12,935 research outputs found
End-to-end Projector Photometric Compensation
Projector photometric compensation aims to modify a projector input image
such that it can compensate for disturbance from the appearance of projection
surface. In this paper, for the first time, we formulate the compensation
problem as an end-to-end learning problem and propose a convolutional neural
network, named CompenNet, to implicitly learn the complex compensation
function. CompenNet consists of a UNet-like backbone network and an autoencoder
subnet. Such architecture encourages rich multi-level interactions between the
camera-captured projection surface image and the input image, and thus captures
both photometric and environment information of the projection surface. In
addition, the visual details and interaction information are carried to deeper
layers along the multi-level skip convolution layers. The architecture is of
particular importance for the projector compensation task, for which only a
small training dataset is allowed in practice. Another contribution we make is
a novel evaluation benchmark, which is independent of system setup and thus
quantitatively verifiable. Such benchmark is not previously available, to our
best knowledge, due to the fact that conventional evaluation requests the
hardware system to actually project the final results. Our key idea, motivated
from our end-to-end problem formulation, is to use a reasonable surrogate to
avoid such projection process so as to be setup-independent. Our method is
evaluated carefully on the benchmark, and the results show that our end-to-end
learning solution outperforms state-of-the-arts both qualitatively and
quantitatively by a significant margin.Comment: To appear in the 2019 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). Source code and dataset are available at
https://github.com/BingyaoHuang/compenne
BLADE: Filter Learning for General Purpose Computational Photography
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive filtering framework that is general, simple, computationally
efficient, and useful for a wide range of problems in computational
photography. We show applications to operations which may appear in a camera
pipeline including denoising, demosaicing, and stylization
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical
images is challenging due to limited images covering different disease types
and severity. The problem is especially acute, where there is a severe class
imbalance. We propose an active learning (AL) framework to select most
informative samples for training our model using a Bayesian neural network.
Informative samples are then used within a novel class aware generative
adversarial network (CAGAN) to generate realistic chest xray images for data
augmentation by transferring characteristics from one class label to another.
Experiments show our proposed AL framework is able to achieve state-of-the-art
performance by using about of the full dataset, thus saving significant
time and effort over conventional methods
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network
In many domestic and military applications, aerial vehicle detection and
super-resolutionalgorithms are frequently developed and applied independently.
However, aerial vehicle detection on super-resolved images remains a
challenging task due to the lack of discriminative information in the
super-resolved images. To address this problem, we propose a Joint
Super-Resolution and Vehicle DetectionNetwork (Joint-SRVDNet) that tries to
generate discriminative, high-resolution images of vehicles fromlow-resolution
aerial images. First, aerial images are up-scaled by a factor of 4x using a
Multi-scaleGenerative Adversarial Network (MsGAN), which has multiple
intermediate outputs with increasingresolutions. Second, a detector is trained
on super-resolved images that are upscaled by factor 4x usingMsGAN architecture
and finally, the detection loss is minimized jointly with the super-resolution
loss toencourage the target detector to be sensitive to the subsequent
super-resolution training. The network jointlylearns hierarchical and
discriminative features of targets and produces optimal super-resolution
results. Weperform both quantitative and qualitative evaluation of our proposed
network on VEDAI, xView and DOTAdatasets. The experimental results show that
our proposed framework achieves better visual quality than thestate-of-the-art
methods for aerial super-resolution with 4x up-scaling factor and improves the
accuracy ofaerial vehicle detection
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