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 the clinical diagnosis of facial dysmorphology, geneticists attempt to identify the underlying syndromes by associating facial features before cyto or molecular techniques are explored. Specifying genotype-phenotype correlations correctly among many syndromes is labor intensive especially for very rare diseases. The use of a computer based prediagnosis system can offer effective decision support...
Deep structure learning is a promising new area of work in the field of machine learning. Previous work in this area has shown impressive performance, but all of it has used connectionist models. We hope to demonstrate that the utility of deep architectures is not restricted to connectionist models. Our approach is to use simple, non-connectionist dimensionality reduction techniques in conjunction...
The work presented in this paper proposes a new approach of using subspace grids for recognizing patterns in multidimensional data. The proposed approach addresses the two problems often associated with this task: i) curse of dimensionality ii) cases with small sample sizes. To handle the curse of dimensionality problem, this paper introduces subspace grids and shows how it can be employed for pattern...
Occluded face recognition is one the most interesting problems of applied computer vision. Among many face recognition approaches, the Nonnegative Matrix Factorization (NMF) turns out to be one of the popular techniques especially for part-based learning. It aims to factorize a nonnegative data matrix into two nonnegative matrices and obtains a well approximated product using an objective function...
Temporal evolution in the generative distribution of nonstationary sequential data is challenging to model. This paper presents a method for retaining the information in prior distributions of matrix variate dynamic linear models (MVDLMs) as the eigenspace of sequential data evolves. The method starts by constructing sliding windows â" matrices composed of a fixed number of columns...
This paper describes a nonlinear face recognition method based on polynomial spatial frequency image processing. This nonlinear method is known as the polynomial distance classifier correlation filter (PDCCF). PDCCF is a member of a well-known family of filters called correlation filters. Correlation filters are attractive because of their shift invariance and potential for distortion tolerant pattern...
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.