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This paper introduces the concept of using a performance measure to select band groups when using a single classifier. Typically, band groups are selected using a proximity measure to determine the similarity or dissimilarity of hyperspectral bands. The problem with that approach is the similarity or dissimilarity of the hyperspectral bands may not be important for some problems. The novelty of using...
In this paper an easy-to-use approach using cubic spline interpolation is proposed for providing initial estimations for digital image correlation (DIC) grid point locations and rotations on objects with major deflection. One of the main problems in using DIC for deflection measurements is that direct correlation fails since image sample rotations can differ considerably across two images. The proposed...
In recent power grid systems, data-driven approach has been taken to grid condition evaluation and classification after successful adoption of big data techniques in internet applications. However, the raw training data from single monitoring system, e.g. dissolved gas analysis (DGA), are rarely sufficient for training in the form of valid instances and the data quality can rarely meet the requirement...
In this paper we introduce an object-based change detection model using correlation analysis and classification. First we use eCognition to obtain an over-segmentation map. Then linear regression is used to gain three unique types of parameters — regression coefficient, offset, and correlation coefficient which can provide valuable information about the location and numeric change value derived within...
This paper presents the usage of Extreme Learning Machines for cancer microarray gene expression data. Extreme Learning Machines overcomes the problems of overfitting, local minima and improper training rate that are most common in traditional algorithms. We have evaluated the binary classification performance of Extreme Learning Machines on five bench marked datasets of cancer microarray gene expression...
In this paper, a novel naïve Bayesian classifier based on the hybrid-weight feature attributes (short of "NBCHWFA") is proposed. NBCHWFA arranges a hybrid weight for each feature attribute by merging the effectiveness of feature attribute on classification and the dependence between feature attribute and class attribute. In order to demonstrate the feasibility and effectiveness of proposed...
Tracking sports motion is a challenging task. This paper presents a comparison of different template matching methods that can be used in such motion tracking applications. Six methods were tested with dynamic and still background conditions. Their performance is analyzed using different video sequences obtained by considering sports such as table tennis, weightlifting, 100m athletics and high jump...
This paper addresses detecting anomalies of individual services from their total resource usage on web-based system. Because the total resource usage is a linear combination of the number of accesses to each service, multiple regression analysis can be applied to estimate a resource usage per an access to each service as regression coefficient. However, the regression coefficients differ from the...
Voice is one of the primary biometrics and can be used to identify a person. By comparing the similarity of two spectrograms, one is able to investigate if the two voice samples were originated from a same speaker. In this paper a method to normalize the spectrogram of a given voiced sound is presented. Through this normalization process, the differences between the spectrograms of two voice samples...
By analyzing the features of general digital image and calculating the deviation of epipolar line, a correlation coefficient matching algorithm is proposed. In this paper, we make a summery of the particularity of general digital stereo, analysis the deviation of the corresponding epipolar line, and provide a matching method under the epipolar line constraint. It is proved by experiments that this...
The problem of detecting overlapped speech in stereo recordings using close-talk microphones is important for a variety of applications including the identification of back-channels, interruptions etc. in a dyadic or multi-party interactions. For detecting overlapped speech, we propose a feature derived using the spectral similarity of two channels over a range of acoustic frames. During overlapped...
Monitoring urban growth and change is an important issue for urban planning and disaster management. In this study, three temporal TerraSAR-X images are used to monitor the urban changes. The difference and correlation coefficient between two images are calculated with a sliding window. Then a new factor that composites the difference and correlation coefficient is proposed to detect the changes....
A novel blind separation algorithm based on double compression for single-channel JPEG permuted image was proposed in this paper. Firstly, the permuted image was compressed again, and then the primary compression factors were estimated by calculating the correlation coefficients of image blocks in pre and post recompression. Secondly, a `mapping space' was constructed based on the primary compression...
Simplified Silhouette Filter (SSF) is a recently introduced feature selection method that automatically estimates the number of features to be selected. To do so, a sampling strategy is combined with a clustering algorithm that seeks clusters of correlated (potentially redundant) features. It is well known that the choice of a similarity measure may have great impact in clustering results. As a consequence,...
Non-wood forest is a kind of important forest resource. This paper focused on the information extraction of non-wood forest based on Advanced Land Observation Satellite (ALOS) data. Band characteristics were analyzed to get understanding of this data wholly by information content, correlation coefficient and Optimum Index Factor (OIF). A new set of data with eight bands were obtained by the fusion...
Correlation is a very widely used filter criterion for gene selection in cancer classification. However, it uses all the training samples in ranking, which may not be equally important for the classification. Using support vectors, we demonstrate that classical correlation coefficient based gene selection is biased because of the sample points away from classification margin. To remove such bias,...
Distinct features play a vital role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, numerous parameters, such as separable frequency bands, data acquisition channels and time point of maximum separability are chosen explicit to each subject...
A general epipolar line constraint equation for binocular stereo vision is first established for the measurement of workpiece surface, in which the intrinsic parameters of two cameras and their extrinsic structural parameters with respect to a reference base are included. To overcome the drawback of the relative distortion between left image and right image, An epipolar line rectification technique...
In this study we have developed a limited lead system to allow reconstruction of the 12-lead ECG. This has been based upon the analysis of eigen-vectors calculated from body surface potential map (BSPM) data. Eigenvectors were calculated from a set of 117 lead BSPMs (normal=172, LVH=178, MI=209). The extrema of the first three eigen-vectors were used to determine the positions of recording sites for...
Traditionally, the sewer inspection usually discovers sewer failures on numerous CCTV images by human interpretation. However, it remains to be improved in both consideration of economic and efficient due to humanpsilas fatigue and subjectivity. To enhance the sewer inspection approaches, this paper attends to employ artificial intelligence into image process to extract the failure features of the...
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