Using noise models to estimate rank parameters for rank order greyscale hit-or-miss transforms

Research output: Contribution to conferencePaper

Abstract

The Hit-or-Miss Transform (HMT) is a morphological transform which can be used for template matching and other applications. Recent developments of the HMT include extensions of the transform for application to greyscale images as well as a variety of techniques aiming to improve its noise robustness. One popular technique for improving noise robustness is to use rank order filters in place of the traditional morphological operations of erosion and dilation. However, very few authors give consideration to developing generic techniques for estimating the rank parameters they introduce. Very recently, techniques which use ROC curves, or the SEs designed for object detection, have been presented for estimating optimal values for the rank parameter. This paper presents a new, simpler technique which uses noise models extracted from the image set under study to estimate the optimal rank parameter.

Conference

Conference6th International Symposium on Communications, Control and Signal Processing
CountryGreece
CityAthens
Period21/05/1423/05/14

Fingerprint

Mathematical transformations
Template matching
Erosion
Object detection

Keywords

  • hit-or-miss transform
  • greyscale morphology
  • noisy image processing
  • rank order filters

Cite this

Murray, P., & Marshall, S. (2014). Using noise models to estimate rank parameters for rank order greyscale hit-or-miss transforms. Paper presented at 6th International Symposium on Communications, Control and Signal Processing, Athens, Greece.
Murray, Paul ; Marshall, Stephen. / Using noise models to estimate rank parameters for rank order greyscale hit-or-miss transforms. Paper presented at 6th International Symposium on Communications, Control and Signal Processing, Athens, Greece.
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abstract = "The Hit-or-Miss Transform (HMT) is a morphological transform which can be used for template matching and other applications. Recent developments of the HMT include extensions of the transform for application to greyscale images as well as a variety of techniques aiming to improve its noise robustness. One popular technique for improving noise robustness is to use rank order filters in place of the traditional morphological operations of erosion and dilation. However, very few authors give consideration to developing generic techniques for estimating the rank parameters they introduce. Very recently, techniques which use ROC curves, or the SEs designed for object detection, have been presented for estimating optimal values for the rank parameter. This paper presents a new, simpler technique which uses noise models extracted from the image set under study to estimate the optimal rank parameter.",
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author = "Paul Murray and Stephen Marshall",
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Murray, P & Marshall, S 2014, 'Using noise models to estimate rank parameters for rank order greyscale hit-or-miss transforms' Paper presented at 6th International Symposium on Communications, Control and Signal Processing, Athens, Greece, 21/05/14 - 23/05/14, .

Using noise models to estimate rank parameters for rank order greyscale hit-or-miss transforms. / Murray, Paul; Marshall, Stephen.

2014. Paper presented at 6th International Symposium on Communications, Control and Signal Processing, Athens, Greece.

Research output: Contribution to conferencePaper

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AU - Marshall, Stephen

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N2 - The Hit-or-Miss Transform (HMT) is a morphological transform which can be used for template matching and other applications. Recent developments of the HMT include extensions of the transform for application to greyscale images as well as a variety of techniques aiming to improve its noise robustness. One popular technique for improving noise robustness is to use rank order filters in place of the traditional morphological operations of erosion and dilation. However, very few authors give consideration to developing generic techniques for estimating the rank parameters they introduce. Very recently, techniques which use ROC curves, or the SEs designed for object detection, have been presented for estimating optimal values for the rank parameter. This paper presents a new, simpler technique which uses noise models extracted from the image set under study to estimate the optimal rank parameter.

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KW - greyscale morphology

KW - noisy image processing

KW - rank order filters

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Murray P, Marshall S. Using noise models to estimate rank parameters for rank order greyscale hit-or-miss transforms. 2014. Paper presented at 6th International Symposium on Communications, Control and Signal Processing, Athens, Greece.