The problem of object recognition in computer vision is addressed. A method for model indexing, w... more The problem of object recognition in computer vision is addressed. A method for model indexing, which, given a group of image features, rapidly extracts from the list of objects those objects containing this group of features, is presented. The method operates on an abstract representation of features, more precisely, groups of features. In practice, this abstract representation takes the form of a graph. The present study deals with binary graphs only, that is, only one feature-type and one feature-relationship-type can be embedded in the representation
This paper describes a method for autocalibrating a stereo rig. A planar object performing genera... more This paper describes a method for autocalibrating a stereo rig. A planar object performing general and unknown motions is observed by the stereo rig and, based on point correspondences only, the autocalibration of the stereo rig is computed. A stratified approach is used and the autocalibration is computed by estimating first the epipolar geometry of the rig, then the plane at infinity Π∞ (affine calibration) and finally the absolute conic Ω∞ (Euclidean calibration). We show that the affine and Euclidean calibrations involve quadratic constraints and we describe an algorithm to solve them based on a conic intersection technique. Experiments with both synthetic and real data are used to evaluate the performance of the method.
In this paper we present a method for optimally estimating the rotation and translation between a... more In this paper we present a method for optimally estimating the rotation and translation between a camera and a 3-D object from point and/or line correspondences. First we devise an error function and second we show how to minimize this error function. The quadratic nature of this function is made possible by representing rotation and translation with a dual number quaternion. We provide a detailed account of the computational aspects of a trust-region optimization method. This method compares favourably with Newton's method which has extensively been used to solve the problem at hand, with Faugeras-Toscani's linear method (Faugeras and Toscani 1986) for calibrating a camera, and with the Levenberg-Marquardt non-linear optimization method. Finally we present some experimental results which demonstrate the robustness of our method with respect to image noise and matching errors.
The authors present a method for robustly and accurately estimating the rotation and translation ... more The authors present a method for robustly and accurately estimating the rotation and translation between a camera and a 3-D object from point and line correspondences. First they devise an error function and then show how to minimize this error function. The quadratic nature of this function is made possible by representing rotation and translation with a dual number quaternion. A detailed account is provided of the computational aspects of a trust-region optimization method. This method compares favourably with Newton's method, which has extensively been used to solve the problem, and with Faugeras-Toscani's linear method (1986) for calibrating a camera. Some experimental results are presented which demonstrate the robustness of the method with respect to image noise and matching errors
The problem of object recognition in computer vision is addressed. A method for model indexing, w... more The problem of object recognition in computer vision is addressed. A method for model indexing, which, given a group of image features, rapidly extracts from the list of objects those objects containing this group of features, is presented. The method operates on an abstract representation of features, more precisely, groups of features. In practice, this abstract representation takes the form of a graph. The present study deals with binary graphs only, that is, only one feature-type and one feature-relationship-type can be embedded in the representation
This paper describes a method for autocalibrating a stereo rig. A planar object performing genera... more This paper describes a method for autocalibrating a stereo rig. A planar object performing general and unknown motions is observed by the stereo rig and, based on point correspondences only, the autocalibration of the stereo rig is computed. A stratified approach is used and the autocalibration is computed by estimating first the epipolar geometry of the rig, then the plane at infinity Π∞ (affine calibration) and finally the absolute conic Ω∞ (Euclidean calibration). We show that the affine and Euclidean calibrations involve quadratic constraints and we describe an algorithm to solve them based on a conic intersection technique. Experiments with both synthetic and real data are used to evaluate the performance of the method.
In this paper we present a method for optimally estimating the rotation and translation between a... more In this paper we present a method for optimally estimating the rotation and translation between a camera and a 3-D object from point and/or line correspondences. First we devise an error function and second we show how to minimize this error function. The quadratic nature of this function is made possible by representing rotation and translation with a dual number quaternion. We provide a detailed account of the computational aspects of a trust-region optimization method. This method compares favourably with Newton's method which has extensively been used to solve the problem at hand, with Faugeras-Toscani's linear method (Faugeras and Toscani 1986) for calibrating a camera, and with the Levenberg-Marquardt non-linear optimization method. Finally we present some experimental results which demonstrate the robustness of our method with respect to image noise and matching errors.
The authors present a method for robustly and accurately estimating the rotation and translation ... more The authors present a method for robustly and accurately estimating the rotation and translation between a camera and a 3-D object from point and line correspondences. First they devise an error function and then show how to minimize this error function. The quadratic nature of this function is made possible by representing rotation and translation with a dual number quaternion. A detailed account is provided of the computational aspects of a trust-region optimization method. This method compares favourably with Newton's method, which has extensively been used to solve the problem, and with Faugeras-Toscani's linear method (1986) for calibrating a camera. Some experimental results are presented which demonstrate the robustness of the method with respect to image noise and matching errors
Uploads
Papers by Radu Horaud