17,104 research outputs found
Importance Sampling for Objetive Funtion Estimations in Neural Detector Traing Driven by Genetic Algorithms
To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low objective function value estimations, and the use of Importance Sampling (IS) techniques becomes convenient. The study of three different objective functions is considered, which implies the proposal of estimators of the objective function using IS techniques: the Mean-Square error, the Cross Entropy error and the Misclassification error criteria. The values of these functions are estimated by IS techniques, and the results are used to train NNs by the application of Genetic Algorithms. Results for a binary detection in Gaussian noise are provided. These results show the evolution of the parameters during the training and the performances of the proposed detectors in terms of error probability and Receiver Operating Characteristics curves. At the end of the study, the obtained results justify the convenience of using IS in the training
Stellar classification from single-band imaging using machine learning
Information on the spectral types of stars is of great interest in view of
the exploitation of space-based imaging surveys. In this article, we
investigate the classification of stars into spectral types using only the
shape of their diffraction pattern in a single broad-band image. We propose a
supervised machine learning approach to this endeavour, based on principal
component analysis (PCA) for dimensionality reduction, followed by artificial
neural networks (ANNs) estimating the spectral type. Our analysis is performed
with image simulations mimicking the Hubble Space Telescope (HST) Advanced
Camera for Surveys (ACS) in the F606W and F814W bands, as well as the Euclid
VIS imager. We first demonstrate this classification in a simple context,
assuming perfect knowledge of the point spread function (PSF) model and the
possibility of accurately generating mock training data for the machine
learning. We then analyse its performance in a fully data-driven situation, in
which the training would be performed with a limited subset of bright stars
from a survey, and an unknown PSF with spatial variations across the detector.
We use simulations of main-sequence stars with flat distributions in spectral
type and in signal-to-noise ratio, and classify these stars into 13 spectral
subclasses, from O5 to M5. Under these conditions, the algorithm achieves a
high success rate both for Euclid and HST images, with typical errors of half a
spectral class. Although more detailed simulations would be needed to assess
the performance of the algorithm on a specific survey, this shows that stellar
classification from single-band images is well possible.Comment: 10 pages, 9 figures, 2 tables, accepted in A&
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
In this paper, we propose the 3DFeat-Net which learns both 3D feature
detector and descriptor for point cloud matching using weak supervision. Unlike
many existing works, we do not require manual annotation of matching point
clusters. Instead, we leverage on alignment and attention mechanisms to learn
feature correspondences from GPS/INS tagged 3D point clouds without explicitly
specifying them. We create training and benchmark outdoor Lidar datasets, and
experiments show that 3DFeat-Net obtains state-of-the-art performance on these
gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201
- …