Name
Title | ||
|---|---|---|
Efficient and Robust Homography Estimation Using Compressed Convolutional Neural Network. |
Abstract | ||
|---|---|---|
Homography estimation is one of the important ways to calculate the transformation between images. For most embedded terminal devices, an efficient and robust homography estimation algorithm is extremely necessary. In this paper, we design an innovative compressed convolutional neural network to estimate homographies which work very well. The model size of the network is less than 10 MB, which is small enough to be used on mobile devices. In addition, to improve the estimated accuracy in challenging environment, we present a novel loss function to train our network. Finally, we compare our algorithm with traditional methods and other learning-based methods. Experiments on our compressed network demonstrate that the innovative network achieves better accuracy compared to other learning-based algorithms, and is more robust to illumination changes compared to traditional algorithms. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-981-13-8138-6_13 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Homography estimation,Convolutional neural network,Model compression,Loss function | Conference | 1009 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoping Wang | 1 | 488 | 63.02 |
| Zhixiang You | 2 | 1 | 1.71 |
| Ping An | 3 | 545 | 68.73 |
| Jiadong Yu | 4 | 0 | 0.34 |
| Yilei Chen | 5 | 0 | 2.37 |