DocumentCode :
1418642
Title :
A New Design Tool for Feature Extraction in Noisy Images Based on Grayscale Hit-or-Miss Transforms
Author :
Murray, P. ; Marshall, S.
Author_Institution :
Dept. of Electron. & Electr. Eng., Univ. of Strathclyde, Glasgow, UK
Volume :
20
Issue :
7
fYear :
2011
fDate :
7/1/2011 12:00:00 AM
Firstpage :
1938
Lastpage :
1948
Abstract :
The hit-or-miss transform (HMT) is a well-known morphological transform capable of identifying features in digital images. When image features contain noise, texture, or some other distortion, the HMT may fail. Various researchers have extended the HMT in different ways to make it more robust to noise. The most successful, and most recent extensions of the HMT for noise robustness, use rank-order operators in place of standard morphological erosions and dilations. A major issue with the proposed methods is that no technique is provided for calculating the parameters that are introduced to generalize the HMT, and, in most cases, these parameters are determined empirically. We present here, a new conceptual interpretation of the HMT which uses a percentage occupancy (PO) function to implement the erosion and dilation operators in a single pass of the image. Further, we present a novel design tool, derived from this PO function that can be used to determine the only parameter for our routine and for other generalizations of the HMT proposed in the literature. We demonstrate the power of our technique using a set of very noisy images and draw a comparison between our method and the most recent extensions of the HMT.
Keywords :
computer vision; feature extraction; image segmentation; mathematical morphology; object recognition; transforms; digital images; feature extraction; grayscale hit-or-miss transforms; image features; morphological dilations; morphological erosions; noisy images; percentage occupancy function; well-known morphological transform; Feature extraction; Gray-scale; Noise; Noise measurement; Pixel; Robustness; Transforms; Machine vision; morphological operations; object recognition; segmentation;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2010.2103952
Filename :
5680651
Link To Document :
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