scholarly journals Evolutionary Approach to Epipolar Geometry Estimation

10.5772/13040 ◽  
2011 ◽  
Author(s):  
Sergio Taraglio ◽  
Stefano Chies
Keyword(s):  
Epipolar Geometry ◽  
Evolutionary Approach ◽  
Geometry Estimation

Epipolar geometry estimation based on evolutionary agents

2008 ◽  
Vol 41 (2) ◽  
pp. 575-591 ◽  
Author(s):  
Mingxing Hu ◽  
Karen McMenemy ◽  
Stuart Ferguson ◽  
Gordon Dodds ◽  
Baozong Yuan
Keyword(s):  
Epipolar Geometry ◽  
Geometry Estimation

Epipolar Geometry Estimation, Metric Reconstruction and Error Analysis from Two Images

2012 ◽  
Vol 45 (6) ◽  
pp. 1739-1744
Author(s):  
Rogério Yugo Takimoto ◽  
Thiago de Castro Martins ◽  
Fábio Kawaoka Takase ◽  
Marcos de Sales Guerra Tsuzuki
Keyword(s):  
Error Analysis ◽  
Epipolar Geometry ◽  
Metric Reconstruction ◽  
Geometry Estimation

HEAD-CENTRIC DISPARITY AND EPIPOLAR GEOMETRY ESTIMATION FROM A POPULATION OF BINOCULAR ENERGY NEURONS

2012 ◽  
Vol 22 (03) ◽  
pp. 1250007 ◽  
Author(s):  
KARL PAUWELS ◽  
MARC M. VAN HULLE
Keyword(s):  
Real World ◽  
Network Architecture ◽  
Degrees Of Freedom ◽  
Computational Models ◽  
Epipolar Geometry ◽  
Disparity Estimation ◽  
Neural Network Architecture ◽  
Six Degrees Of Freedom ◽  
Geometry Estimation ◽  
Image Pairs

We present a hybrid neural network architecture that supports the estimation of binocular disparity in a cyclopean, head-centric coordinate system without explicitly establishing retinal correspondences. Instead the responses of binocular energy neurons are gain-modulated by oculomotor signals. The network can handle the full six degrees of freedom of binocular gaze and operates directly on image pairs of possibly varying contrast. Furthermore, we show that in the absence of an oculomotor signal the same architecture is capable of estimating the epipolar geometry directly from the population response. The increased complexity of the scenarios considered in this work provides an important step towards the application of computational models centered on gain modulation mechanisms in real-world robotic applications. The proposed network is shown to outperform a standard computer vision technique on a disparity estimation task involving real-world stereo images.

Robust epipolar geometry estimation using noisy pose priors

2017 ◽  
Vol 67 ◽  
pp. 16-28 ◽  
Author(s):  
Yehonatan Goldman ◽  
Ehud Rivlin ◽  
Ilan Shimshoni
Keyword(s):  
Epipolar Geometry ◽  
Geometry Estimation
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