3,518 research outputs found
LiDAR-assisted Large-scale Privacy Protection in Street-view Cycloramas
Recently, privacy has a growing importance in several domains, especially in
street-view images. The conventional way to achieve this is to automatically
detect and blur sensitive information from these images. However, the
processing cost of blurring increases with the ever-growing resolution of
images. We propose a system that is cost-effective even after increasing the
resolution by a factor of 2.5. The new system utilizes depth data obtained from
LiDAR to significantly reduce the search space for detection, thereby reducing
the processing cost. Besides this, we test several detectors after reducing the
detection space and provide an alternative solution based on state-of-the-art
deep learning detectors to the existing HoG-SVM-Deep system that is faster and
has a higher performance.Comment: Accepted at Electronic Imaging 201
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Remote Sensing Image Retrieval remains a challenging topic due to the special
nature of Remote Sensing Imagery. Such images contain various different
semantic objects, which clearly complicates the retrieval task. In this paper,
we present an image retrieval pipeline that uses attentive, local convolutional
features and aggregates them using the Vector of Locally Aggregated Descriptors
(VLAD) to produce a global descriptor. We study various system parameters such
as the multiplicative and additive attention mechanisms and descriptor
dimensionality. We propose a query expansion method that requires no external
inputs. Experiments demonstrate that even without training, the local
convolutional features and global representation outperform other systems.
After system tuning, we can achieve state-of-the-art or competitive results.
Furthermore, we observe that our query expansion method increases overall
system performance by about 3%, using only the top-three retrieved images.
Finally, we show how dimensionality reduction produces compact descriptors with
increased retrieval performance and fast retrieval computation times, e.g. 50%
faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal
contributio
Bootstrapped CNNs for Building Segmentation on RGB-D Aerial Imagery
Detection of buildings and other objects from aerial images has various
applications in urban planning and map making. Automated building detection
from aerial imagery is a challenging task, as it is prone to varying lighting
conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are
robust against some of these variations, although they fail to distinguish easy
and difficult examples. We train a detection algorithm from RGB-D images to
obtain a segmented mask by using the CNN architecture DenseNet.First, we
improve the performance of the model by applying a statistical re-sampling
technique called Bootstrapping and demonstrate that more informative examples
are retained. Second, the proposed method outperforms the non-bootstrapped
version by utilizing only one-sixth of the original training data and it
obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.Comment: Published at ISPRS Annals of the Photogrammetry, Remote Sensing and
Spatial Information Science
Homography Estimation in Complex Topological Scenes
Surveillance videos and images are used for a broad set of applications,
ranging from traffic analysis to crime detection. Extrinsic camera calibration
data is important for most analysis applications. However, security cameras are
susceptible to environmental conditions and small camera movements, resulting
in a need for an automated re-calibration method that can account for these
varying conditions. In this paper, we present an automated camera-calibration
process leveraging a dictionary-based approach that does not require prior
knowledge on any camera settings. The method consists of a custom
implementation of a Spatial Transformer Network (STN) and a novel topological
loss function. Experiments reveal that the proposed method improves the IoU
metric by up to 12% w.r.t. a state-of-the-art model across five synthetic
datasets and the World Cup 2014 dataset.Comment: Will be published in Intelligent Vehicle Symposium 202
New Surface Active Antibiotics11Read at the Seventh Annual Meeting of the Society for Investigative Dermatology, San Francisco, California, June 30, 1946.
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