Computer Vision Workbook: Hands-On Problem Solving for University Students

Revive the art of problem-solving at the university level with Barbara Klaudel's Computer Vision Workbook. This comprehensive companion to undergraduate computer vision courses offers a blend of classical and advanced concepts, designed to reinforce key ideas through active learning.

Content Overview

• Classical CNN architectures and YOLO
• Advanced concepts: depthwise separable convolutions, KL divergence
• Spans from foundational to cutting-edge topics in computer vision

Key Features

1. Hands-On Exercises: Engage in active learning to solidify understanding
2. Learn by Doing: Develop intuition for complex concepts through guided exercises
3. Comprehensive Coverage: Refresh and deepen knowledge of essential computer vision topics

Exercise Types

• Layer-by-Layer Analysis: Detailed breakdowns of network architectures
• Comparative Studies: Side-by-side examinations of different architectures (e.g., AlexNet vs. LeNet)
• Innovative Techniques: Exploration of modern approaches like Depthwise Separable Convolutions
• Practical Applications: Calculations of output sizes, network parameter configuration, and complex operations

Additional Features

• Reading Comprehension: Analyze and understand advanced research papers
• True/False Questions: Differentiate between various tasks, architectures, and metrics
• Multiple-Choice Problems: Design appropriate solutions for real-world computer vision challenges
• Fill-in-the-Blank Exercises: Match problems with suitable tasks, datasets, models, and metrics
• Visual Analysis Tasks: Extract crucial information from plots, tables, and figures in seminal papers

This workbook is an invaluable resource for students, researchers, and practitioners aiming to enhance both their theoretical knowledge and practical skills in computer vision. While designed as a course companion, it's equally effective for self-study, offering a structured approach to mastering this complex field.