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.
In this paper, we propose new Fourier-statistical features (FSF) in RGB space for detecting text in video frames of unconstrained background, different fonts, different scripts, and different font sizes. This paper consists of two parts namely automatic classification of text frames from a large database of text and non-text frames and FSF in RGB for text detection in the classified text frames. For...
We propose novel algorithms for organizing large image and video datasets using both the visual content and the associated side-information, such as time, location, authorship, and so on. Earlier research have used side-information as pre-filter before visual analysis is performed, and we design a machine learning algorithm to model the join statistics of the content and the side information. Our...
Image attention is the basic technique for many computer vision applications. In this paper, we propose an adaptive Bayesian framework to detect the image attention in color image. Firstly, three simple semantics and subtractive clustering are used to construct attention Gaussians mixture model (AGMM) and background Gaussians mixture model (BGMM). Secondly, the Bayesian framework is utilized to classify...
We propose semantic texton forests, efficient and powerful new low-level features. These are ensembles of decision trees that act directly on image pixels, and therefore do not need the expensive computation of filter-bank responses or local descriptors. They are extremely fast to both train and test, especially compared with k-means clustering and nearest-neighbor assignment of feature descriptors...
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.