Add to ORKGIn this paper, we investigate the use of a neural network employing Genralised Hebbian Learning for the approximation of an image of a hypothetically ellipsoidal object as an ellipse. Further, we discuss how the same algorithm is used with higher dimensional data to model hyperellipsoids, with the basic aim at a specific application, namely the modelling of an object as an ellipsoid given a set of 3-dimensional points
Ellipses are a widely used cue in many 2D and 3D object recognition pipelines. In fact, they exhibit...
We present a class of neural networks algorithms based on simple Hebbian learning which allow the fi...
In this paper, we propose a new learning (SPRM) called the Hebbian Learning Subspace Method (HLSM). ...
In this paper, we investigate the use of a neural network employing Genralised Hebbian Learning for ...
In this paper, we investigate a method of using principal component analysis(PCA) to fit an encapsul...
This paper presents a new method for fitting an ellipse to a point sequence extracted from images. I...
We present an ellipse finding and fitting algorithm that uses points and tangents, rather than just ...
Abstract—Ellipse and ellipsoid fitting has been extensively re-searched and widely applied. Although...
We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object m...
We investigate an extension of Hebbian learning in a principal component analysis network which has ...
Least square fitting of quadratic surfaces is a fundamental problem in pattern recognition, computer...
International audienceWe are given a set of points in a space of high dimension. For instance, this ...
The convergence performance of typical numerical schemes for geometric fitting for computer vision a...
In this paper, we review an extension of the learning rules in a Principal Component Analysis networ...
We present a novel method based on a recently proposed extension to a negative feedback network whic...
Ellipses are a widely used cue in many 2D and 3D object recognition pipelines. In fact, they exhibit...
We present a class of neural networks algorithms based on simple Hebbian learning which allow the fi...
In this paper, we propose a new learning (SPRM) called the Hebbian Learning Subspace Method (HLSM). ...
In this paper, we investigate the use of a neural network employing Genralised Hebbian Learning for ...
In this paper, we investigate a method of using principal component analysis(PCA) to fit an encapsul...
This paper presents a new method for fitting an ellipse to a point sequence extracted from images. I...
We present an ellipse finding and fitting algorithm that uses points and tangents, rather than just ...
Abstract—Ellipse and ellipsoid fitting has been extensively re-searched and widely applied. Although...
We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object m...
We investigate an extension of Hebbian learning in a principal component analysis network which has ...
Least square fitting of quadratic surfaces is a fundamental problem in pattern recognition, computer...
International audienceWe are given a set of points in a space of high dimension. For instance, this ...
The convergence performance of typical numerical schemes for geometric fitting for computer vision a...
In this paper, we review an extension of the learning rules in a Principal Component Analysis networ...
We present a novel method based on a recently proposed extension to a negative feedback network whic...
Ellipses are a widely used cue in many 2D and 3D object recognition pipelines. In fact, they exhibit...
We present a class of neural networks algorithms based on simple Hebbian learning which allow the fi...
In this paper, we propose a new learning (SPRM) called the Hebbian Learning Subspace Method (HLSM). ...