3,750 research outputs found
Separating a Real-Life Nonlinear Image Mixture
When acquiring an image of a paper document, the image printed on the back page sometimes shows through. The mixture of the front- and back-page images thus obtained is markedly nonlinear, and thus constitutes a good real-life test case for nonlinear blind source separation.
This paper addresses a difficult version of this problem, corresponding to the use of "onion skin" paper, which results in a relatively strong nonlinearity of the mixture, which becomes close to singular in the lighter regions of the images. The separation is achieved through the MISEP technique, which is an extension of the well known INFOMAX method. The separation results are assessed with objective quality measures. They show an improvement over the results obtained with linear separation, but have room for further improvement
Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition
This paper presents a comparative study of two different methods, which are
based on fusion and polar transformation of visual and thermal images. Here,
investigation is done to handle the challenges of face recognition, which
include pose variations, changes in facial expression, partial occlusions,
variations in illumination, rotation through different angles, change in scale
etc. To overcome these obstacles we have implemented and thoroughly examined
two different fusion techniques through rigorous experimentation. In the first
method log-polar transformation is applied to the fused images obtained after
fusion of visual and thermal images whereas in second method fusion is applied
on log-polar transformed individual visual and thermal images. After this step,
which is thus obtained in one form or another, Principal Component Analysis
(PCA) is applied to reduce dimension of the fused images. Log-polar transformed
images are capable of handling complicacies introduced by scaling and rotation.
The main objective of employing fusion is to produce a fused image that
provides more detailed and reliable information, which is capable to overcome
the drawbacks present in the individual visual and thermal face images.
Finally, those reduced fused images are classified using a multilayer
perceptron neural network. The database used for the experiments conducted here
is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database
benchmark thermal and visual face images. The second method has shown better
performance, which is 95.71% (maximum) and on an average 93.81% as correct
recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in
Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11
- 15, 201
Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs
Scene recognition with RGB images has been extensively studied and has
reached very remarkable recognition levels, thanks to convolutional neural
networks (CNN) and large scene datasets. In contrast, current RGB-D scene data
is much more limited, so often leverages RGB large datasets, by transferring
pretrained RGB CNN models and fine-tuning with the target RGB-D dataset.
However, we show that this approach has the limitation of hardly reaching
bottom layers, which is key to learn modality-specific features. In contrast,
we focus on the bottom layers, and propose an alternative strategy to learn
depth features combining local weakly supervised training from patches followed
by global fine tuning with images. This strategy is capable of learning very
discriminative depth-specific features with limited depth images, without
resorting to Places-CNN. In addition we propose a modified CNN architecture to
further match the complexity of the model and the amount of data available. For
RGB-D scene recognition, depth and RGB features are combined by projecting them
in a common space and further leaning a multilayer classifier, which is jointly
optimized in an end-to-end network. Our framework achieves state-of-the-art
accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.Comment: AAAI Conference on Artificial Intelligence 201
- …