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.
Automatic image retrieval similar to a query image is an important task in computer vision. To obtain this goal, every image in database needs to be modeled. A signature is learned for each image and stored. A sparsity based image content modeling and retrieval is proposed in this paper. Sparsity based data modeling has been successfully applied across various areas of image processing. A dictionary...
Texture detection plays an important role in many computer vision tasks. Application ranges from finding an object in satellite images to anomaly detection in medical imaging. Recently sparse representation based texture modeling and detection scheme is proposed. One major drawback using such a generative data modeling is the time taken for texture detection. In this paper we propose and investigate...
Texture identification is an important preliminary step in many computer vision applications. There exists supervised and unsupervised approaches to solve this problem. One of the widely used unsupervised technique is KMeans which identifies the region of image based on clustering. The disadvantage of the KMeans technique is that it is an off-line approach that needs all the data prior to processing...
Face detection is an important and challenging task in many computer vision applications. Signal processing using sparse framework has seen much interest in various areas in the recent past. In this paper, we propose a sparse framework based methodology to model a human face using very few training faces. We propose to use SIFT, LBP and RGB based feature vectors to model and detect the face in the...
Region segmentation is an important and challenging task. The applications range from tumour detection in medical imaging, computer aided surveillance, object location, pattern separation etc. Sparsity based data modelling in recent times have produced state of the art results in many image processing tasks. In this paper, we propose a semi-interactive region segmentation in sparse framework. Proper...
In recent times sparse framework based signal modelling has been extensively used in various signal processing tasks. State-of-the-art results have been obtained using this approach in various image processing applications. In this paper, we have adopted the sparse framework for the task of detection of various facial regions such as eyes, lips, nose etc. We propose a scheme for modelling these regions...
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.