We propose a definition of new 3D invariants. Usual pattern recognition methods use 2D descriptions of 3D objects, we propose a 2D approximation of the ...
We propose a definition of new 3D invariants. Usual pattern recognition methods use 2D descriptions of 3D objects, we propose a 2D approximation of the ...
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Definition of new 3D invariants : applications to pattern recognition problems with neural networks. Author(s), Proriol, J. Publication, 1996. - 18 p. Imprint ...
Files · Definition of new 3D invariants: applications to pattern recognition problems with neural networks - Proriol, J. Main file(s):. SCAN-9701020. version 1 ...
A 3D convolutional neural network can be used in computer vision for action recognition, object and pattern recognition, and scene understanding. For ...
This book is aimed at researchers in neural computing as well as those wishing to apply neural networks to practical applications. It is also intended to be ...
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We propose a 2D approximation of the 3D defined invariants using the 2D silhouette data. The pattern recognition is improved by using MLP neural networks with ...
For example, we show how G-EED can be applied to a model's latent space representations to measure the extent to which a model extracts G-invariant features.
These moment invariants are successfully used as image descriptors in many pattern recognition applications [8], [9]. Since the first introduction of moment ...
In this paper, we are going to present a novel shape similarity retrieval algorithm that can be used to match and recognize 2D objects. The match process uses a ...