Image segmentation for surface material-type classification using 3D geometry information

To, Andrew Wing Keung; Paul, Gavin; Liu, Dikai

doi:10.1109/icinfa.2010.5512230

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The GLCM as a model of the probability density function will be extended to three-dimensional image regions without further restrictions [7,8]. For this purpose, a new definition of the offset vector for 3D domain δ=(δ i ,δ j ,δ s ) will be introduced where s represents each slice.…”

Section: Glcm For 3d Datamentioning

confidence: 99%

A statistical description of 3D lung texture from CT data

Chaisaowong

Paul

2015

SPIE Proceedings

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A method was described to create a statistical description of 3D lung texture from CT data. The second order statistics, i.e. the gray level co-occurrence matrix (GLCM), has been applied to characterize texture of lung by defining the joint probability distribution of pixel pairs. The required GLCM was extended to three-dimensional image regions to deal with CT volume data. For a fine-scale lung segmentation, both the 3D GLCM of lung and thorax without lung are required. Once the co-occurrence densities are measured, the 3D models of the joint probability density function for each describing direction of involving voxel pairs and for each class (lung or thorax) are estimated using mixture of Gaussians through the expectation-maximization algorithm. This leads to a feature space that describes the 3D lung texture.

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Section: Glcm For 3d Datamentioning

confidence: 99%

A statistical description of 3D lung texture from CT data

Chaisaowong

Paul

2015

SPIE Proceedings

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“…For a bridge maintenance robot manipulator (as shown in Fig. 1) tasked to inspect for surface rust and grit-blasting finish quality [1] [2] [3], the use of a machine vision system may require the additional setup of lighting infrastructure to operate successfully. Lighting infrastructure can either be mounted directly onto the robot manipulator or placed in carefully selected locations within the environment.…”

Section: Introductionmentioning

confidence: 99%

Surface-Type Classification Using RGB-D

Paul

Liu

2014

IEEE Trans. Automat. Sci. Eng.

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This paper proposes an approach to improve surface-type classification of images containing inconsistently illuminated surfaces. When a mobile inspection robot is visually inspecting surface-types in a dark environment and a directional light source is used to illuminate the surfaces, the images captured may exhibit illumination variance that can be caused by the orientation and distance of the light source relative to the surfaces. In order to accurately classify the surface-types in these images, either the training image dataset needs to completely incorporate the illumination variance or a way to extract colour features that can provide high classification accuracy needs to be identified. In this paper diffused reflectance values are extracted as new colour features to classifying surface-types. In this approach, RGB-D data is collected from the environment, and a reflectance model is used to calculate a diffused reflectance value for a pixel in each Red, Green, Blue (RGB) colour channel. The diffused reflectance values can be used to train a multi-class support vector machine classifier to classify surface-types. Experiments are conducted in a mock bridge maintenance environment using a portable RGB-Depth (RGB-D) sensor package with an attached light source to collect surface-type data. The performance of a classifier trained with diffused reflectance values is compared against classifiers trained with other colour features including RGB and L*a*b* colour spaces. Results show that the classifier trained with the diffused reflectance values can achieve consistently higher classification accuracy than the classifiers trained with RGB and L*a*b* features. For test images containing a single surface plane, diffused reflectance values consistently provide greater than 90% classification accuracy; and for test images containing a complex scene with multiple surface-types and surface planes, diffused reflectance values are shown to provide an increase in overall accuracy over RGB and L*a*b* by 49.24% and 13.66%, respectively. Note to Practitioners: This paper was motivated by the problem of inspecting inconsistently illuminated steel surfaces on a bridge structure using a robot manipulator. Existing approaches for colour-based surface classification are susceptible to illumination variance. This paper proposes the use of diffused reflectance values, which combines the use of colour and depth data to improve accuracy. In this approach, the diffused reflectance values of each image pixel are calculated by using the distance and angle between the surface represented by a pixel and the light source. The diffused reflectance values are calculated in each colour channel (Red, Green, Blue) to provide three features to classify different surface-types. This proposed approach can be applied to surface classification tasks where the light source does not uniformly illuminate the scene in the image.

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Image segmentation for surface material-type classification using 3D geometry information

Cited by 2 publications

References 13 publications

A statistical description of 3D lung texture from CT data

A statistical description of 3D lung texture from CT data

Surface-Type Classification Using RGB-D

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