Implementing Canny Edge Detection Algorithm for Different Blurred and Noisy Images
Also Available Domains Communications|Testing
Objective
To implement the Canny edge detection algorithm for accurately identifying edges in blurred and noisy images. To enhance image preprocessing by improving edge localization and noise robustness for reliable computer vision applications.
Abstract
In the field of image processing, edge detection of visual data is one of the most important operations of image processing research. This operation is very useful in many applications like object recognition, feature detection of image, motion analysis in computer vision field, etc. Canny detector is an algorithm of these operations of edge detection which constitutes more information about object to other edge detection algorithms. This work consists of a comparison study of image edge detection methods by focusing on the Canny algorithm to present the efficiency and perfection of this methods compared to other. Moreover an improved execution of this methods for blurred and noisy images.
Keywords: Image, Blur, Noise, Restoration, Edge Detection, Canny Detector, Features of image
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software Requirements
Xilinx ISE Tool/Xilinx Vivado
· HDL: Verilog
Learning Outcomes
· Basics of Digital Electronics.
· Introduction to Verilog Coding.
· Xilinx Vivado for design and simulation.
· Learn how to extract parameters.
· Understanding of Finite State Machines (FSM).
· Knowledge of Cellular Automata (CA).
· Experience with UART and Putty.
· Application of IP Protection Techniques.
· Development of Real-World Skills.
Related Projects
Paper Publishing
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