniques are applied, followed by
mapping back to the original domain.
Homomorphic filter is sometimes used for image enhancement. It simultaneously normalizes the
brightness across an image and increases contrast.
Homomorphic filtering is one such technique for removing multiplicative noise that has certain
characteristics.
Homomorphic filtering is most commonly used for correcting non-uniform illumination in images.
Homomorphic filtering can be used for improving the appearance of a grayscale image by
simultaneous intensity range compression (illumination) and contrast enhancement (reflection).
3. OPERATION
The illumination-reflectance model of image formation says that the intensity at any pixel, which is the
amount of light reflected by a point on the object, is the product of the illumination of the scene and the
reflectance of the object(s) in the scene, i.e.,
I(x,y)=L(x,y) * R(x,y)
where I is the image, L is scene illumination, and R is scene reflectance.
Illumination typically varies slowly across the image as compared to reflectance which can change quite
abruptly at object edges. This difference is the key to separating out the illumination component from the
reflectance component. In homomorphic filtering we first transform the multiplicative components to
additive components by moving to the log domain.
ln(I(x,y))=ln(L(x,y) R(x,y))
ln(I(x,y))=ln(L(x,y))+ln(R(x,y))
4. In DFT H(u,v) (DFT) -1 expI 𝑥, 𝑦 g 𝑥, 𝑦
OPERATION
Then we use a high-pass filter in the log domain to remove the low-frequency illumination component
while preserving the high-frequency reflectance component. The basic steps in homomorphic filtering
are shown in the diagram below:
The illumination component of an image generally is characterized by slow spatial variations,
while the reflectance component tends to vary abruptly, particularly at junction of dissimilar
objects.
6. HISTOGRAM EQUALIZATION
• Histogram equalization is a method in image processing of contrast adjustment using
the image's histogram.
• The method is useful in images with backgrounds and foregrounds that are both bright or both
dark.
• A key advantage of the method is that it is a fairly straight forward technique and
an invertible operator. So in theory, if the histogram equalization function is known, then the
original histogram can be recovered.
• A disadvantage of the method is that it is indiscriminate. It may increase the contrast of
background noise, while decreasing the usable signal.
• Histogram equalization often produces unrealistic effects in photographs; however it is very
useful for scientific images like thermal, satellite or x-ray images.