Discrete derivative approximations with scale-space properties: A basis for low-level feature extraction
نویسندگان
چکیده
منابع مشابه
On the feature extraction in discrete space
In many pattern recognition applications, feature space expansion is a key step for improving the performance of the classifier. In this paper, we (i) expand the discrete feature space by generating all orderings of values of k discrete attributes exhaustively, (ii) modify the well-known decision tree and rule induction classifiers (ID3, Quilan, 1986 [1] and Ripper, Cohen, 1995 [2]) using these...
متن کاملLocal Feature Extraction Using Scale-space Decomposition
In our recent work we have introduced a framework for extracting features from solid of mechanical artifacts in polyhedral representation based on scale-space feature decomposition [1]. Our approach used recent developments in efficient hierarchical decomposition of metric data using its spectral properties. In that work, through spectral decomposition, we were able to reduce the problem of mat...
متن کاملMorphological scale-space analysis and feature extraction
This paper presents a morphological scale-space approach to the problem of feature extraction. The method relies on two steps: a hierarchical simplification step based on pyramids of morphological operators and a feature extraction step consisting in measuring the persistence of each image structure through the simplification scales. Specific scalespace properties are needed: the features shoul...
متن کاملScale-space feature extraction on digital surfaces
A classical problem in many computer graphics applications consists in extracting significant zones or points on an object surface, like loci of tangent discontinuity (edges), maxima or minima of curvatures, inflection points, etc. These places have specific local geometrical properties and often called generically features. An important problem is related to the scale, or range of scales, for ...
متن کاملScale-Space Properties of Quadratic Feature Detectors
Feature detectors using a quadratic nonlinearity in the ltering stage are known to have some advantages over linear detectors; here we consider how their scale-space properties compare. In particular, we investigate the question whether, like linear detectors, quadratic feature detectors permit a scale-selection scheme with the \causality property", which guarantees that features are never crea...
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: Journal of Mathematical Imaging and Vision
سال: 1993
ISSN: 0924-9907,1573-7683
DOI: 10.1007/bf01664794