180 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
Dual Embedding Expansion for Vehicle Re-identification
Vehicle re-identification plays a crucial role in the management of
transportation infrastructure and traffic flow. However, this is a challenging
task due to the large view-point variations in appearance, environmental and
instance-related factors. Modern systems deploy CNNs to produce unique
representations from the images of each vehicle instance. Most work focuses on
leveraging new losses and network architectures to improve the descriptiveness
of these representations. In contrast, our work concentrates on re-ranking and
embedding expansion techniques. We propose an efficient approach for combining
the outputs of multiple models at various scales while exploiting tracklet and
neighbor information, called dual embedding expansion (DEx). Additionally, a
comparative study of several common image retrieval techniques is presented in
the context of vehicle re-ID. Our system yields competitive performance in the
2020 NVIDIA AI City Challenge with promising results. We demonstrate that DEx
when combined with other re-ranking techniques, can produce an even larger gain
without any additional attribute labels or manual supervision
Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN
Fast and accurate catheter detection in cardiac catheterization using
harmless 3D ultrasound (US) can improve the efficiency and outcome of the
intervention. However, the low image quality of US requires extra training for
sonographers to localize the catheter. In this paper, we propose a catheter
detection method based on a pre-trained VGG network, which exploits 3D
information through re-organized cross-sections to segment the catheter by a
shared fully convolutional network (FCN), which is called a Direction-Fused FCN
(DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized
by model fitting. Our experiments show that the proposed method can
successfully detect an ablation catheter in a challenging ex-vivo 3D US
dataset, which was collected on the porcine heart. Extensive analysis shows
that the proposed method achieves a Dice score of 57.7%, which offers at least
an 11.8 % improvement when compared to state-of-the-art instrument detection
methods. Due to the improved segmentation performance by the DF-FCN, the
catheter can be localized with an error of only 1.4 mm.Comment: ISBI 2019 accepte
Conditional Transfer with Dense Residual Attention: Synthesizing traffic signs from street-view imagery
Object detection and classification of traffic signs in street-view imagery
is an essential element for asset management, map making and autonomous
driving. However, some traffic signs occur rarely and consequently, they are
difficult to recognize automatically. To improve the detection and
classification rates, we propose to generate images of traffic signs, which are
then used to train a detector/classifier. In this research, we present an
end-to-end framework that generates a realistic image of a traffic sign from a
given image of a traffic sign and a pictogram of the target class. We propose a
residual attention mechanism with dense concatenation called Dense Residual
Attention, that preserves the background information while transferring the
object information. We also propose to utilize multi-scale discriminators, so
that the smaller scales of the output guide the higher resolution output. We
have performed detection and classification tests across a large number of
traffic sign classes, by training the detector using the combination of real
and generated data. The newly trained model reduces the number of false
positives by 1.2 - 1.5% at 99% recall in the detection tests and an absolute
improvement of 4.65% (top-1 accuracy) in the classification tests.Comment: The first two authors have equal contribution. Accepted at
International Conference on Pattern Recognition 2018 (ICPR
- …