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.
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.
To highlight the contribution made to the total image appearance by specific bits.i.e. Assuming that each pixel is represented by 8 bits, the image is composed of 8 1-bit planes.Useful for analyzing the relative importance played by each bit of the image.
Computer Graphics and its applications, Elements of a Graphics, Graphics Systems: Video Display Devices, Raster Scan Systems, Random Scan Systems, Input devices.
: Introduction of Rendering, Raytracing, Antialiasing, Fractals
This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
Feature Matching using SIFT algorithm; co-authored presentation on Photogrammetry studio by Sajid Pareeth, Gabriel Vincent Sanya, Sonam Tashi and Michael Mutale
To highlight the contribution made to the total image appearance by specific bits.i.e. Assuming that each pixel is represented by 8 bits, the image is composed of 8 1-bit planes.Useful for analyzing the relative importance played by each bit of the image.
Computer Graphics and its applications, Elements of a Graphics, Graphics Systems: Video Display Devices, Raster Scan Systems, Random Scan Systems, Input devices.
: Introduction of Rendering, Raytracing, Antialiasing, Fractals
This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
Feature Matching using SIFT algorithm; co-authored presentation on Photogrammetry studio by Sajid Pareeth, Gabriel Vincent Sanya, Sonam Tashi and Michael Mutale
Video surveillance is active research topic in
computer vision research area for humans & vehicles, so it is
used over a great extent. Multiple images generated using a fixed
camera contains various objects, which are taken under different
variations, illumination changes after that the object’s identity
and orientation are provided to the user. This scheme is used to
represent individual images as well as various objects classes in a
single, scale and rotation invariant model.The objective is to
improve object recognition accuracy for surveillance purposes &
to detect multiple objects with sufficient level of scale
invariance.Multiple objects detection& recognition is important
in the analysis of video data and higher level security system. This
method can efficiently detect the objects from query images as
well as videos by extracting frames one by one. When given a
query image at runtime, by generating the set of query features
and it will find best match it to other sets within the database.
Using SURF algorithm find the database object with the best
feature matching, then object is present in the query image.
AN ALTERNATIVE APPROACH FOR SELECTION OF PSEUDO RANDOM NUMBERS FOR ONLINE EXA...cscpconf
Fast and accurate selection of random pattern is needed for many scientific and commercial applications. One of the major applications is Online Examination system. In this paper, a sophisticated approach has been developed for the selection of uniform pseudo random pattern for Online Examination System. Three random integer generators have been compared for this
purpose. Most commonly used procedural language based pseudo random number; PHP random generator and atmospheric noise based true random number generator have been considered for easy generation of random patterns. The test result shows a varying degree of improvement in the quality of randomness of the generated patterns. The randomness quality of the generated pseudo random pattern has been assured by diehard test suite. An experimental
outcome for our recommended approach signifies that our approach selects a quality set of random pattern for Online Examination System
Computer Vision: Feature matching with RANSAC Algorithmallyn joy calcaben
Computer Vision: Feature matching with RANdom SAmple Consensus Algorithm
CMSC197.1 Introduction to Computer Vision
April 2018
by: Allyn Joy Calcaben, Jemwel Autor, & Jefferson Butch Obero
University of the Philippines Visayas
Learning Graphs Representations Using Recurrent Graph Convolution Networks Fo...Yam Peleg
Recently, number of papers re-visited this problem of generalizing neural networks to work on arbitrarily structured graphs, some achieving promising results in domains that have previously been dominated by other shallower algorithms. While Graph convolutions are generalization of spacial convolutions, and easiest to define in spectral domain, General Fourier transform used to represent them scales poorly with size of data. Therefore, first order approximation in Fourier-domain used to obtain efficient linear-time graph-CNNs. Those scales poorly with size of data. due to that, the expressiveness power of the proposed graph convolutional networks is severely impoverished. Another approach for learning graph representations requires the repeated application of contraction maps as propagation functions until node representations reach a stable fixed point. We combine those approaches and propose a recurrent version of Relational Graph Convolution networks, we then proceed to construct two models, Recurrent Variational Graph AutoEncoder and Recurrent Graph Convolution Regressor and show that for Ethereum Blockchain transaction graph we outperform the traditional Graph Convolution Network at predicting future movments of the corresponding tradable asset: Ether.
HRNET : Deep High-Resolution Representation Learning for Human Pose Estimationtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임 입니다! 오늘 소개 드릴 논문은 Deep High-Resolution Representation Learning for Human Pose Estimation 라는 제목의 논문입니다.
오늘 소개드릴 논문은 Pose Estimation에 관련된 논문 입니다. 기존 Pose Estimation 모델의 경우 직렬적인 네트워크 구조를 지녔지만, 직렬적인 구조는 압축하는 과정에서
지엽적인 정보들의 손실을 가져오게 되고 모든 프로세스가 upsampling에 과도하게 의존하고 있다는 한계점을 가지고 있습니다.그래서 이러한 한계점을 극복하고자HRNet은 이러한 직렬 구조에서 벗어나 병렬 구조로 subnetwork를 구성했습니다.
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.
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
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.
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.
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.
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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
When stars align: studies in data quality, knowledge graphs, and machine lear...
Preemptive RANSAC by David Nister.
1. Preemptive RANSAC for Live
Structure and Motion Estimation
by David Nister,
Machine vision and applications(J) in 2005, 261citations.
2nd/June/2011
presented Inkyu.Sa
3. What is RANSAC ?
Random sample consensus.
Observation Estimate model
Source from wikipedia
Outliers
LO-RANSAC
Preemptive RANSAC Inliers
DEGENSAC
PROSAC
MLESAC, NAPSAC etc.
4. What is RANSAC ?
Random sample consensus.
Observation Estimate model
Source from wikipedia
Outliers
Inliers
Source from AFMR, Machine Learning
Reinforcement learning
Use rewards and interaction with the environment to learn
5. Why do we use RANSAC ?
It is possible estimate robust parameters of model.
There are many noise in out real environments.
What are disadvantages of RANSAC?
It takes long time to calculate parameters without
boundary condition.
Therefore, threshold and max iteration are required.
7. How does RANSAC work?
We have this observation data set. Randomly choose any two points
and create a hypothesis.
8. How does RANSAC work?
Randomly choose any two points
and create a hypothesis.
9. How does RANSAC work?
Randomly choose any two points Calculate distances between a
and create a hypothesis. hypothesis and all observations.
This hypothesis has a huge distance value.
This process is called “Scoring”.
“
“ Because wewhich ahas theknowledgeintegration
hypothesis
have prior
minimum
that the
value of distance could be our model.
10. How does RANSAC work?
Calculate distances between a Again randomly choose two points and
hypothesis and all observations. create another hypothesis.
This one has a smaller distance value than
previous.
11. How does RANSAC work?
Again randomly choose two points and How many hypothesis can be created?
create another hypothesis.
This one has a smaller distance value than
previous. Number of lines = 100 C2 = 4,950
Number of lines =999 C2 = 498,501
Total calculation=498,501× 997 = 497,005,497
12. How does RANSAC work?
Set a threshold to prevent calculation
from all observation.
ld
ho
If a hypothesis meet our condition, this
s
re
hypothesis is going to be our best model.
Th
In addition set a maxim iterations or maxim time period.
13. How does RANSAC work?
What happens if we have this observation?
14. How does RANSAC work?
What happens if we have this observation?
Maybe we can’t get a right answer.
This implies that we need a decent observation data set to get a right
model unless RANSAC process takes more time and less accurate.
15. How does RANSAC work?
What happens if we have these observations?
Inlier noise Near degeneracies
Near degeneracies can be dealt with by sampling non-randomly.
16. How can we apply RANSAC
to our applications?
RANSAC can be used refine essential matrix.
Example) A 3D point registration.
pi R,T
Assume that we already create a hypothesis pi
using any methods such as 5 point, 8points
and svd algorithm and so on.
i=N
e2 = pi − Rpi − T 2 These two are our model.
i=1
pi = Current frame a 3D point in world coordinate.
pi = Previous frame a 3D world point in world coordinate.
17. Preemptive RANSAC
Based on breadth-first preemption.
Using time budget and threshold.
Source from ICCV2005 tutorial.
18. Preemptive RANSAC
Based on breadth-first preemption.
Source from ICCV2005 tutorial.
22. Preemptive RANSAC
log-likelihood, L
Cauchy distribution to model the errors = −ln(1 + u)
2
where ui = pi − Rpi − T
Squared magnitude of a reprojection error
10
robust log-likelihood = −ln (1 + ui )
i=1
N where
ρ = scoring function,
L(h) = ρ(o, h) o = observation index,
h = hypothesis index.
o=1
ρ function return a scalar value representing the log-likelihood given o,h
23. Preemptive RANSAC
Algorithm
1. Randomly permute the observations.
2. Generate all the hypotheses indexed by h= 1,..., f(1) using 5 points algorithm.
f (i) = preemption function indicates how many hypotheses
are to be kept at ith stage.
24. Preemptive RANSAC
Algorithm
1. Randomly permute the observations.
2. Generate all the hypotheses indexed by h= 1,..., f(1) using 5 points algorithm.
3. Compute the scores. L1 (h) = ρ(1, h) for h=1,...,f(1). set i=2
25. Preemptive RANSAC
Algorithm
1. Randomly permute the observations.
2. Generate all the hypotheses indexed by h= 1,..., f(1) using 5 points algorithm.
3. Compute the scores. L1 (h) = ρ(1, h) for h=1,...,f(1). set i=2
4. Reorder the hypotheses. The range h=1,...,f(i) contains the best f(i)
5. If i>N of f(i)=1, quit with the best hypothesis. Otherwise, compute
Li (h) = ρ(i, h) + Li−1 (h) for h=1,...,f(1). increase i and go to Step 4.
26. Preemptive RANSAC
Sequences Generate all the hypotheses
Scoring and find the best
hypotheses at each stage.
27. Preemptive RANSAC
Advantages of breath-first RANSAC
The breath-first RANSAC can spend a lot of time on bad
observation.
Only compare the previous hypothesis and it takes long time to get the
best one. (Depth-first RANSAC)
Disadvantages of breath-first RANSAC
can spend a lot of time on bad observation.
Author argues that depth-first RANSAC can also waste time on
bad hypothesis.
28. Preemptive RANSAC
Disadvantages of breath-first RANSAC
The breath-first RANSAC can spend a lot of time on bad
observation.
The depth-first RANSAC can also waste time on bad hypothesis.
29. Preemptive RANSAC
Disadvantages of breath-first RANSAC
The breath-first RANSAC can spend a lot of time on bad
observation.
The depth-first RANSAC can also waste time on bad hypothesis.
Typically a hypothesis is consist of multiple observations.
If this hypothesis contains outliers so that it is meaningless.
The good observations are much larger than the good hypothesis in
typical RANSAC settings.
32. Issues
Dead depth pixel
About 25% of a kinect point clouds are dead depth pixel, that
has NaN(Not a Number) depth value.
320x240=76800, and valid number of point cloud = 57365
2D image plane 3D depth data
33. VO procedures
1. Obtain features using gpuSurf at t-1 and t
Output: featureA(t-1), featureB(t)
2. CrossMatching Using L2
Output: MatchedFeaturesList
3. Find depth for each matched feature (Nr=160~200)
Output: KeypointA(t-1), KeypointB(t) in 3D world coordinate
4. Compute R,t using ICP (iteration 250, threshold..)
5. Find the current pose of camera by multiply homogeneous
transformation matrix.
R i ti
ξc = o ξt−3 t−3 ξt−2 t−2 ξt−1 t−1 ξt where ξi =
01X3 1
6. Draw x,y,z using
40. SBC status
1. Ubuntu 10.10 64Bit installed,
2. ROS Diamondback installed
PCL, Openni_kinect stack
3. CUDA Driver V3.2,
41. Plans
Cut down 54.28% size of original data.(rgb+point cloud).
(320x240 rgb+point cloud)=2,688,000Bytes => (320x240 rgb+point
cloud)=1,228,800Bytes.
Generate 3D features using surf and a kinect.
Visualization 3D matched features using PCL but has some bugs.
Using ICP on ROS(PointCloudLibrary) to get a transformation matrix.
Visualization camera pose using PCL.
Recording data with MikroKopter and SBC. (10th/June)
Put on the MikroKopter and testing.(13th/June)
Refine pose with RANSAC or EKF.... etc.
42. Plans
Cut down 54.28% size of original data.(rgb+point cloud).
(320x240 rgb+point cloud)=2,688,000Bytes => (320x240 rgb+point
cloud)=1,228,800Bytes.
Generate 3D features using surf and a kinect.
Visualization 3D matched features using PCL but has some bugs.
Using ICP on ROS(PointCloudLibrary) to get a transformation matrix.
Visualization camera pose using PCL.
Recording data with MikroKopter and SBC. (10th/June)
Put on the MikroKopter and testing.(13th/June)
Refine pose with RANSAC or EKF.... etc.