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In the EEG signals, information is contained in a narrow frequency band. How does one selects the number of subbands either overlapping or non-overlapping and its bandwidth in context with the EEG signals is a key problem in the subband BSS. The authors propose a novel algorithmic approach to estimate the number of subbands and its bandwidth and applied to movement imagery classification. To ensure...
Mobile vision has attracted increasing research attention recently. A fast and accurate face/eye detector can enable manymobile vision applications. Most ofmobile face detections are close-range face detection. In such a scenario only a limited number of scanning windows are required. Thus the speed is less demanding but the detection accuracy is of higher importance. We further discover that for...
Covariance matrices provide compact, informative feature descriptors for use in several computer vision applications, such as people-appearance tracking, diffusion-tensor imaging, activity recognition, among others. A key task in many of these applications is to compare different covariance matrices using a (dis)similarity function. A natural choice here is the Riemannian metric corresponding to the...
Data from lightning detection networks is often used for forensic purposes: to validate insurance claims or even to determine the cause of death. Stroke location and current estimates are subject to measurement error, and the dilution of precision is reported in terms of a median confidence ellipse and χ2 distribution. A method is presented to derive the probability density function from the reported...
In this paper we investigate the classification performance of the compact polarimetric interferometric SAR (C-PolInSAR). The stressed compact modes are π/4 mode and CTLR mode, due to DCP mode equivalent to CTLR mode in theory. First, we provide a state-of-art of the C-PolInSAR modes, and present the different reconstruction algorithms aiming at recovering the full PolInSAR information from the observed...
In order to get better semantic annotation performance, block-global features are extracted as low-level visual features for image semantic annotation. Specifically, wellknown global feature extraction method, namely two-dimensional principal component analysis (2DPCA) is applied to extract the image block-global features. Unlike typical image annotation methods which use local features or global...
A new method for georegistering motion imagery has recently been introduced. It requires a digital elevation model (DEM) from which it generates and registers predicted images to actual images. For aerial imagery, including wide area motion imagery (WAMI) and full motion video (FMV), the method fits a multi-parameter camera model composed of exterior and interior orientation parameters including radial...
This paper addresses two issues related to the detection of hyperspectral anomalies. The first issue is the evaluation of anomaly detector performance even when labeled data is not available. The second issue is the estimation of the covariance structure of the data in local detection methods, such as the RX detector, when the number of available training pixels n is not much larger than (and may...
This paper addresses the problem of determining the optimal robot trajectory for localizing a robot follower in a leader-follower formation using robot-to-robot distance or bearing measurements. In particular, maintaining a perfect formation has been shown to reduce the localization accuracy (as compared to moving randomly), or even leads to loss of observability when only distance or bearing measurements...
Network Intrusion Detection Systems (NIDS) monitor internet traffic to detect malicious activities including but not limited to denial of service attacks, network accesses by unauthorized users, attempts to gain additional privileges and port scans. The amount of data that must be analyzed by NIDS is too large. Prior studies developed feature selection and feature extraction techniques to reduce the...
This paper proposes a modified two-class LDA based compound distance for similar handwritten Chinese characters discrimination. First the definition of the Intersecting Subspace (IS) between two classes and the modified between-class scatter matrix is given. Then we prove that the modified between-class scatter matrix can supply additional information. Our experiments demonstrate that the additional...
This paper presents a new approach for logo detection exploiting contour based features. At first stage, pre-processing, contour detection and line segmentation are done. These processes result in set of Outer Contour Strings (OCSs) describing each graphics and text parts of the documents. Then, the logo detection problem is defined as a region scoring problem. Two types of features, coarse and finer...
Simultaneous localization and mapping (SLAM) is one of the challenging issues in recent decades. In this paper solving vision based SLAM problem using Kalman filters family have been provided. It is focused on mobile robot equipped with stereo vision sensor which moves in an indoor environment. The mobile robot navigated among the landmarks which were detected by scale invariant feature transform...
Brain Computer Interface is the communication channel between the brain and the computer for recording of electrical activity along the scalp produced by the firing of neurons within the brain. The brain signals which are also known as Electroencephalography (EEG) can be used to direct and control some external activity. This work reports a methodology for acquisition and detection and of EEG signals,...
In this paper we propose a pedestrian detection algorithm and its implementation on a Xilinx Virtex-4 FPGA. The algorithm is a sliding window-based classifier, that exploits a recently designed descriptor, the covariance of features, for characterizing pedestrians in a robust way. In the paper we show how such descriptor, originally suited for maximizing accuracy performances without caring about...
Mutual information is a criterion widely used in statistical language modeling word associations and feature selection. Principle component analysis (PCA) is a statistical technique for unsupervised dimension reduction. K-means clustering is commonly used data clustering for unsupervised learning tasks. In this paper, we first select features from native feature space by mutual information to reduce...
Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously...
Pressure ulcer is an age-old problem imposing a huge cost to our health care system. Detecting and keeping record of the patient's posture on bed, help care givers reposition patient more efficiently and reduce the risk of developing pressure ulcer. In this paper, a commercial pressure mapping system is used to create a time-stamped, whole-body pressure map of the patient. An image-based processing...
In order to implement affective computing, there have been several studies to elicit human emotion using audio and video stimuli or by recalling previous events. Taste-elicited emotion has also been investigated using food to induce different levels of pleasure. This is monitored using a range of methods, from questionnaire feedback to electrophysiological responses of autonomic nervous system (ANS)...
Due to the pervasive deployments of mobile communication technologies, vehicle positioning and tracking by locating the driver's mobile phones has become feasible. However, no single positioning method can provide decent tradeoff between accuracy and coverage. To address this issue, we propose a Kalman filter-based hybrid method which can track the mobile phones traveling on-board vehicles. The proposed...
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