원문정보
초록
영어
According to the limitations of a single measurement algorithm in the current 3D models’ viewpoint extraction, this essay puts forward a viewpoint extraction algorithm based on AdaBoost iterative algorithm, which can make the features adaptive automatically. It, firstly, extracts 3D models’ feature descriptor and feature vector in the model library and adopts AdaBoost iterative algorithm to establish rules about classification and matching from geometric features and various viewpoint extraction algorithm; then, it constructs decision classifier in order to extract optimal viewpoint. In query process, the model obtains viewpoint extraction algorithm which can suit its geometric feature through decision classifier and then gets its best view by calculation. The experimental result shows this algorithm extraction effect is superior to the one by a single measurement algorithm.
목차
1. Introduction
2. Research Method Summary
3. Geometric Features and Viewpoint Extraction
3.1. Extraction of SDF Feature Descriptor and Eigenvector
3.2. Construction of Viewpoint Extraction Algorithm Library
3.3. Construction of Decision Classifier and Viewpoint Extraction
4. Examples and Analysis
4.1. Analysis of Subjective Visual Sense Matching
4.2. Statistical Analysis of Function Comparison
4.3. Analysis of Algorithm Stability
5. Conclusions
References