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.
To achieve a good performance for shape classification, it requires both shape representation and classifier. In this paper, the so-called Eigen Barycenter Contour (EBcC) and Fisher Barycenter Contour (FBcC) techniques are presented for 2D shape classification. The representation utilizes the area of triangles at different scale level of Barycenter Contour (BcC). However, it is not invariant to starting...
In this paper, the algorithm for 2D shape matching and retrieval is developed by using Fisher Barycenter Contour (FBcC). First, the shape is represented into 3D format using the signed enclosed area at each scale level of Barycenter Contour (BcC). Because of high dimension of the feature representation, the eigen Barycenter Contour (EBcC) is applied for dimensionality reduction. Then, the Fisher Barycenter...
In this paper, we present a one dimensional descriptor for the two dimensional object silhouettes associated with each level of barycenter contour for multiple views shape matching and retrieval. Firstly, the barycenter contour is applied onto the shape contour. Then the averaging multi-triangle area representation (AMTAR) at each level of barycenter contour is computed as the shape descriptor. Finally,...
In this paper, the principal component analysis (PCA) for multi-view shape recognition is proposed. Our algorithm presents the signed enclosed area signature as the shape representation. In our method, the barycenter contour is used for decomposing the shape boundary into multiscale level. At each scale level, the signed enclosed area signatures are obtained. After that, the principal component analysis...
In this paper, a new multiresolution created from multi-level of barycenter contour is proposed in order to reduce the moderate amount of noise and to improve the retrieval efficiency of the recognition task in computer vision. Then, the triangle area representation with two points (TAR-2p) signature at each level of barycenter contour is introduced as the shape representation. Finally, the normalized...
Several shape recognition systems based on pairwise shape matching technique have achieved high accuracy but they face a problem of time consumption when they are evaluated on a large database. So this drawback makes the system impractical for real-time applications. Motivated by this obstacle, we have investigated a novel and robust neural network solution to achieve high speed of shape recognition...
Shape recognition is an important part of machine intelligence in both decision making and data processing. A good shape representation in shape recognition should describe the shape in the way that makes it distinguishable from other shapes and be invariant to transform of position, size, angle and skew. More importantly, developing and finding appropriate shape representation are still a challenging...
Fingerprints is the most popular biometric modality. Fingerprint features include core, delta, ridge bifurcation, ridge ending, enclosure and short edge. In order to increase the performance of fingerprint identification system, it is essential that these directional-related features are needed to be enhanced. In this paper, we purpose the directional filter bank to enhance the fingerprint features...
This research presents new algorithm for signature verification using N-tuple learning machine. The features are taken from handwritten signature on digital tablet (on-line). This research develops recognition algorithm using four features extraction, namely horizontal and vertical pen tip position (x-y position), pen tip pressure, and pen altitude angles. Verification use s N-tuple technique with...
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.