Computer Vision & Image Processing with AI Training Course

Introduction

Computer Vision (CV), empowered by Artificial Intelligence (AI), has revolutionized how machines "see," interpret, and interact with the visual world. This Computer Vision & Image Processing with AI Training Course is an immersive program designed for AI engineers, data scientists, software developers, and researchers who aim to master the techniques that enable computers to derive meaningful information from images and videos. You will delve into the core principles of digital image processing and rapidly advance to cutting-edge AI-powered visual recognition methods, including machine learning and deep learning.

This comprehensive course covers essential topics such as image manipulation, feature extraction, object detection, image segmentation, facial recognition, and real-time video analysis. Participants will gain hands-on experience with powerful libraries and frameworks like OpenCV, TensorFlow, and PyTorch, applying them to real-world challenges in diverse sectors like autonomous vehicles, healthcare, security, and manufacturing. By mastering Computer Vision and Image Processing with AI, you will be equipped to design, build, and deploy intelligent vision systems that drive innovation and solve complex problems involving visual data.

Target Audience

• AI/ML Engineers and Data Scientists.
• Software Developers interested in visual AI applications.
• Robotics Engineers working on perception systems.
• Image and Video Analysts.
• Researchers in AI and Computer Science.
• Professionals seeking to specialize in visual data processing.

Duration

10 days

Course Objectives

1. Understand the foundational concepts of digital images and core image processing techniques.
2. Learn various methods for feature extraction and description from visual data.
3. Apply traditional Machine Learning algorithms for image classification tasks.
4. Master Deep Learning architectures, especially Convolutional Neural Networks (CNNs), for advanced image recognition.
5. Implement state-of-the-art techniques for object detection and image segmentation.
6. Explore the principles and applications of facial recognition and other biometrics.
7. Gain proficiency in real-time computer vision and video analysis.
8. Address ethical considerations, bias, and privacy issues in AI-driven computer vision systems.

Course Content

Module 1. Foundations of Computer Vision

• What is Computer Vision: Definition, goals, and historical overview
• The human visual system vs. machine vision
• Key applications and impact of Computer Vision across industries
• Introduction to the Computer Vision pipeline: Acquisition, processing, analysis, understanding
• Overview of common Computer Vision libraries and frameworks (e.g., OpenCV, PIL)

Module 2. Digital Image Fundamentals

• Digital image representation: Pixels, resolution, aspect ratio
• Image types: Grayscale, RGB, HSV, and other color models
• Image file formats and compression techniques
• Basic image manipulation: Resizing, cropping, rotation, flipping
• Image acquisition and noise sources in digital images

Module 3. Basic Image Processing Techniques

• Image filtering: Smoothing (Gaussian, Median), sharpening (Laplacian, Unsharp Masking)
• Image enhancement: Contrast stretching, histogram equalization
• Morphological operations: Erosion, dilation, opening, closing
• Image thresholding: Binarization, adaptive thresholding
• Noise reduction techniques and de-noising filters

Module 4. Feature Extraction & Description

• Importance of features in computer vision
• Edge detection: Sobel, Prewitt, Canny operators
• Corner detection: Harris Corner Detector, Shi-Tomasi
• Keypoint detection and description: SIFT, SURF, ORB
• Histogram of Oriented Gradients (HOG) for object shape description
• Feature matching and homography estimation

Module 5. Machine Learning for Computer Vision

• Traditional Machine Learning algorithms for image classification
• Support Vector Machines (SVMs) and K-Nearest Neighbors (KNN) for image data
• Principal Component Analysis (PCA) for dimensionality reduction in image features
• Supervised vs. Unsupervised learning in a computer vision context
• Building basic image classifiers using scikit-learn

Module 6. Deep Learning for Image Recognition

• Introduction to Neural Networks and their application to images
• Convolutional Neural Networks (CNNs): Architecture, convolutional layers, pooling layers
• Activation functions, loss functions, and optimizers for CNNs
• Training and fine-tuning CNNs: Transfer learning with pre-trained models (e.g., VGG, ResNet)
• Regularization techniques for deep learning models (dropout, batch normalization)

Module 7. Object Detection

• Introduction to Object Detection: Localizing and classifying multiple objects in an image
• Two-stage detectors: R-CNN, Fast R-CNN, Faster R-CNN
• One-stage detectors: YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector)
• Non-Maximum Suppression (NMS) and Intersection Over Union (IoU)
• Practical implementation of object detection models

Module 8. Image Segmentation

• Understanding Image Segmentation: Pixel-level classification
• Semantic Segmentation: Classifying each pixel into a category
• Instance Segmentation: Identifying and segmenting individual object instances
• Architectures for segmentation: U-Net, Mask R-CNN
• Applications of segmentation in medical imaging, autonomous driving, etc.

Module 9. Facial Recognition & Biometrics

• Face Detection vs. Face Recognition: Key differences
• Traditional face detection methods (e.g., Haar Cascades)
• Deep learning-based face detection and landmark localization
• Face verification and identification using embeddings (e.g., FaceNet)
• Ethical considerations and privacy implications of facial recognition technology

Module 10. Real-time Computer Vision & Video Analysis

• Processing video streams: Frame extraction, real-time inference
• Motion detection and background subtraction
• Object tracking algorithms (e.g., Kalman Filters, SORT)
• Human pose estimation and activity recognition
• Optimizing models for real-time performance on edge devices

Module 11. Computer Vision Applications

• Autonomous Vehicles: Perception for self-driving cars and drones
• Healthcare: Medical image analysis, disease detection, surgical assistance
• Retail: Customer behavior analysis, inventory management, automated checkout
• Security and Surveillance: Anomaly detection, crowd analysis, access control
• Industrial Automation: Quality control, robotic vision, defect inspection

Module 12. Ethical AI in Computer Vision & Future Trends

• Bias in Computer Vision systems: Sources and mitigation strategies
• Privacy concerns related to visual data and surveillance
• Explainable AI (XAI) in Computer Vision: Understanding model decisions
• Legal and societal implications of widespread CV deployment
• Emerging trends: Generative Adversarial Networks (GANs) for image generation, Vision Transformers, multi-modal AI

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank