Biologically inspired model for crater detection

Mu, Y; Ding, W; Tao, D; Stepinski, TF

Biologically inspired model for crater detection

Mu, Y Ding, W Tao, D

Stepinski, TF

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2011, pp. 2487 - 2494
Issue Date:: 2011-10-24

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Field	Value	Language
dc.contributor.author	Mu, Y	en_US
dc.contributor.author	Ding, W	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Stepinski, TF	en_US
dc.date.issued	2011-10-24	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2011, pp. 2487 - 2494	en_US
dc.identifier.isbn	9781457710865	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29390
dc.description.abstract	Crater detection from panchromatic images has its unique challenges when comparing to the traditional object detection tasks. Craters are numerous, have large range of sizes and textures, and they continuously merge into image backgrounds. Using traditional feature construction methods to describe craters cannot well embody the diversified characteristics of craters. On the other hand, we are gradually revealing the secret of object recognition in the primate's visual cortex. Biologically inspired features, designed to mimic the human cortex, have achieved great performance on object detection problem. Therefore, it is time to reconsider crater detection by using biologically inspired features. In this paper, we represent crater images by utilizing the C1 units, which correspond to complex cells in the visual cortex, and pool over the S1 units by using a maximum operation to reserve only the maximum response of each local area of the S1 units. The features generated from the C1 units have the hallmarks of size invariance and location invariance. We further extract a set of improved Haar features on each C1 map which contain gradient texture information. We apply this biologically inspired based Haar feature to crater detection. Because the feature construction process requires a set of biologically inspired transformations, these features are embedded in a high dimension space. We apply a subspace learning algorithm to find the intrinsic discriminative subspace for accurate classification. Experiments on Mars impact crater dataset show the superiority of the proposed method. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2011.6033542	en_US
dc.title	Biologically inspired model for crater detection	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	San Jose	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Crater detection from panchromatic images has its unique challenges when comparing to the traditional object detection tasks. Craters are numerous, have large range of sizes and textures, and they continuously merge into image backgrounds. Using traditional feature construction methods to describe craters cannot well embody the diversified characteristics of craters. On the other hand, we are gradually revealing the secret of object recognition in the primate's visual cortex. Biologically inspired features, designed to mimic the human cortex, have achieved great performance on object detection problem. Therefore, it is time to reconsider crater detection by using biologically inspired features. In this paper, we represent crater images by utilizing the C1 units, which correspond to complex cells in the visual cortex, and pool over the S1 units by using a maximum operation to reserve only the maximum response of each local area of the S1 units. The features generated from the C1 units have the hallmarks of size invariance and location invariance. We further extract a set of improved Haar features on each C1 map which contain gradient texture information. We apply this biologically inspired based Haar feature to crater detection. Because the feature construction process requires a set of biologically inspired transformations, these features are embedded in a high dimension space. We apply a subspace learning algorithm to find the intrinsic discriminative subspace for accurate classification. Experiments on Mars impact crater dataset show the superiority of the proposed method. © 2011 IEEE.

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