NDLI: Extraction of Structural Shape of Low DOF Image using Morphological Operators

Content Provider	IEEE Xplore Digital Library
Author	Santhi, N. Seldev Christopher Ramar, K. Arun Prem Santh, J.
Copyright Year	2007
Description	Author affiliation: NI Coll. of Eng., Kumaracoil (Santhi, N.)
Abstract	Automatic image segmentation and shape extraction is one of the most challenging problems in computer vision. This paper 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 paper, 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 skeleton 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.
Starting Page	523
Ending Page	527
File Size	6290419
Page Count	5
File Format	PDF
ISBN	1424409969
DOI	10.1109/ICSCN.2007.350656
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-02-22
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Discrete transforms Image segmentation Computer vision Filters Focusing Skeleton Partitioning algorithms Structural shapes Pixel Higher order statistics
Content Type	Text
Resource Type	Article

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