The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Upper body detection is a challenging problem in practical application scenarios and shares all the difficulties of object detection. This paper focuses on the problems of multiple upper bodies detection in still images, including the diversity of appearances and a non-rigid human body. We present a new architecture for upper body detection using a Convolutional Neural Network (CNN). In this architecture,...
Regularization has been one of the most popular approaches to prevent overfitting in electroencephalogram (EEG) classification of brain–computer interfaces (BCIs). The effectiveness of regularization is often highly dependent on the selection of regularization parameters that are typically determined by cross-validation (CV). However, the CV imposes two main limitations on BCIs: 1) a large amount...
The training of SVM can be viewed as a Convex Quadratic Programming (CQP) problem which becomes difficult to be solved when dealing with the large scale data sets. Traditional methods such as Sequential Minimal Optimization (SMO) for SVM training is used to solve a sequence of small scale sub-problems, which costs a large amount of computation time and is hard to be accelerated by utilizing the computation...
In this letter, we present a novel approach for spectral-spatial classification in hyperspectral imagery. To this end, after applying principal component analysis (PCA) for dimensionality reduction, we extract the spectral-spatial information by first reorganizing the local image patch with the first d principal components(PCs) into a vector representation, followed by a sorting scheme to make it...
Bi-dimensional empirical mode decomposition (BEMD) has been one of the core activities in image processing. Unfortunately, this promising technique is sensitive to boundary effect. Here, a new technique based on multivariate grey model termed as GM(1, 3) is developed for boundary extension in BEMD. More specifically, pixel values and coordinates of the image are regarded as characteristic data series...
In this paper, an experiment was designed to get the electroencephalography (EEG) when people caught the vision of moving to different direction (right, left, front, back). Through Fourier Transform., the feature of the EEG was obtained. Then, the algorithm of principal component analysis (PCA) was used to simplify the feature. Finally, in order to classify the direction perception EEG, it was distinguished...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.