Computer Vision Engineer Certification Program (CVEC)

Introduction to Computer Vision

• Understanding Digital Image Fundamentals
• Image Representation and Transformation Techniques
• Feature Extraction Techniques: Edge Detection, HOG, and SIFT

Image Processing and Analysis

• Image Filtering, Smoothing, and Morphological Operations
• Histogram Equalization and Contrast Enhancement
• Working with OpenCV and Scikit-Image Libraries

Deep Learning for Computer Vision

• Convolutional Neural Networks (CNNs): VGG, ResNet, and Inception
• Advanced CNN Architectures: DenseNet, MobileNet, EfficientNet
• Object Detection Models: YOLO, Faster R-CNN, SSD

3D Computer Vision and Visual SLAM

• Understanding 3D Vision: Depth Estimation and Stereo Vision
• Structure from Motion (SfM) and Visual Odometry
• Simultaneous Localization and Mapping (SLAM) Techniques

Real-Time Computer Vision Applications

• Video Analysis and Activity Recognition
• Real-Time Object Detection and Tracking
• Face Recognition and Emotion Detection Systems

Generative Models for Vision

• Introduction to GANs: StyleGAN, CycleGAN, Pix2Pix
• Image Synthesis and Style Transfer
• Implementing Image-to-Image Translation Models

Computer Vision in Industry Use-Cases

• Case Studies in Healthcare: Automated Medical Imaging and Diagnostics
• Autonomous Driving: Lane Detection, Traffic Sign Recognition
• Smart Retail: Visual Search and Automated Checkout Systems

Capstone Project

• Choose a Real-World Problem (e.g., Autonomous Driving, Medical Imaging)
• Develop a Complete Vision-Based Solution Using Deep Learning
• Present Findings and Implementation with Detailed Documentation