Browsing by Author "Atalay, V"

Now showing 1 - 5 of 5

Citation - WoS: 4
Citation - Scopus: 5
Almost autonomous training of mixtures of principal component analyzers
(Elsevier Science Bv, 2004) Musa, MEM; de Ridder, D; Duin, RPW; Atalay, V
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 submodels (local models) in the mixture and the dimensionality of the submodels (i.e., number of PC's) as well. To make the model free of these parameters, we propose a greedy expectation-maximization algorithm to find a suboptimal number of submodels. For a given retained variance ratio, the proposed algorithm estimates for each submodel the dimensionality that retains this given variability ratio. We test the proposed method on two different classification problems: handwritten digit recognition and 2-class ionosphere data classification. The results show that the proposed method has a good performance. (C) 2004 Elsevier B.V. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 16
Camera Auto-calibration Using a Sequence of 2D Images with Small Rotations
(Elsevier, 2004) Hassanpour, R; Hassanpour, Reza; Atalay, V; Yazılım Mühendisliği
In this study, we describe an auto-calibration algorithm with fixed but unknown camera parameters. We have modified Triggs' algorithm to incorporate known aspect ratio and skew values to make it applicable for small rotation around a single axis. The algorithm despite being a quadratic one is easy to solve. We have applied the algorithm to some artificial objects with known size and dimensions for evaluation purposes. In addition, the accuracy of the algorithm has been verified using synthetic data. The described method is particularly suitable for three dimensional human head modeling. (C) 2004 Elsevier B.V. All rights reserved.
Citation - WoS: 0
Camera auto-calibration using a sequence of 2D images with small rotations and translations
(Crc Press-taylor & Francis Group, 2003) Hassanpour, R; Atalay, V
3D model generation needs depth information of the object in the input images. This information can be found using stereo imaging but it needs camera parameters. Camera calibration is not possible without some knowledge about the objects in the scene or assuming fixed or known values for the camera parameters. When using fixed camera parameters, however, small rotation angles or small translation in camera position can degenerate the results. The degeneracy can be omitted by adding new restrictions to the a-priori knowledge about the camera parameters. The calibrated data may be used to reconstruct 3D model of the scene.
Citation - WoS: 0
Head Modeling with Camera Auto-calibration and Deformation
(Akademische verlagsgesellsch Aka Gmbh, 2002) Hassanpour, R; Hassanpour, Reza; Atalay, V; Yazılım Mühendisliği
A 3D head modeling method from a sequence of 2D images is described. The views from which the input images are acquired are not calibrated., Therefore, an auto-calibration method for a sequence of images with small rotations and translation is developed. For this purpose, we have modified an already existing auto-calibration algorithm to incorporate known aspect ratio and skew values to make it applicable for small rotation around a single axis. We apply this auto-calibration technique to head (face) modeling. Three dimensional positions of known facial features computed from two dimensional images are used to. deform a generic head model by using a spring based energy minimization method.
Citation - WoS: 1
Citation - Scopus: 2
Texture Segmentation Using the Mixtures of Principal Component Analyzers
(Springer-verlag Berlin, 2003) Musa, MEM; Duin, RPW; de Ridder, D; Atalay, V
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.