Computer Vision Basics for Quality Management 

This course equips quality professionals with the fundamental knowledge and skills of computer vision (CV). You'll explore the core concepts and techniques used by computers to "see" and analyze images and videos, enabling you to leverage CV for automating visual inspections and enhancing quality control processes. This empowers you to improve efficiency, consistency, and objectivity in quality assessments across various product types. 

• Explain the fundamental concepts of computer vision, including image formation, image processing techniques, and feature extraction.

• Identify the key components of a typical computer vision system, including image acquisition, processing, and analysis modules.

• Understand different image processing techniques for enhancing and preparing images for computer vision tasks in quality control (e.g., noise reduction, filtering, image resizing).

• Explore common feature extraction techniques in CV, such as edge detection, corner detection, and image segmentation, and their role in identifying relevant information for quality assessment.

• Apply basic image processing and feature extraction techniques using a user-friendly platform or library (e.g., OpenCV) to prepare real-world quality control images for further analysis.

• Identify potential applications of computer vision in various quality control tasks, such as visual inspection for defects, product classification by type or color, and object counting for inventory management.

• Explore popular deep learning models for image classification and object detection, understanding their potential for automated visual inspections in quality control.

• Discuss the limitations and challenges associated with implementing computer vision solutions for quality management, including data quality and ethical considerations.

• Develop a high-level plan for integrating computer vision into a specific quality control process within your company, considering the chosen techniques, potential challenges, and expected benefits.

1. Unveiling the Power of Computer Vision for Quality Management:

• • The Quality Challenge in the Visual Age: Highlighting the increasing reliance on visual data in quality control and introducing computer vision as a powerful tool for automating visual inspections.

  • Delving into the fundamental concepts of computer vision, including image formation, digital image representation (pixels), and basic image processing techniques for enhancing image quality.

  • Case Study 1: Analyzing a real-world scenario of using computer vision for automated visual inspection of manufactured goods, showcasing the benefits and considerations for implementation.

  • Interactive Workshop: Exploring different types of visual data relevant to quality control (e.g., product images, inspection videos) and discussing how computer vision can be applied for automated analysis.

  • Hands-on Session 1: Utilizing a user-friendly platform or library (e.g., OpenCV) to apply basic image processing techniques (e.g., noise reduction, filtering) to real-world quality control images and observe the effects on image quality.

2. From Images to Insights: Extracting Features for Quality Control:

• Understanding image feature extraction techniques in CV, such as edge detection, corner detection, and image segmentation, and their role in identifying key elements for quality assessment.

• Hands-on Session 2: Applying image feature extraction techniques (e.g., edge detection) using the chosen platform or library (e.g., OpenCV) to extract relevant features from real-world quality control images.

• The Power of Deep Learning in CV: Introducing deep learning models for image classification and object detection, understanding their potential for automated visual inspections in quality control.

• Exploring Pre-trained Models: Discussing pre-trained deep learning models for CV tasks and exploring their potential for quick implementation in specific quality control scenarios.

• The Future of CV in Quality Management: Exploring emerging trends in computer vision and deep learning, and their potential impact on future quality control practices (e.g., real-time anomaly detection in video streams).

• Course Wrap-up & Project Presentations: Teams develop a high-level plan for integrating computer vision into a specific quality control process within their company. The plan should consider the chosen techniques (image processing, feature extraction, or deep learning), potential challenges (data collection, model training), and expected benefits for quality improvement. 

• 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