Edge Detection Methodologies for Color Image.

Date of Submission

December 2011

Date of Award

Winter 12-12-2012

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science

Department

Machine Intelligence Unit (MIU-Kolkata)

Supervisor

Murthy, C. A. (MIU-Kolkata; ISI)

Abstract (Summary of the Work)

1.1 Edge DetectionAN EDGE is characterized by an abrupt change in intensity indicating the boundary between two regions in an image. However, there is no universally accepted mathematical definition of an edge. It is a local property of an individual pixel and is calculated from the image function in a neighborhood of the pixel. Edge detection is a fundamental operation in computer vision and image processing. It concerns the detection of significant variations of a gray level image. The output of this operation is mainly used in higher-level visual processing like three-dimensional (3-D) reconstruction, stereo motion analysis, recognition, scene segmentation, image compression, etc. Hence, it is important for a detector to be efficient and reliable. In edge-detection, the input is an image (gray-scale or color) and the output is a binary image with the edge pixels and the non edge pixels.For edge detection, different approaches have been followed, such as mathematical morphology, Markov random fields, surface models, or PDE. Surface fitting approach for edge detection has been adopted by several authors. Bergholm’s edge detector applies a concept of edge focusing to find significant edges. Detectors based on some optimality criteria are also developed. Statistical procedures are also adopted. Other approaches on edge detection include the use of genetic algorithms neural networks, the Bayesian approach, and residual analysis-based techniques. The most common method is still the derivative approach with linear filtering. Many derivative filters have been studied and used to compute the intensity gradient of gray-level images : Roberts, Sobel, or Prewitt operators, finite impulse response filters with a large kernel, such as Canny’s filters, first derivative of the Gaussian function. Some well-known edge detectors for gray-scale images are the Marr and Hildreth edge detector, Canny edge detector, Demigny edge detection filter and Paplinski edge detection filter.Color images provide more information than gray scale images. Thus more edge information is expected from a color edge detector than a gray scale edge detector. Sometimes it is difficult to detect a low intensity edge between two regions in gray scale, but in color image, the clarity is more because, without being much different in intensity there can be a substantial difference in hue.However, it becomes more challenging when color images are considered because of its multidimensional nature. Also the gray values are partially ordered in gray scale images but in a color image this freedom is not there. One of the earliest color edge detectors is proposed by Navatia. Other color image edge detectors are due to Huckel, DiZenzo, Cumani and others.1.2 Outline of the ReportThe document primarily consists of 5 chapters. In Chapter 1 we discuss the definition of an edge, and hence define edge detection. Then we go on to discuss the basic principles of edge detection. We conclude the chapter by mentioning the names of several edge detectors for gray scale and color images. In Chapter 2 we discuss some well-known algorithms for edge detection in gray scale images. We discuss the Canny framework followed and the Demigny 1-D filter and Paplinski's n-directional filtering. The omnidirectional edge detector for gray level images is introduced next. We build the foundation for describing our work and the motivation to extend omnidirectional edge detectors to color images. In Chapter 3 we state the problem under consideration. We discuss methods to find the gradient magnitude and gradient direction in a color image. We discuss the intuition for our proposed method followed by its description and the proposed algorithm. In Chapter 4 we show the results of our experiments on different types of images using different types of filters. We use natural as well as artificially generated images for our purpose. The report ends with Chapter 5 citing the references.

Comments

ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28843163

Control Number

ISI-DISS-2011-268

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

DOI

http://dspace.isical.ac.in:8080/jspui/handle/10263/6423

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