This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - II [Computer Graphics] (Old Course). [Year - June / 2014] . . .Solution Set of this Paper is Coming soon..
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - II [Computer Graphics] (Old Course). [Year - September / 2013] . . .Solution Set of this Paper is Coming soon..
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - II [Computer Graphics] (Old Course). [Year - June / 2014] . . .Solution Set of this Paper is Coming soon..
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - II [Computer Graphics] (Old Course). [Year - September / 2013] . . .Solution Set of this Paper is Coming soon..
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - III [Computer Graphics] (Revised Course). [Year - June / 2016] . . .Solution Set of this Paper is Coming soon...
4-CONNECTED AND 8-CONNECTED NEIGHBOR SELECTION By Sintiak HaqueSintiak haque
Boundary fill algorithm is used frequently in computer graphics to fill a desired color inside a closed polygon having the same boundary color for all of its sides.Boundary Fill Algorithm starts at a pixel inside the polygon to be filled and paints the interior proceeding outwards towards the boundary. This algorithm works only if the color with which the region has to be filled and the color of the boundary of the region are different.
Geographic Information System (May – 2016) [75:25 Pattern | Question Paper]Mumbai B.Sc.IT Study
mumbai bscit study, kamal t, mumbai university, old question paper, previous year question paper, bscit question paper, bscit semester vi, semester vi question paper, internet technology, april - 2015, 75:25 Pattern, 60:40 Pattern, revised syllabus, old syllabus, cbsgc, question paper, may - 2016, april - 2017, april - 2014, april - 2013, may – 2016, october – 2016, digital signals and system, data warehousing, geographic information system
This file contains all the practicals with output regarding GTU syllabus. so it will help to IT and Computer engineering students. It is really knowledgeable so refer these for computer graphics practicals.
This is a Question Papers of Mumbai University for B.Sc.IT Student of Semester - III [Computer Graphics] (Revised Course). [Year - June / 2016] . . .Solution Set of this Paper is Coming soon...
4-CONNECTED AND 8-CONNECTED NEIGHBOR SELECTION By Sintiak HaqueSintiak haque
Boundary fill algorithm is used frequently in computer graphics to fill a desired color inside a closed polygon having the same boundary color for all of its sides.Boundary Fill Algorithm starts at a pixel inside the polygon to be filled and paints the interior proceeding outwards towards the boundary. This algorithm works only if the color with which the region has to be filled and the color of the boundary of the region are different.
Geographic Information System (May – 2016) [75:25 Pattern | Question Paper]Mumbai B.Sc.IT Study
mumbai bscit study, kamal t, mumbai university, old question paper, previous year question paper, bscit question paper, bscit semester vi, semester vi question paper, internet technology, april - 2015, 75:25 Pattern, 60:40 Pattern, revised syllabus, old syllabus, cbsgc, question paper, may - 2016, april - 2017, april - 2014, april - 2013, may – 2016, october – 2016, digital signals and system, data warehousing, geographic information system
This file contains all the practicals with output regarding GTU syllabus. so it will help to IT and Computer engineering students. It is really knowledgeable so refer these for computer graphics practicals.
COMPARISON OF GPU AND FPGA HARDWARE ACCELERATION OF LANE DETECTION ALGORITHMsipij
The two fundamental components of a complete computer vision system are detection and classification.
The Lane detection algorithm, which is used in autonomous driving and smart vehicle systems, is within the
computer vision detection area. In a sophisticated road environment, lane marking is the responsibility of
the lane detection system. The warning system for a car that leaves its lane also heavily relies on lane
detection. The two primary stages of the implemented lane detection algorithm are edge detection and line
detection. In order to assess the trade-offs for latency, power consumption, and utilisation, we will
compare the state-of-the-art implementation performance attained with both FPGA and GPU in this work.
Our analysis highlights the benefits and drawbacks of the two systems.
Comparison of GPU and FPGA Hardware Acceleration of Lane Detection Algorithmsipij
The two fundamental components of a complete computer vision system are detection and classification.
The Lane detection algorithm, which is used in autonomous driving and smart vehicle systems, is within the
computer vision detection area. In a sophisticated road environment, lane marking is the responsibility of
the lane detection system. The warning system for a car that leaves its lane also heavily relies on lane
detection. The two primary stages of the implemented lane detection algorithm are edge detection and line
detection. In order to assess the trade-offs for latency, power consumption, and utilisation, we will
compare the state-of-the-art implementation performance attained with both FPGA and GPU in this work.
Our analysis highlights the benefits and drawbacks of the two systems.
Interactive Editing of Signed Distance FieldsMatthias Trapp
Presentation of paper "Interactive Editing of Voxel-Based Signed Distance Fields" presented at the 30th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2022).
OpenCVand Matlab based Car Parking System Module for Smart City using Circle ...JANAK TRIVEDI
finding parking availability for a specific time period is
a very tedious job in urban areas. The Indian government now
focusing on t he smart city project, already they published city
name for a n upcoming smart city project. In smart city
application , intelligent transportation system (ITS) plays an
important role- in that finding parking place, specifically for the
car owner to avoid time computation, as well as congestion in
traffic is going to be very important. In this article, we propose
an intelligent car parking system for the smart city using Circle
Hough Transform (CHT).
Build Your Own 3D Scanner: 3D Scanning with Structured LightingDouglas Lanman
Build Your Own 3D Scanner:
3D Scanning with Structured Lighting
http://mesh.brown.edu/byo3d/
SIGGRAPH 2009 Courses
Douglas Lanman and Gabriel Taubin
This course provides a beginner with the necessary mathematics, software, and practical details to leverage projector-camera systems in their own 3D scanning projects. An example-driven approach is used throughout; each new concept is illustrated using a practical scanner implemented with off-the-shelf parts. The course concludes by detailing how these new approaches are used in rapid prototyping, entertainment, cultural heritage, and web-based applications.
Photo-realistic Single Image Super-resolution using a Generative Adversarial ...Hansol Kang
* Ledig, Christian, et al. "Photo-realistic single image super-resolution using a generative adversarial network." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
LSGAN - SIMPle(Simple Idea Meaningful Performance Level up)Hansol Kang
LSGAN은 기존의 GAN loss가 아닌 MSE loss를 사용하여, 더욱 realistic한 데이터를 생성함.
LSGAN 논문 리뷰 및 PyTorch 기반의 구현.
[참고]
Mao, Xudong, et al. "Least squares generative adversarial networks." Proceedings of the IEEE International Conference on Computer Vision. 2017.
InfoGAN : Interpretable Representation Learning by Information Maximizing Gen...Hansol Kang
InfoGAN은 기존 GAN이 manupulation이 어렵다는 단점을 극복함. latent space에 z 이외에 c(condition)을 부여하여 원하는 결과물을 얻을 수 있음. c에 대해 잘 학습하기 위해 Mutual information을 이용해 상관관계를 부여함.
InfoGAN 논문 리뷰 및 PyTorch 기반의 구현.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/taeoh-kim/Pytorch_InfoGAN
Chen, Xi, et al. "Infogan: Interpretable representation learning by information maximizing generative adversarial nets." Advances in neural information processing systems. 2016.
Deep Convolutional GANs - meaning of latent spaceHansol Kang
DCGAN은 GAN에 단순히 conv net을 적용했을 뿐만 아니라, latent space에서도 의미를 찾음.
DCGAN 논문 리뷰 및 PyTorch 기반의 구현.
VAE 세미나 이슈 사항에 대한 리뷰.
my github : https://github.com/messy-snail/GAN_PyTorch
[참고]
https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
https://github.com/taeoh-kim/Pytorch_DCGAN
Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).
쉽게 설명하는 GAN (What is this? Gum? It's GAN.)Hansol Kang
Original GAN 논문 리뷰 및 PyTorch 기반의 구현.
딥러닝 개발환경 및 언어 비교.
[참고]
Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.
Wang, Su. "Generative Adversarial Networks (GAN) A Gentle Introduction."
초짜 대학원생의 입장에서 이해하는 Generative Adversarial Networks (https://jaejunyoo.blogspot.com/)
1시간만에 GAN(Generative Adversarial Network) 완전 정복하기 (https://www.slideshare.net/NaverEngineering/1-gangenerative-adversarial-network)
프레임워크 비교(https://deeplearning4j.org/kr/compare-dl4j-torch7-pylearn)
AI 개발에AI 개발에 가장 적합한 5가지 프로그래밍 언어 (http://www.itworld.co.kr/news/109189#csidxf9226c7578dd101b41d03bfedfec05e)
Git는 머꼬? GitHub는 또 머지?(https://www.slideshare.net/ianychoi/git-github-46020592)
svn 능력자를 위한 git 개념 가이드(https://www.slideshare.net/einsub/svn-git-17386752)
신뢰 전파 기법을 이용한 스테레오 정합(Stereo matching using belief propagation algorithm)Hansol Kang
스테레오 정합, 신뢰 전파 기법에 대한 개념과 간단한 예제.
[참고]
J.H. Kim, and Y.H. Ko, “Multibaseline based Stereo Matching Using Texture adaptive Belief Propagation Technique." Journal of the Institute of Electronics and Information Engineers Vol. 50, No. 1, pp.75-85, 2013.
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
1. Continuously Adaptive Mean Shift (CAMSHIFT)
Han-Sol Kang
Image Processing Term Project
* Bradski, Gary R. "Computer vision face tracking for use in a perceptual user interface." (1998).
*
2015.12.15
4. 2015-12-15
01CAMSHIFT Algorithm
4
Color Probability Distributions
misconception : Different color models are needed for different races of people
Except for albinos, humans are all the same color(hue)
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5. 2015-12-15
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How to Calculate the Mean Shift Algorithm
1. Choose a search window size
2. Choose the initial location of the search window
3. Compute the mean location in the search window
4. Center the search window at the mean location computed in Step 3
5. Repeat Steps 3 and 4 until convergence (or until the mean location moves less than a preset threshold)
3x3?
5x5?
15x15?
6. 2015-12-15
01CAMSHIFT Algorithm
6
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1. Choose the initial location of the search window
2. Mean Shift as above(one or many iteration); store the zeroth moment
3. Set the search window size equal to a function of the zeroth moment found in Step 2
4. Repeat Steps 2 and 3 until convergence (mean location moves less than a preset threshold)
8. 2015-12-15
01CAMSHIFT Algorithm
8
How to Calculate the Coupled CAMSHIFT Algorithm
1. First, set the calculation region of the probability distribution to the whole image
2. Choose the initial location of the 2D mean shift search window
3. Calculate the color probability distribution in the 2D region centered at the search window location in an area slightly larger than
the mean shift window size
4. Mean shift to convergence or for a set number of iterations. Store the zeroth moment (area or size) and mean location.
5. For the next video frame, center the search the search window at the mean location stored in Step 4 and set the window size to
a function of the zeroth moment found there. Go to Step 3
9. 2015-12-15
01CAMSHIFT Algorithm
9
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10. 2015-12-15
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02Source Code
11
Calculation of Head Roll
CTrackTestDlg
HSI_Calc_Func
Calc_Hue_Hist
momentCenter
CAMSHIFT
void Calc_Hue_Hist(int imWidth, int imHeight, int xl, int xh, int yl, int yh, unsigned char* H, float* HistH)
void momentCenter(unsigned char* H, int imWidth, int imHeight, WPoint& w, int& xc, int& yc, float& angle)
void CAMSHIFT(unsigned char* H, int imWidth, int imHeight, WPoint& w, int& xc, int& yc, float& angle)
void HSI_Calc_Func(unsigned char* R, unsigned char* G, unsigned char* B, unsigned char* H, int width, int height)