Overview
Explore edge detection techniques in computer vision through this comprehensive lecture from the University of Central Florida's CAP5415 Computer Vision course. Delve into the fundamentals of edge detection, including its importance, types of edges, and real-world applications. Learn about filtering, smoothing, and evaluation methods for edge detection algorithms. Examine design criteria and various approaches, including human edge detection and face detection. Gain insights into second-order derivative techniques and their application to real images. This lecture, part of a broader course covering mathematical preliminaries, coordinate transforms, image filtering, neural networks, deep learning, segmentation, classification, and object detection, provides essential knowledge for students and professionals in the field of computer vision and artificial intelligence.
Syllabus
Introduction
Outline
Class Project
Example
Why do we see edges
Types of edges
Why perform edge detection
Real world examples
Real World Example
Simple Example
Filtering
Smoothing
Evaluation
Design Criteria
Algorithms
Human Edge Detection
Human Face Detection
Private Edge Detection
Real Image
Questions
Second Order Derivative
Taught by
UCF CRCV