Computer Vision Basics

This course is designed to provide a solid foundation in computer vision techniques and their applications in Production Control and Operations (P&OC). 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 production monitoring, quality control, and inventory management.

• 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 production environments

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

• Develop computer vision applications for various P&OC use cases, such as production monitoring, quality control, and inventory management

1. Introduction to Computer Vision in P&OC

• Overview of computer vision and its applications in Production Control and Operations

• Digital image fundamentals (pixels, color spaces, image formats)

• Image processing basics (reading, writing, and displaying images)

• Hands-on exercises: Basic image manipulation using Python and OpenCV

2. Image Processing Techniques for P&OC

• 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 production images

3. Object Detection in P&OC

• Template matching and its limitations

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

• Cascade classifiers and Haar-like features for detecting objects in production environments

• Deep learning-based object detection (YOLO, SSD, and Faster R-CNN) for quality control and inventory management

• Hands-on exercises: Detecting objects of interest in production images and videos

4. Image Segmentation for P&OC

• 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 defect detection and product classification

• Hands-on exercises: Segmenting production images for quality control and inventory management

5. Applications and Advanced Topics in P&OC

• Case studies of computer vision applications in Production Control and Operations (e.g., production monitoring, quality control, inventory management)

• Introduction to 3D computer vision and point cloud processing for robotic manipulation and assembly

• Challenges and future directions in computer vision for P&OC

• Hands-on exercises: Developing a computer vision application for a specific P&OC 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

• Knowledge of production control and operations management principles