Computer Vision Basics

This course is designed to provide a solid foundation in computer vision techniques and their applications in the Transportation & Logistics industries. Participants will learn the fundamental concepts and algorithms used in image processing, object detection, and image segmentation. The course covers various computer vision libraries and tools, enabling participants to develop practical skills in analyzing and interpreting visual data relevant to transportation and logistics, such as satellite imagery, traffic camera feeds, and warehouse surveillance.

• Understand the fundamental concepts and techniques in computer vision

• Implement basic image processing operations, such as filtering, edge detection, and morphological transformations

• Apply object detection and image segmentation algorithms to identify and localize objects of interest in transportation and logistics imagery

• Utilize computer vision libraries, such as OpenCV and scikit-image, for efficient image processing and analysis

• Develop computer vision applications for various Transportation & Logistics use cases, such as traffic monitoring, vehicle tracking, and inventory management

1. Image Processing Techniques

• Image filtering (smoothing, sharpening, and noise reduction)

• Edge detection (Sobel, Canny, and Laplacian operators)

• Morphological transformations (erosion, dilation, opening, and closing)

• Hands-on exercises: Implementing image processing techniques on transportation and logistics imagery

2. Object Detection

• Template matching and its limitations

• Feature-based object detection (SIFT, SURF, and ORB)

• Cascade classifiers and Haar-like features for vehicle detection

• Deep learning-based object detection (YOLO, SSD, and Faster R-CNN) for transportation and logistics applications

• Hands-on exercises: Detecting objects of interest in traffic camera feeds and warehouse surveillance footage

3. Image Segmentation

• Thresholding techniques (global, adaptive, and Otsu's method)

• Region-based segmentation (region growing and split-and-merge)

• Edge-based segmentation (watershed and graph-based methods)

• Semantic segmentation using deep learning (FCN, U-Net, and DeepLab) for road and infrastructure segmentation

• Hands-on exercises: Segmenting satellite imagery and traffic scenes

4. Applications and Advanced Topics

• Case studies of computer vision applications in the Transportation & Logistics industries (e.g., traffic monitoring, vehicle tracking, inventory management)

• Introduction to 3D computer vision and point cloud processing for autonomous vehicles and robotics

• Challenges and future directions in computer vision for transportation and logistics

• Hands-on exercises: Developing a computer vision application for a specific Transportation & Logistics use case

• Strong understanding of linear algebra and calculus

• Proficiency in programming with Python and libraries such as NumPy and Matplotlib

• Familiarity with basic machine learning concepts and techniques