Browsing by Author "Atalay, Volkan"

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Citation - WoS: 0
Citation - Scopus: 3
Experimental study on the sensitivity of autocalibration to projective camera model parameters
(Spie-soc Photo-optical instrumentation Engineers, 2006) Hassanpour, Reza; Hassanpour, Reza; Atalay, Volkan; 48646; Yazılım Mühendisliği
Existing methods of 3-D object modeling and recovering 3-D data from uncalibrated 2-D images are subject to errors introduced by assumptions about camera parameters and mismatches in finding point pairs in the images. In this study, we experimentally evaluate the effect of each of these assumptions together with the inaccuracy in the measurements in the images. Sensitivity of reconstruction errors to inaccuracies in the estimation of camera parameters and mismatches due to noise in input data is measured using a linear and two nonlinear autocalibration methods for a projective camera. Our experimental results show that some assumptions such as a vanishing skew can be safely made; however, other parameters such as principal point location are quite sensitive to wrong assumptions. (c) 2006 Society of Photo-Optical Instrumentation Engineers.
Texture segmentation using the mixtures of principal component analyzers
(2003) Musa, Mohamed E.M.; Duin, Robert P.W.; De Ridder, Dick; Atalay, Volkan
The problem of segmenting an image into several modalities representing different textures can be modelled using Gaussian mixtures. Moreover, texture image patches when translated, rotated or scaled lie in low dimensional subspaces of the high-dimensional space spanned by the grey values. These two aspects make the mixture of local subspace models worth consideration for segmenting this type of images. In recent years a number of mixtures of local PCA models have been proposed. Most of these models require the user to set the number of subspaces and subspace dimensionalities. To make the model autonomous, we propose a greedy EM algorithm to find a suboptimal number of subspaces, besides using a global retained variance ratio to estimate for each subspace the dimensionality that retains the given variability ratio. We provide experimental results for testing the proposed method on texture segmentation.