Shape analysis of decisive objects from an image using mathematical morphology

An image is comprised of focused and non-focused objects and focused objects are treated as decisive objects. Primary task of image registration process is the associated shape analysis of the images under consideration. Shape analysis of the image is the identification of outer and inner border line characteristic of the decisive objects along with other characteristic features of the images. In this work we have proposed a scheme to analyze the outer shape of a decisive object in terms of optimum number of external boundary points. The external boundary points are identified using cross sectional views of the contour and skeleton of the objects under consideration. Further, the extreme points are labeled according to their angular representation for identification of the outer shape of the decisive objects of the image. Here, we have used the principle of Mathematical Morphology for contour as well as skeleton identifications.

[1]  Gisela Klette Skeletons in Digital Image Processing , 2002 .

[2]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[3]  HARRY BLUM,et al.  Shape description using weighted symmetric axis features , 1978, Pattern Recognit..

[4]  David A. Clausi,et al.  Morphological skeleton algorithm for PDP production line inspection , 2001, Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555).

[5]  Serge Beucher Digital skeletons in Euclidean and geodesic spaces , 1994, Signal Process..

[6]  E. R. Davies Computer and Machine Vision: Theory, Algorithms, Practicalities , 2012 .

[7]  Qing Liu,et al.  Edge detection based on mathematical morphology theory , 2011, 2011 International Conference on Image Analysis and Signal Processing.

[8]  Ian T. Young,et al.  An Analysis Technique for Biological Shape. I , 1974, Inf. Control..

[9]  Ralph Roskies,et al.  Fourier Descriptors for Plane Closed Curves , 1972, IEEE Transactions on Computers.

[10]  Joe-Air Jiang,et al.  Mathematical-morphology-based edge detectors for detection of thin edges in low-contrast regions , 2007 .

[11]  Somasis Roy,et al.  Morphologically contour extraction of decisive objects from image , 2014, 2014 First International Conference on Automation, Control, Energy and Systems (ACES).

[12]  Sven Loncaric,et al.  A survey of shape analysis techniques , 1998, Pattern Recognit..

[13]  Xiaoping Li,et al.  Object Detection Based on Feature Extraction and Morphological Operations , 2005, 2005 International Conference on Neural Networks and Brain.

[14]  Tinku Acharya,et al.  Image Processing: Principles and Applications , 2005, J. Electronic Imaging.

[15]  X. Bai,et al.  An Efficient Quick Algorithm for Computing Stable Skeletons , 2009, 2009 2nd International Congress on Image and Signal Processing.

[16]  Aijun Chen,et al.  Circular object detection with mathematical morphology and geometric properties , 2010, 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering.

[17]  Gösta H. Granlund,et al.  Fourier Preprocessing for Hand Print Character Recognition , 1972, IEEE Transactions on Computers.

[18]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[19]  Frank Y. Shih Object representation and recognition using mathematical morphology model , 1991, J. Syst. Integr..