Authors:
Geoff Bull
;
Junbin Gao
and
Michael Antolovich
Affiliation:
Charles Sturt University, Australia
Keyword(s):
Compressed Sensing, Random Projections, Sparse Representation, Image Patches, Feature Extraction, Image Segmentation, Classification.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
Monitoring of rock fragmentation is a commercially important problem for the mining industry. Existing
analysis methods either resort to physically sieving rock samples, or using image analysis software. The
currently available software systems for this problem typically work with 2D images and often require a
significant amount of time by skilled human operators, particularly to accurately delineate rock fragments.
Recent research into 3D image processing promises to overcome many of the issues with analysis of 2D images
of rock fragments. However, for many mines it is not feasible to replace their existing image collection systems
and there is still a need to improve on methods used for analysing 2D images. This paper proposes a method
for delineation of rock fragments using compressed Haar-like features extracted from small image patches,
with classification by a support vector machine. The optimum size of image patches and the numbers of
compressed features have been determined e
mpirically. Delineation results for images of rocks were superior
to those obtained using the watershed algorithm with manually assigned markers. Using compressed features
is demonstrated to improve the computational efficiently such that a machine learning solution is viable.
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