277,744 research outputs found
Constructing multi-layered boundary to defend against intrusive anomalies: an autonomic detection coordinator
© 2005 IEEE.An autonomic detection coordinator is developed in this paper, which constructs a multi-layered boundary to defend against host-based intrusive anomalies by correlating several observation-specific anomaly detectors. Two key observations facilitate the model formulation: First, different anomaly detectors have different detection coverage and blind spots; Second, diverse operating environments provide different kinds of information to reveal anomalies. After formulating the cooperation between basic detectors as a partially observable Markov decision process, a policy-gradient reinforcement learning algorithm is applied to search in an optimal cooperation manner, with the objective to achieve broader detection coverage and fewer false alerts. Furthermore, the coordinator’s behavior can be adjusted easily by setting a reward signal to meet the diverse demands of changing system situations. A preliminary experiment is implemented, together with some comparative studies, to demonstrate the coordinator’s performance in terms of admitted criteria.Zonghua Zhang, Hong She
Fully Automatic Ultrasound Fetal Heart Image Detection and Segmentation based on Texture Analysis
Ultrasound fetal heart image analysis is important for the antenatal diagnosis of congenital heart disease, therefore, design an automated fetal heart ultrasound image analysis approaches to improve detection ratio of congenital heart disease is necessary. Nevertheless, because of the complicated structure of fetal heart ultrasound image, location, detection and segmentation approaches of fetal heart images as interesting topics that get more attention. Therefore, in this work, we present a framework to segment ultrasound image automatically for tracking the boundary of fetal heart region. In the first step, this paper contributes to breed candidate regions. And then, in the segmentation progress, we apply an energy-based active contour model to detect the edges of fetal heart. Finally, in the experiment section, the performance is estimated by the Dice similarity coefficient, which calculate the spatial overlap between two different segmentation regions, and the experiment results indicate that the proposed algorithm achieves high levels of accuracy
Application of Machine Vision in UAVs for Autonomous Target Tracking
This research presents experimental results for the application of Machine Vision (MV) techniques to address the problem of target detection and tracking. The main objective is the design of a prototype UAV surveillance environment to emulate real-life conditions. The model environment for this experiment consists of a target simulated by a small electric train system, located at ground level, and a MV camera mounted on a motion-based apparatus located directly above the model setup. This system is meant to be a non-flying mockup of an aerial robot retrofitted with a MV sensor. Therefore, the final design is a two degree-of-freedom gantry simulating aircraft motions above the ground level at a constant altitude. On the ground level, the design of the landscape is an attempt to achieve a realistic natural landscape within a laboratory setting. Therefore, the scenery consists of small scale trees, bushes, a mountain, and a tunnel system within a 914 mm by 1066 mm boundary. To detect and track the moving train, MV algorithms are implemented in a Matlab/SimulinkRTM based simulation environment. Specifically, image pre-processing techniques and circle detection algorithms are implemented to detect and identify the chimney stack on the train engine. The circle detection algorithms analyzed in this research effort consists of a least squares based method and the Hough transform (HT) method for circle detection. The experimental results will show that the solution to the target detection problem could produce a positive detection rate of 90% during each simulation while utilizing only 56% of the input image. Tracking and timing data also shows that the least squares based target detection method performs substantially better then the HT method. This is evident from the result of using a 1--2 Hz frequency update rate for the SimulinkRTM scheme which is acceptable, in some cases, for use in navigation for a UAV performing scouting and reconnaissance missions. The development of vision-based control strategies, similar to the approach presented in this research, allows UAVs to participate in complex missions involving autonomous target tracking
Exploiting Contextual Information for Prosodic Event Detection Using Auto-Context
Prosody and prosodic boundaries carry significant information regarding linguistics and paralinguistics and are important aspects of speech. In the field of prosodic event detection, many local acoustic features have been investigated; however, contextual information has not yet been thoroughly exploited. The most difficult aspect of this lies in learning the long-distance contextual dependencies effectively and efficiently. To address this problem, we introduce the use of an algorithm called auto-context. In this algorithm, a classifier is first trained based on a set of local acoustic features, after which the generated probabilities are used along with the local features as contextual information to train new classifiers. By iteratively using updated probabilities as the contextual information, the algorithm can accurately model contextual dependencies and improve classification ability. The advantages of this method include its flexible structure and the ability of capturing contextual relationships. When using the auto-context algorithm based on support vector machine, we can improve the detection accuracy by about 3% and F-score by more than 7% on both two-way and four-way pitch accent detections in combination with the acoustic context. For boundary detection, the accuracy improvement is about 1% and the F-score improvement reaches 12%. The new algorithm outperforms conditional random fields, especially on boundary detection in terms of F-score. It also outperforms an n-gram language model on the task of pitch accent detection
WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network
Deep learning has driven a great progress in natural and biological image
processing. However, in material science and engineering, there are often some
flaws and indistinctions in material microscopic images induced from complex
sample preparation, even due to the material itself, hindering the detection of
target objects. In this work, we propose WPU-net that redesigns the
architecture and weighted loss of U-Net, which forces the network to integrate
information from adjacent slices and pays more attention to the topology in
boundary detection task. Then, the WPU-net is applied into a typical material
example, i.e., the grain boundary detection of polycrystalline material.
Experiments demonstrate that the proposed method achieves promising performance
and outperforms state-of-the-art methods. Besides, we propose a new method for
object tracking between adjacent slices, which can effectively reconstruct 3D
structure of the whole material. Finally, we present a material microscopic
image dataset with the goal of advancing the state-of-the-art in image
processing for material science.Comment: technical repor
Real-Time Salient Closed Boundary Tracking via Line Segments Perceptual Grouping
This paper presents a novel real-time method for tracking salient closed
boundaries from video image sequences. This method operates on a set of
straight line segments that are produced by line detection. The tracking scheme
is coherently integrated into a perceptual grouping framework in which the
visual tracking problem is tackled by identifying a subset of these line
segments and connecting them sequentially to form a closed boundary with the
largest saliency and a certain similarity to the previous one. Specifically, we
define a new tracking criterion which combines a grouping cost and an area
similarity constraint. The proposed criterion makes the resulting boundary
tracking more robust to local minima. To achieve real-time tracking
performance, we use Delaunay Triangulation to build a graph model with the
detected line segments and then reduce the tracking problem to finding the
optimal cycle in this graph. This is solved by our newly proposed closed
boundary candidates searching algorithm called "Bidirectional Shortest Path
(BDSP)". The efficiency and robustness of the proposed method are tested on
real video sequences as well as during a robot arm pouring experiment.Comment: 7 pages, 8 figures, The 2017 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2017) submission ID 103
A Meta-Theory of Boundary Detection Benchmarks
Human labeled datasets, along with their corresponding evaluation algorithms,
play an important role in boundary detection. We here present a psychophysical
experiment that addresses the reliability of such benchmarks. To find better
remedies to evaluate the performance of any boundary detection algorithm, we
propose a computational framework to remove inappropriate human labels and
estimate the intrinsic properties of boundaries.Comment: NIPS 2012 Workshop on Human Computation for Science and Computational
Sustainabilit
- …