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Edge detection in untextured and textured images-a common computational framework
1Dept. of Comput. Sci. & Eng., Indian Inst. of Technol., Kharagpur.
Summary
A new computational framework unifies edge detection for textured and untextured 2-D images using configurable difference operators. This method effectively separates edge, texture, and noise responses for improved image analysis.
Area of Science:
- Computer Vision
- Image Processing
- Computational Mathematics
Background:
- Edge detection is crucial for image analysis, but existing methods often struggle with textured regions.
- Distinguishing between edges and textures, as well as noise, remains a significant challenge in image processing.
Purpose of the Study:
- To present a unified computational framework for edge detection in both untextured and textured 2-D images.
- To develop configurable difference operators derived from orthogonal polynomials that can represent existing edge detectors.
- To introduce novel methods for statistically analyzing texture responses and creating texture descriptors for improved edge detection.
Main Methods:
- Developed a framework using a complete set of configurable difference operators based on orthogonal polynomials.
- Represented established edge operators (Roberts, Sobel, Prewitt, Marr's LOG) within the new framework.
- Proposed a two-stage approach for textured edge detection involving statistical significance testing and texture descriptors ('pronum', 'prospectrum').
- Utilized signal-to-noise ratio (SNR) maximization and zero crossings of second directional derivatives for untextured edge detection.
Main Results:
- The proposed operators successfully separate responses for edges, textures, and noise.
- Demonstrated the ability to represent widely known edge operators using the configurable set.
- Introduced statistically-based texture descriptors ('pronum', 'prospectrum') for enhanced detection in textured images.
- Achieved encouraging results in detecting both untextured and textured edges.
Conclusions:
- The unified computational framework offers a versatile approach to edge detection across different image types.
- The novel texture analysis and descriptor generation significantly improve the detection of edges in textured images.
- The framework's ability to configure and represent existing operators suggests broad applicability and potential for further development.