Edge detection is a research field within Image processing and Computer vision, in particular within the area of feature extraction. It is extensively used in image segmentation when we want to divide the image into areas corresponding to different objects. Representing an image by its edges has the further advantage that the amount of data is reduced significantly while retaining most of the image information.
Since edges consist of mainly high frequencies, we can, in theory, detect edges by applying a high pass frequency filter in the Fourier domain or by convolving the image with an appropriate kernel in the spatial domain. In practice, edge detection is performed in the spatial domain, because it is computationally less expensive and often yields better results. Since edges correspond to strong illumination gradients, we can highlight them by calculating the derivatives of the image.
The present Thesis aims at extracting a good & accurate edge detected image from the application of various masks or edge detection operators on the image.Convolution is the mathematical tool, that is used to implement the various masks operators to get an edge detected image from the original image. Our thesis provides the implementation of the following edge detection techniques to get a better edge detected image: a) 1 Dimensional operators : Kirch ,Prewitt, Sobel and Quick Masking; b) 2 Dimensional operators: LOG( Laplacian of Guassian) and DOG( difference of Guassian).
Inhaltsverzeichnis (Table of Contents)
- ABSTRACT
- LIST OF FIGURES
- LIST OF EQUATIONS
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This thesis investigates the application of various edge detection operators and their implementation for enhancing image edge detection accuracy. The primary goal is to extract a precise and well-defined edge detected image from the original image by applying convolution techniques with different masks or edge detection operators.
- Edge Detection Techniques
- Image Processing and Computer Vision
- Feature Extraction
- Image Segmentation
- Convolution Operations
Zusammenfassung der Kapitel (Chapter Summaries)
The thesis provides a detailed exploration of edge detection methods, including both 1-dimensional and 2-dimensional operators. It covers the theoretical background of edge detection, highlighting the importance of high-frequency filtering and derivative calculations. The thesis then delves into the implementation of specific edge detection techniques, including:
- 1-Dimensional operators: Kirsch, Prewitt, Sobel, and Quick Masking
- 2-Dimensional operators: LOG (Laplacian of Gaussian) and DOG (Difference of Gaussian)
The thesis concludes by presenting a comprehensive analysis of the performance of these methods, comparing their effectiveness in extracting accurate edges from various test images.
Schlüsselwörter (Keywords)
Edge detection, image processing, computer vision, feature extraction, image segmentation, convolution, Kirsch, Prewitt, Sobel, Quick Masking, LOG, DOG, Laplacian of Gaussian, Difference of Gaussian, high pass filter, derivative calculation, image analysis.
Frequently Asked Questions
What is edge detection in image processing?
Edge detection is a feature extraction technique used to identify points in a digital image where the image brightness changes sharply, typically used for image segmentation.
Why is edge detection performed in the spatial domain?
It is computationally less expensive than frequency domain filtering and often provides more accurate results for highlighting illumination gradients.
What are 1-Dimensional edge detection operators?
The thesis implements several 1D operators, including Kirsch, Prewitt, Sobel, and Quick Masking techniques.
What is the difference between LOG and DOG operators?
LOG stands for Laplacian of Gaussian, while DOG stands for Difference of Gaussian; both are 2D operators used to detect edges by identifying zero-crossings in the second derivative.
How is convolution used in edge detection?
Convolution is the mathematical tool used to apply specific masks or kernels to an image to calculate derivatives and highlight edges.
- Citar trabajo
- Ashima Kalra (Autor), 2008, Implementation and Performance Study of Edge Detection of Images, Múnich, GRIN Verlag, https://www.grin.com/document/496900
