臺灣博碩士論文加值系統

在這個論文中，我們根據Rochester Institute of Technology的Munsell Color Science Laboratory所提出的架構，建立一套多頻譜式影像擷取系統。這個系統可以完整的重建物體表面的反射頻譜函式，整個系統包含一個單色數位像機、七個色彩濾波器、一具實驗用光源一台個人電腦。在仔細的分析完系統的特徵後，我們發現估測的反射頻譜函式和原始函式十分相近，彼此的色彩差異性Delta Eab小於三。

In this thesis, we present the development of a multi-spectral image acquisition system, which is based on the scheme proposed by the Munsell Color Science Laboratory at Rochester Institute of Technology. This system can faithfully reconstruct the spectral reflectance function of the subject surface. The whole system consists of a digital monochromatic camera, a set of seven color filters, a given light source, and a personal computer. After careful system characterization, we have demonstrated that the reconstructed reflectance functions are very close to the original reflectance functions. Their color differences Delta Eab are usually smaller than 3.

Chapter 1 Introduction
1.1 Motivation
Chapter 2 Background
2.1 Color
2.1.1 Light
2.1.2 Spectral reflectance function of object surface
2.1.3 Color vision
2.1.4 Metamerism
2.2 Colorimetry
2.2.1 RGB color space
2.2.2 XYZ color space
2.2.3 LAB color space
2.2.4 Color difference
2.3 Image capture
2.3.1 Conventional digital image acquisition
2.3.2 Problems of modern image acquisition systems
2.3.3 Image recording with spectral reflectance function
2.3.4 Multi-spectral image capture
2.4 Principal component analysis
2.4.1 Principal components
2.4.2 Derivation of principal components
2.4.3 Properties of principal components
Chapter 3 Multi-spectral Image Acquisition System
3.1 Multi-spectral image acquisition
3.1.1 Analysis of the transformation matrix
3.1.2 Reflectance reconstruction using a transformation matrix
3.2 The measurement of spectrum
3.2.1 Initial setting
3.2.2 The measurement of illuminant spectrum
3.2.3 The measurement of spectral reflectance function
3.3 Principal component analysis (PCA)
3.3.1 The training targets
3.3.2 Principal component analysis
3.4 Multispectral image acquisition
3.4.1 Digital camera
3.4.2 Filters
3.4.3 Reflectance measurement of samples
3.5 Spectral reflectance estimation
3.5.1 Linear transform
3.5.2 Linear regression
3.5.3 Pseudo inverse
3.6 Spectral reflectance estimation of objects
3.6.1 Multispectral image capture of clothes
3.6.2 Multispectral image capture of images
Chapter 4 Conclusion
References

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