2D and 3D curve modeling — multidimensional data recovery. (September 2017)

• Record Type: Journal Article
• Authors: Jakóbczak, Dariusz Jacek
• Is Part Of: Engineering applications of artificial intelligence. Volume 64(2017:Apr.)
• Page Start: 208
• Page End: 212
• Publication Date: 2017-09
• Subjects: Image retrieval -- Pattern recognition -- Data modeling -- Vector interpolation -- PFC method -- Feature reconstruction -- Probabilistic modeling; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.06.015
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Proposed method, called Probabilistic Features Combination (PFC), is the method of multi-dimensional data modeling, extrapolation and interpolation using the set of high-dimensional feature vectors. This method is a hybridization of numerical methods and probabilistic methods. Identification of faces or fingerprints need modeling and each model of the pattern is built by a choice of multi-dimensional probability distribution function and feature combination. PFC modeling via nodes combination and parameter γ as N -dimensional probability distribution function enables data parameterization and interpolation for feature vectors. Multi-dimensional data is modeled and interpolated via nodes combination and different functions as probability distribution functions for each feature treated as random variable: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.