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The moving blocker method is economic and effective for scatter correction (SC) of cone-beam computed tomography (CBCT). However, at the regions with large intensity transition in the projection images in the axial blocker moving direction, the estimation of scatter signal from blocked regions in a single projection view can produce large errors, which can cause significant artifacts in reconstructed...
Robust estimation of linear structures such as edges and junction features in digital images is an important problem. In this paper, we use an adaptive robust structure tensor (ARST) method where the local adaptation process uses spatially varying adaptive Gaussian kernel that is initialized using the total least-squares structure tensor solution. An iterative scheme is designed with size, orientation,...
Detecting and localizing insulator plays a vital role in any power line monitoring system. In this work, we present a novel method for rotation invariant insulator detection. Rotation invariance is achieved by an efficient approach for estimating rotation angle of all insulator of an image. Sliding window based local directional pattern (LDP) feature is extracted from the image and support vector...
Identifying and detecting the unknown abnormal sparse signal has become an important issue for distributed networks. In this paper, we proposed a new detection scheme based on convex optimization for wireless sensor networks. Under the Neyman-Pearson testing framework, the detection scheme first estimates the unknown signal by employing the convex optimization at the fusion center. Then the sensor...
In this paper we tackle the ℋ∞ filtering problem for a discrete-time Markov Jump Linear System (MJLS) with hidden parameters. We consider a hidden Markov model (θ(k); θ̂(k)) in which θ(k) is not accessible and corresponds to the mode of operation of the system, while θ̂(k) is a signal coming from a detector. The signal θ̂(k) acts as an estimation of θk) and is the observable part of the hidden Markov...
In this paper we focus on the problem of pedestrian detection in low visibility conditions, with infrared cameras. Widely applied, tracking is essential for driving assistance applications, providing support for removing false positives and forcing the detection of border line true positives. We propose a multiple feature and temporal based pedestrian detector for far-infrared images. Our model benefits...
In this paper, a noise estimation method is proposed and examined for spectrum sensing purposes. The proposed method is based on the use of the first Intrinsic Mode Function (IMF), which is the output of the Empirical Mode Decomposition (EMD). This technique is blind and a priori knowledge of the received signal characteristics are not required. Two well known noise-dependent spectrum sensing techniques,...
Multi-object tracking is a difficult problem underlying many computer vision applications. In this work, we focus on sediment transport experiments in a flow were sediments are represented by spherical calibrated beads. The aim is to track all beads over long time sequences to obtain sediment velocities and concentration. Classical algorithms used in fluid mechanics fail to track the beads over long...
This paper presents a fast algorithm for deriving the defocus map from a single image. Existing methods of defocus map estimation often include a pixel-level propagation step to spread the measured sparse defocus cues over the whole image. Since the pixel-level propagation step is time-consuming, we develop an effective method to obtain the whole-image defocus blur using oversegmentation and transductive...
Two sequential camera fingerprint detection methods are proposed. Sequential tests implement a log-likelihood ratio test in an incremental way, thus enabling a reliable decision with a minimal number of observations. One of our methods adapts Goljan et al.'s to sequential operation. The second, which offers better performance in terms of average number of test observations, is based on treating the...
Human detection in RGB-D images is an important yet very challenging task in computer vision. In this paper, we propose a novel human detection approach in RGB-D images, which integrates ROI (region-of-interest) generation, depth-size relationship estimation and a human detector. Our approach has the following advantages: 1) ROI generation and depth-size relationship estimation take full advantage...
Despite significant progress in pedestrian detection has been made in recent years, detecting pedestrians in crowded scenes remains a challenging problem. In this paper, we propose to use visual contexts based on scale and occlusion cues from detections at proximity to better detect pedestrians for surveillance applications. Specifically, we first apply detectors based on full body and parts to generate...
Non-Gaussian noises usually fail many conventional and effective signal detection techniques including the energy detector and the eigenvalue-based detector. The fractional lower order moment (FLOM) based detector has proved to be useful for unknown stochastic signal detection in α-stable distributed noises. However, the fixed exponent prevents the improvement of its performance. This paper presents...
Nowadays railway vehicle speed sensors suffer from insufficient measurement accuracy. E. g. the Doppler radar is prone to adverse weather conditions while wheel speed sensors are not sufficiently robust against wheel slip and wheel wear. However, since velocity sensors are safety relevant components, it becomes clear that conventional sensors are not able to cover all the requirements for an everyday...
In this paper, the security issue is investigated for networked control systems (NCSs) where the physical plant is controlled by a remote observer-based controller. The communication channel from system measurement to remote control centre is vulnerable to attacks from malicious adversaries. Here, false data injection (FDI) attacks are considered. The aim is to find the so-called insecurity conditions...
Spectrum sensing is one of the key technologies in cognitive radio system. Sensitivity to noise uncertainty is a fundamental limitation of current spectrum sensing strategies in detecting the presence of primary users in cognitive radio. Because of noise uncertainty, the detection performance of traditional detectors such as energy detector, matched filter and even cyclostationary detectors deteriorate...
The detection of heartbeat is an important and challenging issue for health care. This work proposes to estimate the QRS complex parameters based on the maximum-likelihood (ML) principle. To this goal, a new signal model and its Bayesian framework are studied. Detectors or estimators based on the Bayesian framework are considered to be optimal in the statistical signal processing point of view. To...
The motion dynamics and geometric information are considered to be one of the most useful features for infrared (IR) targets recognition. Especially for the exo-atmospheric target, when a target undergoes micro-motion dynamics in the outer space, such as mechanical vibrations or rotations, it would induce amplitude modulations on signature of target projected area along the Line-of-Sight (LOS) of...
This paper presents an integrated approach to resource utilization and security concern for wireless communication. In wireless communication, during the exchange of data, allocated spectrum and its utilization over a wireless channel plays an important role in providing efficient communication service in such network. In the need towards optimal resource utilization in wireless communication, a spectrum...
In this paper the concept of Bayesian Networks (BN) is applied to the problem of traffic data acquisition by data fusion. Two wireless communication based sensors are used as data sources: IEEE 802.15.1 Bluetooth and IEEE 802.11p V2X (vehicle to vehicle and vehicle to infrastructure). Via V2X so called cooperative awareness messages (CAM) are received, which provide information on vehicle location...
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