{"id":121,"date":"2011-01-28T18:07:33","date_gmt":"2011-01-28T18:07:33","guid":{"rendered":"http:\/\/www.macs.hw.ac.uk\/texturelab\/"},"modified":"2011-03-11T16:15:25","modified_gmt":"2011-03-11T16:15:25","slug":"reports","status":"publish","type":"page","link":"https:\/\/www.macs.hw.ac.uk\/texturelab\/publications\/reports\/","title":{"rendered":"Texturelab Edinburgh \u2013 Publications – Reports"},"content":{"rendered":"\n\n\n
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Reports and Miscellaneous Items<\/a><\/strong><\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Author<\/strong><\/td>\n\n
Description<\/strong><\/div>\n<\/td>\n
Download<\/strong><\/td>\n<\/tr>\n
Spence, A.
\nChantler, M.<\/td>\n		Apparatus and Methods for Obtaining Surface Texture Information
\n<\/strong>Patent
\nInternational Application No. PCT\/GB2005\/004241.
\nFiling Date: 03.11.2005<\/em><\/td>\n		<\/td>\n<\/tr>\n
Drbohlav, O
\nChantler, M.<\/td>\n		How do joint image statistics change with illumination?
\n<\/strong>Technical Report HW-MACS-TR-0020, UK. August 2004
\nSchool of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh.<\/em><\/p>\n

Abstract<\/strong>
\nThe dependence of statistical properties of an image as a function of illumination direction is an exciting topic which was so far investigated experimentally, and also addressed theoretically for a special case of low-order moments of image features. In this paper we observe the principal relationship between the joint (co-occurrence) distribution of surface properties and the corresponding joint distribution of local image features. We focus on two kinds of statistics computed from local image neighbourhoods: (a) the joint distribution of pixel intensities, and (b) the joint distribution of binary patterns obtained by taking the signs of intensity differences between a selected reference pixel and all other pixels. We work with a non-parametric histogram representation of the probability distribution functions, and show how the frequencies in a histogram bin depend on illumination. Finally, we focus on approximating the illumination dependence of image statistics by a harmonic series. Experimental results obtained using a real surface are presented.<\/td>\n

\n
Ged Mcgunnigle<\/td>\nMethodology of Classifying Rough Surfaces using Photometric Stereo<\/strong>
\nResearch Memorandum RM\/02\/1, September 2002, School of Mathematical & Computer Sciences<\/em><\/td>\n		<\/td>\n<\/tr>\n
Andreu Gonzalez<\/td>\nModel-based Texture Classification under Varying Illumination<\/strong><\/a>
\nResearch Memorandum RM\/02\/8, September 2002, Dept. of Computing and Electrical Engineering<\/em><\/p>\n

Abstract<\/strong>
\n<\/em>This dissertation presents a complete texture classification system to overcome the problem induced by changes in the angle of illumination incident upon a 3D surface.
\nThe system works on the basis of a surface model, previously formed by means of a photometric stereo technique. From this model, the system is able to render a 2D image of the surface at any particular illuminant direction, thus providing a more appropriate data for training the classifier.
\nMany laboratory experiments are carried out in order to assess the accuracy of image prediction as an individual component. The investigation considers a large diversity of textures, including challenging situations such as rough, specular and anisotropic surfaces. It is concluded that the predicted images, yet not being perfectly accurate, are in all cases a much more reliable training data than a merely single image from a single illuminant direction.
\nThe technique is evaluated using supervised statistical classification, which combines a bank of Gabor filters for feature extraction with a linear Bayes classifier. The classification performance is tested for different composite images, consisting of a varying number of disjoint textures and configurations. It is shown that our approach significantly reduces the misclassification rate, when compared with a naive classification system. Furthermore, in some cases it even reaches the level of accuracy that one would obtained with the proviso that training and classification were performed under invariant illumination. <\/em><\/td>\n

\n
G. McGunnigle
\nM.J. Chantler<\/td>\n		Recovery of Indented Handwriting<\/strong>
\nResearch Memorandum RM\/02\/3, February 2002, Department of Computing & Electrical, Engineering.<\/em><\/td>\n		<\/td>\n<\/tr>\n
G. McGunnigle
\nM.J. Chantler<\/td>\n		Photometric Stereo and Oil Paintings: Techniques and Applications<\/strong>
\nResearch Memorandum RM\/02\/1 April 2001, Department of Computing & Electrical Engineering.<\/em><\/td>\n		\n
Mike Chantler
\nGed McGunnigle<\/td>\n		A Simple Theory of Texture Classification that is Robust to Lighting Direction<\/strong>
\nResearch Memorandum RM\/02\/05 March 2001, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		<\/td>\n<\/tr>\n
G. McGunnigle
\nM.J. Chantler<\/td>\n		Photometric recovery of moulded fingerprints<\/strong>
\nResearch Memorandum RM\/01\/01, March 2001, Department of Computing & Electrical Engineering.<\/em><\/td>\n		\n
Andreas Penirsche<\/td>\nIlluminant Invariant Classification of 3D Surface Textures<\/strong>
\nResearch Memorandum RM\/02\/4, March 2002, Dept. of Computing and Electrical Engineering<\/em><\/p>\n

Abstract<\/strong>
\n<\/em>Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance. Such variations affect texture feature images can dramatically increase the failure rates of texture classifiers. Changes in illumination direction causes texture clusters to describe a two dimensional trigonometric function in feature space, which is dependent on illumination tilt and slant angle.<\/p>\n

Starting from a sinosodial prediction for texture features we analytically derive two different classification algorithms. First a classifier is introduced, which is robust to changes in illumination tilt direction. The classifier is tested with both, simulations and experiments.
\nFinally we introduce a classifier, which is robust to changes in illumination direction.<\/td>\n

\n
Michael Schmidt<\/td>\nThe effect of changing illuminant tilt on texture features<\/strong>
\nResearch Memorandum RM\/01\/5A, October 2001, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		\n
Susana Gutierrez<\/td>\nAn Investigation into Four Different Surface Texture Classifiers<\/strong>
\nResearch Memorandum RM\/01\/3, March 2001, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		<\/td>\n<\/tr>\n
Raul Garcia<\/td>\nAn Investigation into the Accuracy of Photometric Model-based Texture Classification<\/strong>
\nResearch Memorandum RM\/01\/2, March 2001, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		<\/td>\n<\/tr>\n
Christian Sinn<\/td>\nAn Investigation into the Importance of Phase in Textured Images<\/strong>
\nResearch Memorandum RM\/00\/10, July 2000, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		\n
Maria Penilla<\/td>\nA photometric Stereo Technique to\u00a0 Rough Surface Classification<\/strong>
\nResearch Memorandum RM\/00\/9, May 2000, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		<\/td>\n<\/tr>\n
Fred Quivy<\/td>\nA photometric Stereo Approach to Tilt-Invariant Classification of Rough Surfaces<\/strong>
\nResearch Memorandum RM\/98\/3, June 1998, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		<\/td>\n<\/tr>\n
J.M. Bell
\nM.J. Chantler
\nT. Wittig<\/td>\n		Directional characteristics of sidescan images<\/strong>
\nIEE Colloquium, 26th March 1998, Savoy Place, London<\/em><\/td>\n		\n
\"\"<\/div>\n<\/td>\n<\/tr>\n
T. Wittig<\/td>\nThe Effect of Changing the Direction of the Illuminating Acoustic Wave on Sonar Images<\/strong>
\nResearch Memorandum RM\/97\/4, May 1997, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		<\/td>\n<\/tr>\n
M. Damoisea<\/td>\nA Frequency Domain Approach to Rotation Invariant Texture Classification<\/strong>
\nResearch Memorandum, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		<\/td>\n<\/tr>\n
G. Delguste<\/td>\nA Comparison of Spatial and Frequency Domain Illuminant Vector Estimators<\/strong>
\nResearch Memorandum RM\/96\/8, June 1996, Dept. of Computing and Electrical Engineering<\/em><\/td>\n		<\/td>\n<\/tr>\n
M.J. Chantler<\/td>\n\n
Towards illuminant invariant texture classification<\/strong>
\nIEE Colloquium “Texture Classification: theory and applications”, Savoy Place, 7th October 1994<\/em><\/p>\n

Abstract<\/strong>
\n“This paper provides a short overview of work recently performed by the author into the effects of illuminant variation on texture classification, and proposes one method to reduce miss-classifications caused by such variations [Chantler94a]. The paper first presents a model of image texture originally introduced by Kube and Pentland and further developed by the author. The effect of variation in the tilt angle of the illuminant is high-lighted. A set of tilt-compensation filters is developed from the model. The filters are used to pre-process texture images used in training and classification sessions.”<\/div>\n<\/td>\n

\n
\"\"<\/div>\n<\/td>\n<\/tr>\n
M.J. Chantler<\/td>\nImage models and feature measures for topological textures<\/strong>
\nResearch Memorandum RM\/93\/1, March 1993, Dept. Computing & Electrical Engineering.<\/em><\/td>\n		<\/td>\n<\/tr>\n
M.J. Chantler<\/td>\nTexture analysis of underwater video images<\/strong>
\nResearch Memorandum RM\/91\/25, December 1991, Dept. Computing & Electrical Engineering.<\/em><\/td>\n		<\/td>\n<\/tr>\n
M.J. Chantler<\/td>\nFractal characteristics of rendered fractal surfaces<\/strong>
\nResearch Memorandum RM\/92\/8, October 1992, Dept. Computing & Electrical Engineering<\/em>.<\/td>\n		<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

Reports and Miscellaneous Items Author Description Download Spence, A. Chantler, M. Apparatus and Methods for Obtaining Surface Texture Information Patent International Application No. PCT\/GB2005\/004241. Filing Date: 03.11.2005 Drbohlav, O Chantler, M. How do joint image statistics change with illumination? Technical … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":110,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"onecolumn-page.php","meta":[],"_links":{"self":[{"href":"https:\/\/www.macs.hw.ac.uk\/texturelab\/wp-json\/wp\/v2\/pages\/121"}],"collection":[{"href":"https:\/\/www.macs.hw.ac.uk\/texturelab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.macs.hw.ac.uk\/texturelab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.macs.hw.ac.uk\/texturelab\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.macs.hw.ac.uk\/texturelab\/wp-json\/wp\/v2\/comments?post=121"}],"version-history":[{"count":7,"href":"https:\/\/www.macs.hw.ac.uk\/texturelab\/wp-json\/wp\/v2\/pages\/121\/revisions"}],"predecessor-version":[{"id":152,"href":"https:\/\/www.macs.hw.ac.uk\/texturelab\/wp-json\/wp\/v2\/pages\/121\/revisions\/152"}],"up":[{"embeddable":true,"href":"https:\/\/www.macs.hw.ac.uk\/texturelab\/wp-json\/wp\/v2\/pages\/110"}],"wp:attachment":[{"href":"https:\/\/www.macs.hw.ac.uk\/texturelab\/wp-json\/wp\/v2\/media?parent=121"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}