TY  - JOUR
T1  - Extraction of Structural Shape of Low DOF Image Using Morphological Operators
AU - , N. Santhi AU - , Seldev Christopher AU - , K. Ramar AU - , J. Arun Prem Santh 
JO  - Asian Journal of Information Technology
VL  - 6
IS  - 3
SP  - 303
EP  - 308
PY  - 2007
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2007.303.308
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2007.303.308
KW  - Extraction
KW  -structural shape
KW  -DOF
KW  -morphological
KW  -HOS
KW  -algorithm
AB  - Automatic image segmentation and shape extraction is one of the most Challenging problems in computer vision.This study presents a novel algorithm to partition an image with low Depth-of-Field (DOF) into focused Object-of-Interest (OOI) and extracts the structural shape components using a generalized discrete morphological skeleton transform.The proposed segmentation algorithm unfolds into three steps. In the first step, we transform the low DOF image into an appropriate feature space, in which the spatial distribution of the high-frequency components is represented. This is conducted by computing Higher Order Statistics (HOS) for all pixels in the low-DOF image. Next, the obtained feature space, which is called HOS map in this study, is simplified by removing small dark holes and bright patches using a morphological filter by reconstruction. Finally, the OOI is extracted by applying region merging to the simplified image and by thresholding. Unlike the previous methods that rely on sharp details of OOI only, the proposed algorithm complements the limitation of them by using morphological filters, which also allows perfect preservation of the contour information. For the morphological shape representation algorithms, a generalized discrete morphological skelton transform is used which uses eight structuring elements to generate skeleton subsets will be adjacent to each other. Each skeletal point will represent a shape part that is in general an octagon with four pairs of parallel opposing sides. The number of representative points needed to represent a given shape is significantly lower than that in the standard skeleton transform. A collection of shape components needed to build a structural representation is easily derived from the generalized skeleton transform. Each shape component covers a significant area of the given shape and severe overlapping is avoided. The given shape can also be accurately approximated using a small number of shape components.
ER  -