105,047 research outputs found
A new protocol for texture mapping process and 2d representation of rupestrian architecture
The development of the survey techniques for architecture and archaeology requires a general review in the methods used for the representation of numerical data. The possibilities offered by data processing allow to find new paths for studying issues connected to the drawing discipline. The research project aimed at experimenting different approaches for the representation of the rupestrian architecture and the texture mapping process. The nature of the rupestrian architecture does not allow a traditional representation of sections and projections of edges and outlines. The paper presents a method, the Equidistant Multiple Sections (EMS), inspired by cartography and based on the use of isohipses generated from different geometric plane. A specific paragraph is dedicated to the
texture mapping process for unstructured surface models. One of the main difficulty in the image projection consists in the recognition of homologous points between image and point cloud, above all in the areas with most deformations. With the aid of the “virtual scan” tool a different procedure was developed for improving the correspondences of the image. The result show a sensible improvement of the entire process above all for the architectural vaults. A detailed study concerned the unfolding of the straight line surfaces; the barrel vault of the analyzed chapel has been unfolded for observing the paintings in the real shapes out of the morphological context
Stratified Transfer Learning for Cross-domain Activity Recognition
In activity recognition, it is often expensive and time-consuming to acquire
sufficient activity labels. To solve this problem, transfer learning leverages
the labeled samples from the source domain to annotate the target domain which
has few or none labels. Existing approaches typically consider learning a
global domain shift while ignoring the intra-affinity between classes, which
will hinder the performance of the algorithms. In this paper, we propose a
novel and general cross-domain learning framework that can exploit the
intra-affinity of classes to perform intra-class knowledge transfer. The
proposed framework, referred to as Stratified Transfer Learning (STL), can
dramatically improve the classification accuracy for cross-domain activity
recognition. Specifically, STL first obtains pseudo labels for the target
domain via majority voting technique. Then, it performs intra-class knowledge
transfer iteratively to transform both domains into the same subspaces.
Finally, the labels of target domain are obtained via the second annotation. To
evaluate the performance of STL, we conduct comprehensive experiments on three
large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI
DSADS), which demonstrates that STL significantly outperforms other
state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%).
Furthermore, we extensively investigate the performance of STL across different
degrees of similarities and activity levels between domains. And we also
discuss the potential of STL in other pervasive computing applications to
provide empirical experience for future research.Comment: 10 pages; accepted by IEEE PerCom 2018; full paper. (camera-ready
version
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Deep neural networks are vulnerable to adversarial examples, i.e.,
carefully-perturbed inputs aimed to mislead classification. This work proposes
a detection method based on combining non-linear dimensionality reduction and
density estimation techniques. Our empirical findings show that the proposed
approach is able to effectively detect adversarial examples crafted by
non-adaptive attackers, i.e., not specifically tuned to bypass the detection
method. Given our promising results, we plan to extend our analysis to adaptive
attackers in future work.Comment: European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN) 201
Exploiting 2D Floorplan for Building-scale Panorama RGBD Alignment
This paper presents a novel algorithm that utilizes a 2D floorplan to align
panorama RGBD scans. While effective panorama RGBD alignment techniques exist,
such a system requires extremely dense RGBD image sampling. Our approach can
significantly reduce the number of necessary scans with the aid of a floorplan
image. We formulate a novel Markov Random Field inference problem as a scan
placement over the floorplan, as opposed to the conventional scan-to-scan
alignment. The technical contributions lie in multi-modal image correspondence
cues (between scans and schematic floorplan) as well as a novel coverage
potential avoiding an inherent stacking bias. The proposed approach has been
evaluated on five challenging large indoor spaces. To the best of our
knowledge, we present the first effective system that utilizes a 2D floorplan
image for building-scale 3D pointcloud alignment. The source code and the data
will be shared with the community to further enhance indoor mapping research
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