525 research outputs found
Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images
This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time
Performance of Unsupervised Change Detection Method Based on PSO and K-means Clustering for SAR Images
This paper presents unsupervised change detection method to produce more accurate change map from imbalanced SAR images for the same land cover. This method is based on PSO algorithm for image segmentation to layers which classify by Gabor Wavelet filter and then K-means clustering to generate new change map. Tests are confirming the effectiveness and efficiency by comparison obtained results with the results of the other methods. Integration of PSO with Gabor filter and k-means will providing more and more accuracy to detect a least changing in objects and terrain of SAR image, as well as reduce the processing time
Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
Hand motion capture is a popular research field, recently gaining more
attention due to the ubiquity of RGB-D sensors. However, even most recent
approaches focus on the case of a single isolated hand. In this work, we focus
on hands that interact with other hands or objects and present a framework that
successfully captures motion in such interaction scenarios for both rigid and
articulated objects. Our framework combines a generative model with
discriminatively trained salient points to achieve a low tracking error and
with collision detection and physics simulation to achieve physically plausible
estimates even in case of occlusions and missing visual data. Since all
components are unified in a single objective function which is almost
everywhere differentiable, it can be optimized with standard optimization
techniques. Our approach works for monocular RGB-D sequences as well as setups
with multiple synchronized RGB cameras. For a qualitative and quantitative
evaluation, we captured 29 sequences with a large variety of interactions and
up to 150 degrees of freedom.Comment: Accepted for publication by the International Journal of Computer
Vision (IJCV) on 16.02.2016 (submitted on 17.10.14). A combination into a
single framework of an ECCV'12 multicamera-RGB and a monocular-RGBD GCPR'14
hand tracking paper with several extensions, additional experiments and
detail
Combining Differential Kinematics and Optical Flow for Automatic Labeling of Continuum Robots in Minimally Invasive Surgery
International audienceThe segmentation of continuum robots in medical images can be of interest for analyzing surgical procedures or for controlling them. However, the automatic segmentation of continuous and flexible shapes is not an easy task. On one hand conventional approaches are not adapted to the specificities of these instruments, such as imprecise kinematic models, and on the other hand techniques based on deep-learning showed interesting capabilities but need many manually labeled images. In this article we propose a novel approach for segmenting continuum robots on endoscopic images, which requires no prior on the instrument visual appearance and no manual annotation of images. The method relies on the use of the combination of kinematic models and differential kinematic models of the robot and the analysis of optical flow in the images. A cost function aggregating information from the acquired image, from optical flow and from robot encoders is optimized using particle swarm optimization and provides estimated parameters of the pose of the continuum instrument and a mask defining the instrument in the image. In addition a temporal consistency is assessed in order to improve stochastic optimization and reject outliers. The proposed approach has been tested for the robotic instruments of a flexible endoscopy platform both for benchtop acquisitions and an in vivo video. The results show the ability of the technique to correctly segment the instruments without a prior, and in challenging conditions. The obtained segmentation can be used for several applications, for instance for providing automatic labels for machine learning techniques
Living analytics methods for the social web
[no abstract
Adversarial Machine Learning in Network Intrusion Detection Systems
Adversarial examples are inputs to a machine learning system intentionally
crafted by an attacker to fool the model into producing an incorrect output.
These examples have achieved a great deal of success in several domains such as
image recognition, speech recognition and spam detection. In this paper, we
study the nature of the adversarial problem in Network Intrusion Detection
Systems (NIDS). We focus on the attack perspective, which includes techniques
to generate adversarial examples capable of evading a variety of machine
learning models. More specifically, we explore the use of evolutionary
computation (particle swarm optimization and genetic algorithm) and deep
learning (generative adversarial networks) as tools for adversarial example
generation. To assess the performance of these algorithms in evading a NIDS, we
apply them to two publicly available data sets, namely the NSL-KDD and
UNSW-NB15, and we contrast them to a baseline perturbation method: Monte Carlo
simulation. The results show that our adversarial example generation techniques
cause high misclassification rates in eleven different machine learning models,
along with a voting classifier. Our work highlights the vulnerability of
machine learning based NIDS in the face of adversarial perturbation.Comment: 25 pages, 6 figures, 4 table
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