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About Project » History » Revision 3

Revision 2 (Sandeep GANESAN, 10/30/2025 09:02 AM) → Revision 3/5 ( Yaroslav MAYDEBURA, 10/30/2025 03:53 PM)

h1. ๐ŸŽฏ About Project - G12-2025 

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 h2. I. ๐Ÿ“– Project Overview 

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 Our project focuses on developing a *high-quality image composition system* that seamlessly merges two or more projected images into a single, visually consistent display. 

    Using *Python* **Python** and *OpenCV*, **OpenCV**, we employ advanced image processing techniques such as: 
 * ๐ŸŽจ *Gamma Correction* - For as *gamma correction*, *alpha blending*, and *intensity modification* to eliminate brightness normalization 
 * ๐Ÿ”„ *Alpha Blending* - For smooth transitions 
 * โœจ *Intensity Modification* - For and color consistency 

 mismatches between overlapping regions. The project is organized into specialized sub-teams responsible for software development, UML design, testing, and wiki management. Each member plays a key role in ensuring collaborative progress and well-structured documentation. 
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 *Key Integration Tools:* 
 * ๐Ÿ“š *Doxygen* - Automated By integrating **Doxygen** for code documentation 
 * ๐Ÿ“Š *Redmine* - Project tracking and management 
 * ๐ŸŽจ *Astah* - UML diagram creation 

 Our goal is **Redmine** for project tracking, we aim to produce a well-documented, scalable, and reproducible system for real-time image correction and blending. 

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 h2. II. ๐Ÿ’ก Motivation & Problem Statement 

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 h3. The Challenge 

 When using multiple projectors to display a single image, several issues arise: 

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 โš ๏ธ *Common Problems:* 
 * Visible visible seams or brightness inconsistencies often occur in overlapping regions 
 * Brightness regions. These inconsistencies between projectors 
 * Color mismatches at boundaries 
 * Uneven degrade image quality and make the final projection quality 
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 h3. Current Limitations 

 * โฑ๏ธ appear uneven. Manual calibration is methods are time-consuming 
 * ๐ŸŽฏ Human error in manual adjustments 
 * ๐Ÿ’ฐ Hardware-based solutions are expensive 
 * ๐Ÿ”ง Limited flexibility for different setups 

 h3. and prone to human error. Our Solution 

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 โœจ *Software-Based Automation* 

 We motivation is to develop an intelligent a software-based approach that automates the alignment and blending process, ensuring seamless image projection. By leveraging the **OpenCV** library, the system that: 
 1. *Detects* can detect overlapping areas automatically 
 2. *Applies* areas, apply brightness corrections in real-time 
 3. *Blends* corrections, and blend images smoothly using alpha blending 
 4. *Eliminates* smoothlyโ€”eliminating the need for costly hardware hardware-based calibration systems. 

 By leveraging the *OpenCV* library, our system automates the entire alignment and blending process, ensuring seamless image projection with minimal manual intervention. 
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 h2. III. ๐ŸŽฏ Objectives 

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 |_. # |_. Objective |_. Status | 
 | 1 | Develop * To develop an automated image blending system for multiple capable of merging two or more projections | ๐Ÿ”„ In Progress | into a single seamless image. 
 | 2 | Apply gamma correction * To apply *gamma correction* and intensity modification *intensity modification* techniques | โœ… Algorithm Designed | to balance color and brightness across overlapping regions. 
 | 3 | Implement alpha blending * To implement *alpha blending* for smooth transitions | โœ… Implemented | between images. 
 | 4 | Design * To design and visualize the system architecture using UML diagrams | โœ… Complete | **UML diagrams**. 
 | 5 | Document * To document the entire project using Doxygen | ๐Ÿ”„ Ongoing | 
 | 6 | Manage **Doxygen** and manage tasks and track progress via Redmine | โœ… Active | **Redmine**. 

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 h2. IV. ๐ŸŒŸ Key Features 

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 h3. Core Capabilities 

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 *๐ŸŽจ Image Processing Features:* 
 * Multi-image composition and merging 
 * Automatic overlap detection 
 * Real-time brightness adjustment 
 * Color consistency maintenance 
 * Seamless boundary blending 
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 h3. Technical Features 

 * *โšก Performance:* Real-time processing capability 
 * *๐Ÿ”ง Flexibility:* Supports 2+ projector configurations 
 * *๐Ÿ“Š Accuracy:* Pixel-level precision in blending 
 * *๐ŸŽฏ Reliability:* Consistent results across different inputs 

 h3. System Features 

 * *๐Ÿ“š Documentation:* Comprehensive Doxygen-generated docs 
 * *๐Ÿงช Testing:* Automated test suite for quality assurance 
 * *๐Ÿ”„ Version Control:* Git-based collaborative development 
 * *๐Ÿ“Š Project Management:* Redmine integration for tracking 

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 h2. V. ๐Ÿ—๏ธ System Architecture 

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 h3. Architecture Overview 

 Our system follows a modular design pattern with clear separation of concerns: 

 *Layer 1: Input Processing* 
 * Image acquisition from multiple sources 
 * Pre-processing and validation 

 *Layer 2: Analysis & Correction* 
 * Overlap detection algorithms 
 * Gamma correction calculations 
 * Intensity adjustment computations 

 *Layer 3: Blending & Composition* 
 * Alpha blending engine 
 * Final image composition 
 * Real-time rendering 

 *Layer 4: Output & Display* 
 * Optimized image delivery 
 * Multi-projector synchronization 

 _For detailed UML diagrams, see [[UML Diagrams]]_ 
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 h2. VI. ๐Ÿ”ฌ Methodology and Development Process 

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 h3. Development Approach 

 We follow an *Agile-inspired iterative development process*: 

 |_. Phase |_. Activities |_. Duration | 
 | Planning | Requirements analysis, team formation | Week 1-2 | 
 | Design | UML diagrams, architecture design | Week 3-4 | 
 | Development | Core algorithm implementation | Week 5-8 | 
 | Testing | Unit testing, integration testing | Week 7-9 | 
 | Documentation | Code docs, wiki, user guides | Ongoing | 
 | Refinement | Optimization and bug fixes | Week 10+ | 

 h3. Quality Assurance Process 

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 *Testing Strategy:* 
 1. *Unit Testing* - Individual component validation 
 2. *Integration Testing* - System-wide functionality 
 3. *Performance Testing* - Speed and efficiency metrics 
 4. *User Acceptance Testing* - Real-world scenario validation 
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 h3. Collaboration Tools 

 * *Version Control:* Git & GitHub for code management 
 * *Project Tracking:* Redmine for task and issue management 
 * *Communication:* Regular team meetings and Slack 
 * *Documentation:* Doxygen for code, Wiki for project info 

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 h2. VII. ๐Ÿ› ๏ธ Technology Stack 

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 h3. Programming & Libraries 

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 *Core Technologies:* 

 |_. Technology |_. Purpose |_. Version | 
 | !https://img.shields.io/badge/Python-3.9+-blue! | Primary language | 3.9+ | 
 | !https://img.shields.io/badge/OpenCV-4.x-green! | Image processing | 4.x | 
 | !https://img.shields.io/badge/NumPy-Latest-orange! | Numerical computations | Latest | 
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 h3. Development Tools 

 


 * *๐ŸŽจ Astah* - UML diagram creation and management *Python (OpenCV, NumPY)* 
 * *๐Ÿ“š Doxygen* - Automated documentation generation *Doxygen* 
 * *๐Ÿ“Š Redmine* - Project management and issue tracking *Redmine* 
 * *๐Ÿ”ง Git* - Version control and collaboration *Astah* 

 h3. Development Environment 

 * *IDE:* PyCharm / VS Code 
 * *OS:* Cross-platform (Windows, macOS, Linux) 
 * *Testing:* pytest framework 
 * *CI/CD:* GitHub Actions (planned) 

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 h2. VIII. ๐ŸŒ Application & Impact 

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 h3. Real-World Applications 

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 *Industry Use Cases:* 

 ๐ŸŽญ *Entertainment & Events* 
 * Large-scale concert projections 
 * Theater and stage productions 
 * Immersive art installations 

 ๐Ÿข *Corporate & Education* 
 * Conference room presentations 
 * Educational institutions 
 * Training facilities 

 ๐ŸŽฎ *Gaming & Simulation* 
 * Flight simulators 
 * Virtual reality environments 
 * Gaming arcades 

 ๐Ÿ›๏ธ *Museums & Exhibitions* 
 * Interactive displays 
 * Historical recreations 
 * Planetariums 
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 h3. Project Impact 

 * *Cost Reduction:* Eliminates expensive hardware calibration systems 
 * *Time Savings:* Automated process vs. manual adjustment 
 * *Quality Improvement:* Consistent, reproducible results 
 * *Accessibility:* Software-based solution available to more users 
 * *Scalability:* Easily adapts to different projector configurations 

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 h2. IX. ๐Ÿš€ Limitations Limitation & Future Enhancements 

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 h3. Current Limitations 

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 โš ๏ธ *Known Constraints:* 
 * Processing time increases with image resolution 
 * Requires compatible projector specifications 
 * Limited to static image composition (no video yet) 
 * Calibration needed for each new setup 
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 h3. Planned Enhancements 

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 *Phase 2 Features:* 

 ๐ŸŽฅ *Video Support* 
 * Real-time video blending 
 * Multi-stream synchronization 

 ๐Ÿค– *AI Integration* 
 * Machine learning for automatic calibration 
 * Intelligent scene detection 

 โšก *Performance Optimization* 
 * GPU acceleration 
 * Parallel processing implementation 

 ๐ŸŒ *Extended Compatibility* 
 * Support for more projector models 
 * Cloud-based processing option 

 ๐Ÿ“ฑ *User Interface* 
 * GUI for non-technical users 
 * Mobile app for remote control 

 ๐Ÿ”ง *Advanced Features* 
 * 3D projection mapping 
 * Curved surface support 
 * Dynamic brightness adjustment 
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 h3. Research Opportunities 

 * Integration with IoT devices for smart environments 
 * Edge computing implementation for distributed systems 
 * Advanced color science algorithms 
 * Virtual reality applications 

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 *๐Ÿ“… Last Updated:* October 30, 2025 
 *๐Ÿ“ Maintained by:* Documentation Team 
 [[Group Members|View Team]] | [[UML Diagrams|Technical Design]] | [[Class Activity|Progress Log]] 
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