Cany Edge Detetcor
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Data Scientist
Data Visualizer
Data Analyst
Jupyter
Python
TensorFlow
TABLE OF CONTENTS
1. Introduction
- Problem Statement
- Overview of Canny Edge Detection
- Objectives of the Implementation
2. Grayscale Conversion
- Importance of Grayscale Conversion in Edge Detection
- Methodology for Converting RGB to Grayscale
- Code Implementation
3. Gaussian Blur
- Role of Gaussian Blur in Noise Reduction
- Explanation of Gaussian Kernel
- Manual Implementation of Gaussian Kernel
- Convolution Process
- Code Implementation
4. Gradient Calculation
- Importance of Gradient Calculation
- Explanation of Intensity Gradients
- Calculation of Gradient Magnitude and Direction
- Use of Sobel Filters for Gradient Calculation
- Code Implementation
5. Non-Maximum Suppression
- Purpose of Non-Maximum Suppression
- Methodology for Thinning Edges
- Suppressing Non-Maximum Pixels
- Code Implementation
6. Double Thresholding
- Role of Thresholding in Edge Detection
- Explanation of Strong, Weak, and Non-Relevant Pixels
- Selection of High and Low Threshold Values
- Code Implementation
7. Edge Tracking by Hysteresis
- Importance of Edge Tracking
- Process of Connecting Weak Edges to Strong Edges
- Suppression of Unconnected Weak Edges
- Code Implementation
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Posted Aug 3, 2024
The "Canny Edge Detector" project focuses on implementing and evaluating the Canny edge detection algorithm, a popular technique in computer vision for identify
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Clients
GIFT
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Data Scientist
Data Visualizer
Data Analyst
Jupyter
Python
TensorFlow
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