Parsimonious Feature Extraction Methods: Extending Robust Probabilistic Projections with Generalized Skew-t
نویسندگان
چکیده
منابع مشابه
Extraction methods of voicing feature for robust speech recognition
In this paper, three different voicing features are studied as additional acoustic features for continuous speech recognition. The harmonic product spectrum based feature is extracted in frequency domain while the autocorrelation and the average magnitude difference based methods work in time domain. The algorithms produce a measure of voicing for each time frame. The voicing measure was combin...
متن کاملRadial Projections for Non-Linear Feature Extraction
In this work, two new techniques for non-linear feature extraction are presented. In these techniques, new features are obtained as radial projections of the original measurements. Radial projections are a particular kind of second order transformations that show interesting properties: they capture the local structure of the data and reduce dramatically the number of parameters to estimate fro...
متن کاملFeature Extraction Methods & Applications
There are numerous methods to classification of feature types. Imagine provides classification models in addition to texture features and convolution methods that assist in detecting various feature types. Using ESRI's ArcView and ArcGIS Feature Analyst extension, the process of feature extraction is readily accessible and user-friendly to the analyst. But, in general, road detection, specifica...
متن کاملRobust Image Topological Feature Extraction
Topological image features such as ridge and valley lines provide an intuitive and powerful way of characterizing image content. The notion of ridges and valleys in digital images was introduced by Haralick already in 1983, but so far it has received fairly limited attention. The main reason is the lack of efficient and robust algorithms for their computation in discrete images. Typically lines...
متن کاملshape adaptive, robust iris feature extraction from noisy iris images
in the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. to the best of our knowledge, the effect of noise factors on feature extraction has not been c...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2020
ISSN: 1556-5068
DOI: 10.2139/ssrn.3678383