DISSERTATION

3-D object recognition from 2-d view

Samuel Lukas

Year: 2023 University:   Open Access Repository (University of Tasmania)   Publisher: University of Tasmania

Abstract

This thesis presents two methods that can be used in recognizing 3-D objects from 2-D views. Those are the methods that use either the affine invariant Fourier descriptors and or the cross-ratio descriptors. The performance of affine invariant Fourier descriptors in recognizing 3-D objects is invariant under translation, rotation, scaling and shear distortion. However, it cannot cope with perspective distortion. On the other hand, the cross-ratio descriptors' performance is more than satisfactory under all those conditions including perspective distortion. This thesis also presents the simulation results applying the cross-ratio descriptors to the six simple 3-D objects which are individually different but in general quite similar.

Keywords:
Perspective distortion Cross-ratio Invariant (physics) Affine transformation Fourier transform Artificial intelligence Mathematics Perspective (graphical) Distortion (music) Pattern recognition (psychology) Scaling Cognitive neuroscience of visual object recognition Computer science Computer vision Geometry Object (grammar) Mathematical analysis Image (mathematics)

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Topics

Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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