Performance Analysis of Area Morphology Operator in Color Image Processing.

Date of Submission

December 2002

Date of Award

Winter 12-12-2003

Institute Name (Publisher)

Indian Statistical Institute

Document Type

Master's Dissertation

Degree Name

Master of Technology

Subject Name

Computer Science


Electronics and Communication Sciences Unit (ECSU-Kolkata)


Chanda, Bhabatosh (ECSU-Kolkata; ISI)

Abstract (Summary of the Work)

The ficld of mathematical morphology contributes a wide range of uperators to image prucessing, all based around a few simple mathematical concepts Irum set theory. The operators are particularly useful for the analysis of images and common usuges include cdge detection, noise removal, image enhancenient and image segmentation.The concept of digital morphology is based on the fact that images consist of set of picture elements called pixels that are collected into groups having a two- dimensional structure called shape. A group of mathematical operators can be applied to the set of pixels to enhance or highlight specific aspects of the shape so that they can be recognized.Erosion or shrinking of set of pixels having a given pattern (structuring element) and dilation or expanding of a given pattern to, are basic morphology operators. Virtually, all other mathematical morphology operators can be defined [10] in terms of combinations of erosion and dilation along with set operators such as intersection and union. Some of the more important are opening (application of erosion immediately followed by a dilation using the same structuring element) and closing (application of dilation immediately followed by a erosion using the same structuring element ).The idea underneath the muthematical morphology operators is extended in [2] to give rise area morphology operators - viz, area opening and area closing. The composition of area opening with it's dual i.e. area closing, is defined as area open_elose. It is a filter by reconstruction which is size dependent in the sense that it removes image components that are smaller in area than a given limit. The study reported in [1] revealed that filters by reconstruction belong to a larger elass called connected operators that have the fundamental property that these operators do not remove some frequency components like linear filters or some shapes like median filters or standard morphological opening and closing. Now-a-days, they are becoming very popular because they have been claimed to simplify the image while preserving contours. This rather surprising property makes them very attractive for a large number of applications such as noise cancellation, smoothing and segmentation.The present study is circled around one such filter by reconstruction, viz area open_close and a systematic (experiment based) approach has been taken to establish the benefits of such a spatial filtering algorithm over others with respect to parameters like noise cancellation, smoothing and segmentation.


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Creative Commons License

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


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