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.
Using mean curvature (H) and Gaussian curvature (K) values or shape index (S) and curvedness (C) values, HK and SC curvature spaces are constructed in order to classify surface patches into types such as pits, peaks, saddles etc. Since both HK and SC curvature spaces classify surface patches in to similar types, their classification capabilities are comparable. Previously, HK and SC curvature spaces...
Although they are orientation invariant, mean (H) and Gaussian (K) curvature values are essentially variant under scale and resolution changes. In order to overcome this fact, in this study, scale-spaces of the 3D surface and the curvature values are constructed. Then features with their scale information are sought within the scale-space. Thus, different from previous studies, H and K curvature values...
Using transform invariant 3D features obtained from a database of 3D range images, geometric hashing is applied for the purpose of 3D object recognition. Mean (H) and Gaussian (K) curvature values within a scale-space of the surface is used. Since H and K values are used and a scale-space of the surface is constructed the method is independent of transformation and resolution. The method is tested...
Most 3D object recognition methods use mean-Gaussian curvatures (HK) or shape index-curvedness (SC) values for classification. Although these two curvature descriptions classify objects into same categories, their mathematical definitions vary. In this study a comparison between the two curvature description is carried out for the purpose of 3D object recognition. Since unlike S; H, K and C values...
The data acquired by 3D face scanners have distortions such as spikes, holes and noise. Enhancement of 3D face data by removing these distortions while keeping the face features is important for the applications using these data. In this study, thresholding is used for removing spikes, thresholding together with face symmetry is used for hole filling and bilateral filtering is used for smoothing and...
Finding slope units for a given watershed is an important task before analyzing landslide susceptibility. Usually, slope unit generation requires integration of several digital elevation model (DEM)-based outputs obtained from GIS and related hydrological software. Therefore, it is time consuming due to involved steps and compilation of various software outputs. In this paper a DEM-based, scale and...
In this paper, the 3D face scanner that we developed using stereo cameras and structured light together is presented. Structured light having a pattern of vertical lines is used to create feature points and to match them easily. 3D point cloud obtained by stereo analysis is post processed to obtain the 3D model in obj format.
In this study a representation using scale and invariant generic 3D features, for 3D facial models is proposed. These generic feature vectors obtained from descriptive parts of the face like eyes, nose, or nose saddle, are then convolved into a graphical model where a characteristic topology for a 3D facial model representation is achieved. These scale and invariant 3D features are determined by using...
In this paper we present a physically-based 3D facial skin model based on biomechanical properties of human facial anatomy. The skin model is mainly formed of two DNURBS surfaces as a two layered membrane structure. The model represents the soft tissue layer which covers the skull and is attached to the skull via muscle insertion points. The model is fitted on digitally scanned 3D facial surfaces...
An algorithm is proposed for 3D object representation using generic 3D features which are transformation and scale invariant. Descriptive 3D features and their relations are used to construct a graphical model for the object which is later trained and then used for detection purposes. Descriptive 3D features are the fundamental structures which are extracted from the surface of the 3D scanner output...
An algorithm is proposed to extract transformation and scale invariant 3D fundamental elements from the surface structure of 3D range scan data. The surface is described by mean and Gaussian curvature values at every data point at various scales and a scale-space search is performed in order to extract the fundamental structures and to estimate the location and the scale of each fundamental structure...
3D face modeling based on real images is one of the important subjects of Computer Vision that is studied recently. In this paper the study that we conducted in our Computer Vision and Intelligent Systems Research Laboratory on 3D face model generation using uncalibrated multiple still images is explained
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.