Open AccessPROBABILISTIC APPROACH TO DOMAIN SELECTION FOR INTEGRATING THE NORMAL’S FIELD IN 3D RECONSTRUCTION
Author(s) -
Bohdan Rusyn,
Oleksiy Lutsyk,
Yarema Varetskyy
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.14.1.649
Subject(s) - retraining , computer science , probabilistic logic , set (abstract data type) , domain (mathematical analysis) , class (philosophy) , process (computing) , selection (genetic algorithm) , field (mathematics) , machine learning , sequence (biology) , artificial intelligence , software , data mining , algorithm , mathematics , mathematical analysis , biology , international trade , pure mathematics , business , genetics , programming language , operating system
The method of integrating normal’s field is improved by using the proposed algorithm for breaking local areas based on Schwartz equations. It was proposed local criteria of splitting normal’s onto local areas, which is crucial for the next step of building the sequence chain. The process of classification in case of reducing the training set to select regions of integration is investigated. The reason for the effect of retraining is conditioned with a minimal number of errors on the training sample. It is shown that stratification of algorithms by mistakes and increasing their similarities reduce the likelihood of the retraining. Proposed approaches are implemented as a software and are suitable for the broad class of real surfaces.