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.
Deep learning methods have been successfully used in many areas of computer vision, including super resolution. However, all of the previous deep learning methods have been proposed for generic image super resolution. In this paper, we proposed to use convolutional neural network for face hallucination (FH) by combining the domain specific prior knowledge of face images and properties of deep learning...
Numbers of neighbor embedding (NE) methods have been proposed, which use the image content metric based on the distance values such as Euclidean distance between the input image patch and the image patches in the training set to find the nearest neighbors. In contrast to these approaches we propose to use image content metric that uses the most effective singular values of the patch of interest. Singular...
This paper presents a method that combines the background subtraction with the Viola-Jones face detector to detect human faces from video sequence captured by a fixed camera. We use a texture based method for background subtraction to extract foreground sub-images for the face detector. It allows the detector to focus on face detection on smaller image regions and thereby reduces its computational...
Feature selection only using wrapper method in high-dimensional data space is always time-consuming. A new feature selection method, named fast static particle swarm optimization, is proposed for tackling this problem. It treats the whole initial feature set as a static particle swarm in which no new particle would be generated in high dimensional space, and the proposed method takes filter and wrapper...
In this paper, we propose an example-based facial sketch hallucination approach. Given a face image, its sketch image will be automatically hallucinated by learning from a training set, which includes a lot of face images and their corresponding sketch images. Our algorithm involves three stages. In the first stage which is called ??feature extracting??, we create the feature pyramid for each face...
Regularization plays a vital role in ill-posed problems. A properly chosen regularization can direct the solution toward a better quality outcome. An emerging powerful regularization is one that leans on image examples. In this paper, we propose a novel scheme for face hallucination. We target specially the quality of highly zoomed outputs. Our work bases on the pyramid framework and assigns several...
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.