A Real-Time Image Segmentation System Using a Connectionist Classifier Architecture

Blanz, W. E.; Gish, Sheri L.

doi:10.1142/9789812814890_0006

Cited by 6 publications

(4 citation statements)

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“…The method of segmentation used in the PIRAT system involved feature extraction followed by classification of the feature vector [10]. In the PIRAT project differential geometric features were used and the classifier was the nearest neighbour classifier.…”

Section: The Segmentation Sub-systemmentioning

confidence: 99%

A morphological approach to pipe image interpretation based on segmentation by support vector machine

Mashford

Rahilly

Davis

et al. 2010

Automation in Construction

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Section: The Segmentation Sub-systemmentioning

confidence: 99%

A morphological approach to pipe image interpretation based on segmentation by support vector machine

Mashford

Rahilly

Davis

et al. 2010

Automation in Construction

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“…The main segmentation approach that we consider in this paper is described in [17]. It is a supervised method involving classification of feature vectors derived from pixels or neighbourhoods of pixels [18].…”

Section: The Segmentation Subsystemmentioning

confidence: 99%

An Approach to Pipe Image Interpretation Based Condition Assessment for Automatic Pipe Inspection

Mashford

Marlow

Burn

2009

Advances in Civil Engineering

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Condition assessment forms an important part of the asset management of buried pipelines. This is carried out through the use of inspection systems which usually consist of an image acquisition device attached to a mobile robotic platform. Complete or partial automation of image interpretation could increase the efficiency and objectivity of pipe inspection. A key component of an automatic pipe inspection system is the segmentation module. This paper describes an approach to automatic pipe inspection using pixel-based segmentation of colour images by support vector machine (SVM) coupled with morphological analysis of the principal component of the segmented image. The morphological analysis allows the principal component of the segmented image to be decomposed into the pipe flow lines region, the pipe joints, and adjoining defects. A simple approach to detecting pipe connections using fuzzy membership functions relating to defect size and location is also described.

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“…We have tried a number of approaches to pipe image segmentation including thresholding and clustering and found that pixel-based segmentation by SVM was the most promising (see [10,11,12]). Segmentation can be effected by a SVM, acting as a pixel classifier, classifying feature vectors derived from image values at a pixel or in a neighbourhood of the pixel.…”

Section: Segmentation By Support Vector Machinementioning

confidence: 99%

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

Mashford

Rahilly

Davis

2008

2008 Digital Image Computing: Techniques and Applications

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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 α-dilation and α-erosion are defined. Some simple properties of these operations are derived.

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A Real-Time Image Segmentation System Using a Connectionist Classifier Architecture

Cited by 6 publications

References 0 publications

A morphological approach to pipe image interpretation based on segmentation by support vector machine

A morphological approach to pipe image interpretation based on segmentation by support vector machine

An Approach to Pipe Image Interpretation Based Condition Assessment for Automatic Pipe Inspection

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

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