Mike Rahilly’s research while affiliated with The Commonwealth Scientific and Industrial Research Organisation and other places

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Publications (3)


Processing by SVM of Haar Wavelet Transforms for Discontinuity Detection
  • Article

April 2012

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54 Reads

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2 Citations

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Mike Rahilly

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This paper describes a new approach to discontinuity detection involving the processing by support vector machine (SVM) of Haar wavelet transforms of local image windows. The method labels each pixel in an image as a pixel of discontinuity or otherwise by processing the Haar wavelet transform of the image in a window about the pixel using an SVM. The pixel labeler is trained using software for labeling training images by a human operator. As a pixel classifier, to within a moderate morphological tolerance, the discontinuity detector has an accuracy of 99% on the images for which it has been tested.


An Approach Using Mathematical Morphology and Support Vector Machines to Detect Features in Pipe Images

January 2008

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14 Reads

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4 Citations

This paper presents a new approach to detecting features in pipe images based on a generalisation of the erosion operation. The pipe images can be segmented using support vector machine or other method. The binary image obtained in this way contains a principal connected component made up from the pipe flow lines, the pipe joints and adjoining defects. The morphological analysis allows the principal component of the segmented image to be decomposed into its components. Generalisations of the dilation and erosion operations called alpha-dilation and alpha-erosion are defined. Some simple properties of these operations are derived.


Pixel-Based Colour Image Segmentation Using Support Vector Machine for Automatic Pipe Inspection

December 2007

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37 Reads

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16 Citations

This paper presents a new approach to image segmentation of colour images for automatic pipe inspection. Pixel-based segmentation of colour images is carried out by a support vector machine (SVM) labelling pixels on the basis of local features. Segmentation can be effected by this pixel labelling together with connected component labelling. The method has been tested using RGB, HSB, Gabor, local window and HS feature sets and is seen to work best with the HSB feature set.

Citations (3)


... In the approach for edge detection in this article (see Mashford et al. 2011), a binary edge image is generated by applying a pixel classifier to every pixel of the input image. The pixel classifier operates by means of an SVM acting on the 192 components of the DHT of the input image restricted to an 8 × 8 window about the pixel under consideration. ...

Reference:

Edge Detection in Pipe Images Using Classification of Haar Wavelet Transforms
Processing by SVM of Haar Wavelet Transforms for Discontinuity Detection
  • Citing Article
  • April 2012

... Acoustic detection method judged the sound mainly from the pipeline; however, electromagnetic induction probably interpreted the signal. What is more, the acoustic sounding method and high-precision acoustic amplifier can judge the water flow in the pipeline [51][52][53][54], which is worth further exploration. As the water supply system standard and the measures of water saving and the development stages vary between countries, the water consume estimation method should be adjusted according local conditions, and its accuracy needs to be further explored [38,40]. ...

An Approach Using Mathematical Morphology and Support Vector Machines to Detect Features in Pipe Images
  • Citing Conference Paper
  • January 2008

... Anitescu et al. [3] proposed an algorithm based on an artificial neural network(ANN) to solve partial differential equations (PDEs), which can result in significant computational savings. Mashford et al. [38] proposed a method for automatic detection and segmentation of pipeline color images based on SVM by combining local feature markers with connected component markers. Sinha et al. [54] proposed a neuro-fuzzy classifier combining neural network and fuzzy logic. ...

Pixel-Based Colour Image Segmentation Using Support Vector Machine for Automatic Pipe Inspection
  • Citing Conference Paper
  • December 2007