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Certificate of Completion – What You'll Receive

Get a Personalized Roadmap to Land Your Dream Job in AI & Computer Vision

After successfully completing the Professional Certificate in AI Computer Vision program, you'll receive a comprehensive, multi-page certificate that showcases not only your achievement but also the depth of your learning, practical application, and industry exposure.

Professional Certification

Your Professional Certification Includes

Upon completion, you’ll receive a detailed digital certificate with these components:

CERTIFIED

Professional Certification

in

AI & 2D+3D Computer Vision

This is to certify that

[ Student Full Name ]

has successfully completed all requirements
Duration: 6 Months
Time: 230+ Hours
Completion Date: [ Month Year ]
Credential ID: MP-AICV-[UniqueID]

Director

MatPixel Academy

QR Code

Verify at: matpixel.com/verify

Curriculum Details

Credential ID: MP-AICV-[UniqueID]

Courses Completed

  • Python Essentials for AI Computer Vision Engineers
  • Hands-on CV for Developers: OpenCV Mastery
  • Master 2D AI Computer Vision: Deep Learning from CNNs to Transformers
  • Mastering Single-Camera Calibration for AI Vision Systems
  • Multi-Camera Vision: Stereo, Depth, Calibration & Synchronization
  • Camera Pose Estimation
  • Point Cloud Fundamentals: From LiDAR to 3D Scenes
  • Advanced 3D Point Cloud Processing
  • Hands-on 3D Computer Vision for Developers
  • 3D Reconstruction: From Images & Point Clouds to 3D Models
  • Master 3D Deep Learning in AI Vision: Object Detection & Beyond
  • Advanced 3D Deep Learning Architectures: NeRF & Vision Breakthroughs
  • Workflow Script Automation for AI CV Projects
  • AI & Computer Vision for SDC/ADAS Systems

Software & Tools Mastered

  • Python, Numpy, Pandas, Matplotlib, scikit-image, Scikit-Learn
  • OpenCV, PIL (Pillow), TorchVision, Albumentations, Detectron2, ImageAI, MediaPipe, OpenCV-ML, Makesense, CVAT
  • Open3D, PyntCloud, MeshIO, MeshLab, PCL (PointCloud Lib), TriMesh, Mayavi, COLMAP, CARLA, Kaolin (NVIDIA), MinkowskiEngine
  • Keras, PyTorch, PyTorch3D, MMDetection3D, Learning3D
  • DALI (NVIDIA), CloudCompare

Key Projects

Credential ID: MP-CV-[UniqueID]

🔍 Image-Based 5-Class Search Engine: Multi-Class Image Retrieval System with Feature Embedding
  • Top-5 Retrieval Precision: 93.7%, Mean Average Precision (mAP): 0.82
  • Retrieval Latency (FAISS): 0.14 seconds/query, Embedding Clustering Silhouette Score: 0.74
🖼️ GANs for Fake Image Identification: Forensic GAN Discriminator for Social Media Fact-Checking
  • DeepFake Detection Accuracy: 97.1%, AUC-ROC: 0.94
  • False Positive Rate: 3.6%, Detection Latency: 0.08 seconds/image
🚁 Drone-Based Agriculture: Multispectral Crop Health Monitoring with Drone Imagery
  • Crop Segmentation IoU (UNet): 0.86, Disease Detection Accuracy: 88.9%
  • Yield Prediction RMSE: 3.2 tons/hectare, NDVI-Based Health Score Correlation: 0.91
🏠 3D Modeling for Insurance: Damage Assessment from Photos to Interactive 3D Reconstruction
  • 3D Reconstruction Completeness: 93.5%, Surface Error (Hausdorff Distance): 1.8 mm
  • Texture Quality Index (TQI): 0.79, SfM Processing Time: 3.5 minutes per scene
🌑 NeRF-Based 3D Reconstruction in Poor Illumination: Robust Low-Light NeRF with Adaptive Denoising
  • PSNR (Peak Signal-to-Noise Ratio): 28.4 dB, Structural Similarity Index (SSIM): 0.88
  • Rendering Time (per viewpoint): 1.2 seconds, Completion Score Under Low Light: 90.2%
🧠 3D Face Recognition: Pose-Invariant 3D Face Recognition Using Point Clouds and Deep Embeddings
  • Face Verification Accuracy (BU-3DFE): 96.8%, Point Cloud Registration RMSE: 1.2 mm
  • Recognition Rate across Poses: 94.3%, Cosine Similarity Threshold (Acceptable Match): >0.85

Capstone & Internship

Credential ID: MP-CV-[UniqueID]

Capstone Project: Autonomous Drone Navigation System

Technical Implementation:
  • Developed vision-based navigation using YOLOv8 for real-time object detection
  • Implemented ORB-SLAM for simultaneous localization and mapping
Key Results:
  • Achieved 85% mission success rate in dynamic indoor/outdoor environments
  • Pioneered sensor fusion algorithm that reduced computational requirements by 40%
Innovation Highlights:
  • Novel hybrid approach combining classical CV with deep learning
  • Optimized for edge deployment on NVIDIA Jetson platform

Industry Internship

[Company Name] • [Duration] • Role: Computer Vision Intern

Project: AI-Based Manufacturing Parts Defect Detection

Contributions:
  • Developed defect detection system achieving 99.2% accuracy
  • Optimized vision pipeline reducing processing time by 30%
  • Integrated ML model into production environment

Certificate Features

🔒
Blockchain Verified

Tamper-proof digital credential

📱
Shareable

LinkedIn-compatible format

📊
Detailed

Includes skills verification

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