Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. In this paper, we ...
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Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. In this paper, we argue that tracking may be regarded as one-class problem, which avoids gathering limited negative samples for background description. Inspired by the fact the positive feature space generated by One-Class SVM is bounded by a closed sphere, we propose a novel tracking method utilizing One-Class SVMs that adopt HOG and 2bit-BP as features, called One-Class SVM Tracker (OCST). Simultaneously an efficient initialization and online updating scheme is also proposed. Extensive experimental results prove that OCST outperforms some state-of-the-art discriminative tracking methods on providing accurate tracking and alleviating serious drifting.
In this paper, the Harmony Search (HS)-based BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can...
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In this paper, the Harmony Search (HS)-based BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Two HS methods, the original version and a new variation recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training.
Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectu...
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Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate.
This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the larges...
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In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks ...
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In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained using a back propagation algorithm in combination with a least squares method. However, the training method sometimes may lead to local optima. We here propose a new strategy of hybrid training algorithm based on the fusion of the ANFIS and Harmony Search (HS), HS-ANFIS, which is adopted to tune all the parameters of the ANFIS. The validity of our method is verified by numerical experiments.
In this paper, we experimentally evaluate three different averaging methods for processing of electroencephalogram (EEG) event related potentials (ERPs) measured from scalp in response to repeated stimulus. In ERP app...
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In this paper, we experimentally evaluate three different averaging methods for processing of electroencephalogram (EEG) event related potentials (ERPs) measured from scalp in response to repeated stimulus. In ERP applications, arithmetic mean (AM) is normally employed in processing the ERPs prior to ERP detection, whereas also other averaging methods might have beneficial properties. Fast ERP detection is essential, for example, in brain computer interfaces and during spine surgery. Thus, it is of interest to search for methods to aid in detecting ERPs with as few stimulus repetitions as possible. Here, noise reduction properties of AM, geometric mean (GM), and harmonic mean (HM) are demonstrated with simulations, and ERP processing by the three methods is illustrated by processing real visual evoked potentials (VEPs).
A hybrid method is used to evaluate atherosclerosis through a mathematical morphology approach and GVF-Snake method. Common carotid artery (CCA) segmentation requires outlining the intima and adventitia contours on th...
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A hybrid method is used to evaluate atherosclerosis through a mathematical morphology approach and GVF-Snake method. Common carotid artery (CCA) segmentation requires outlining the intima and adventitia contours on the transverse view of B-mode ultrasound (US) images. The lumen and adventitia contours are segmented using a morphology and GVF-Snake methods, respectively. Upon analyzing ten separate patient data sets demonstrate that a comparison between the proposed method and the traditional approach (manual contouring) on 110 transverse images of the CCA showed a mean absolute distance (MAD) of 0.67±0.17mm for lumen and 0.64 ± 0.19mm for adventitia. Their Dice Similarity Coefficient (DSC) values are 92.7%±2.3% and 90.3%±3.5% for lumen and adventitia segmentation, respectively. These values are in good agreement with clinical standards.
Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high...
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We prove that Fv(3,5;6) = 16, which solves the smallest open case of vertex Folkman numbers of the form Fv(3,k;k + 1). The proof uses computer algorithms.
We prove that Fv(3,5;6) = 16, which solves the smallest open case of vertex Folkman numbers of the form Fv(3,k;k + 1). The proof uses computer algorithms.
image annotation is the process of assigning proper keywords to describe the content of a given image, which can be regarded as a problem of multi-object image classification. In this paper, a general multi-label anno...
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