VG-RAM WNN approach to monocular depth perception

Authors:
Hélio Perroni Filho;Alberto F. De Souza
Affiliations:
Laboratório de Computação de Alto Desempenho, Universidade Federal do Espírito Santo, Vitória, ES, Brazil;Laboratório de Computação de Alto Desempenho, Universidade Federal do Espírito Santo, Vitória, ES, Brazil
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

Citing 4
Cited 0

3-D Depth Reconstruction from a Single Still Image

International Journal of Computer Vision
A theoretical and experimental account of n-tuple classifier performance

Neural Computation
Face Recognition with VG-RAM Weightless Neural Networks

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Improving VG-RAM neural networks performance using knowledge correlation

ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We have examined Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) as platform for depth map inference from static monocular images. For that, we have designed, implemented and compared the performance of VG-RAM WNN systems against that of depth estimation systems based on Markov Random Field (MRF) models. While not surpassing the performance of such systems, our results are consistent to theirs, and allow us to infer important features of the human visual cortex.