7,719 research outputs found
Analysis of multi-sensor data, 12 September - 11 December 1968
Analysis of multi-sensor data obtained by Earth Resources Aircraft Progra
Towards learning free naive bayes nearest neighbor-based domain adaptation
As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015
Angular variation of the magnetoresistance of the superconducting ferromagnet UCoGe
We report a magnetoresistance study of the superconducting ferromagnet UCoGe.
The data, taken on single-crystalline samples, show a pronounced structure at
~T for a field applied along the ordered moment . Angle
dependent measurements reveal this field-induced phenomenon has an uniaxial
anisotropy. Magnetoresistance measurements under pressure show a rapid increase
of to 12.8~T at 1.0~GPa. We discuss in terms of a field induced
polarization change. Upper critical field measurements corroborate the unusual
S-shaped -curve for a field along the -axis of the orthorhombic
unit cell.Comment: 6 pages, 5 figures; accepted for publication in Phys. Rev.
Object Matching in Distributed Video Surveillance Systems by LDA-Based Appearance Descriptors
Establishing correspondences among object instances is still challenging in multi-camera surveillance systems, especially when the cameras’ fields of view are non-overlapping. Spatiotemporal constraints can help in solving the correspondence problem but still leave a wide margin of uncertainty. One way to reduce this uncertainty is to use appearance information about the moving objects in the site. In this paper we present the preliminary results of a new method that can capture salient appearance characteristics at each camera node in the network. A Latent Dirichlet Allocation (LDA) model is created and maintained at each node in the camera network. Each object is encoded in terms of the LDA bag-of-words model for appearance. The encoded appearance is then used to establish probable matching across cameras. Preliminary experiments are conducted on a dataset of 20 individuals and comparison against Madden’s I-MCHR is reported
Face Detection with Effective Feature Extraction
There is an abundant literature on face detection due to its important role
in many vision applications. Since Viola and Jones proposed the first real-time
AdaBoost based face detector, Haar-like features have been adopted as the
method of choice for frontal face detection. In this work, we show that simple
features other than Haar-like features can also be applied for training an
effective face detector. Since, single feature is not discriminative enough to
separate faces from difficult non-faces, we further improve the generalization
performance of our simple features by introducing feature co-occurrences. We
demonstrate that our proposed features yield a performance improvement compared
to Haar-like features. In addition, our findings indicate that features play a
crucial role in the ability of the system to generalize.Comment: 7 pages. Conference version published in Asian Conf. Comp. Vision
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Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
Image-based camera relocalization is an important problem in computer vision
and robotics. Recent works utilize convolutional neural networks (CNNs) to
regress for pixels in a query image their corresponding 3D world coordinates in
the scene. The final pose is then solved via a RANSAC-based optimization scheme
using the predicted coordinates. Usually, the CNN is trained with ground truth
scene coordinates, but it has also been shown that the network can discover 3D
scene geometry automatically by minimizing single-view reprojection loss.
However, due to the deficiencies of the reprojection loss, the network needs to
be carefully initialized. In this paper, we present a new angle-based
reprojection loss, which resolves the issues of the original reprojection loss.
With this new loss function, the network can be trained without careful
initialization, and the system achieves more accurate results. The new loss
also enables us to utilize available multi-view constraints, which further
improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning
MinMax Radon Barcodes for Medical Image Retrieval
Content-based medical image retrieval can support diagnostic decisions by
clinical experts. Examining similar images may provide clues to the expert to
remove uncertainties in his/her final diagnosis. Beyond conventional feature
descriptors, binary features in different ways have been recently proposed to
encode the image content. A recent proposal is "Radon barcodes" that employ
binarized Radon projections to tag/annotate medical images with content-based
binary vectors, called barcodes. In this paper, MinMax Radon barcodes are
introduced which are superior to "local thresholding" scheme suggested in the
literature. Using IRMA dataset with 14,410 x-ray images from 193 different
classes, the advantage of using MinMax Radon barcodes over \emph{thresholded}
Radon barcodes are demonstrated. The retrieval error for direct search drops by
more than 15\%. As well, SURF, as a well-established non-binary approach, and
BRISK, as a recent binary method are examined to compare their results with
MinMax Radon barcodes when retrieving images from IRMA dataset. The results
demonstrate that MinMax Radon barcodes are faster and more accurate when
applied on IRMA images.Comment: To appear in proceedings of the 12th International Symposium on
Visual Computing, December 12-14, 2016, Las Vegas, Nevada, US
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