對於許多電腦視覺方面的應用而言，從輸入單一張的影像中粹取特徵影像（characteristic images）是非常重要的一個課題，例如陰影的分析、輔助光影的研究、反光消除、反射影像的移除等。舉例來說，對於許多視覺化的智慧型交通運輸應用系統（Intelligent Transportation System）而言，例如交通監控、交通的違規執法、駕駛安全輔助、自動車輛導引等，這些視覺系統若不是架設於戶外，就是裝設於車輛中，它們都遭遇到一共同的困擾，就是光影（包括陰影、反光等現象）常常會干擾甚至降低相關系統的可靠度，使得後續的處理工作增加不少的困難。
然而，想要直接從輸入單一張的影像中粹取特徵影像並非一件容易的事。本論文提出一個可靠的架構從輸入單一張的影像中粹取特徵影像，本架構包括四個主要步驟：邊緣偵測（boundary generation）、分類資訊擷取（information extraction）、邊緣分類（boundary classification）以及特徵影像組成（image composition）。在本論文中共提出應用本架構所解決的三像主要問題，包括反射干擾、反光消除、陰影移除，其特徵影像分別定義如下：反射干擾影像（Interference images）之特徵影像包括：觀測物體影像（Object image）以及反射影像（Reflection image）；反光影像（Dichromatic reflection images）之特徵影像包括：強光影像（Specular image）以及物體影像（Diffuse image）；本質影像（Intrinsic images）之特徵影像包括：實體影像（Reflectance image）以及光影影像（Illumination image）。
本論文所提出的架構，可從輸入單一張的影像中粹取特徵影像的技術，除了可以提供上述應用系統一個問題的解決方法之外，並可以應用於任何戶外或是與光影有關的系統。

Many computer vision applications have had successful results in limited environmental conditions. However, they often fail when the constraints are loosened as in real world scenes. One of the most common restrictions imposed on vision algorithms is the illumination condition. Techniques that are able to tolerate illumination variations will be useful for general and realistic scenes. In this study, a solution is proposed to get around the undesired effects of illumination such as shadows, highlights and interference reflections. They are called characteristic images and decomposed from the input image.
In view of that edges are one of the keys to understand an image; a computational framework for characteristic images decomposition from a single image based on the edges of the image is developed. The major idea is to classify the edge pixels of the image to target characteristic subsets. The proposed computational framework for characteristic decomposition consists of four major steps: boundary detection, evidence extraction, boundary classification, and characteristic image reconstruction. Given an image, the boundaries of the image are first detected. Evidence is extracted to classify the edge pixels to characteristic subsets. Based on the classification result of edge pixels, an integration process is applied to the classified edges to reconstruct the characteristic images.
Three applications of this computational framework, i.e., interference reflections, highlight reflections, and intrinsic images, are developed in this dissertation. For interference reflections, a technique for separating reflection and object components of a single interference image in an automated manner is presented. The key idea of the proposed method is to classify edges of the interference image into either reflection or object, and to use integration to reconstruct reflection and object images. The method utilizes TV model, blur measure, and region segmentation results as evidence with fuzzy integral technique to classify the edge pixels. Based on the classification results of edge pixels, an integration method is applied to reconstruct the reflection and object components of the input image. For separating specular and diffuse components, Shafer’s dichromatic reflection model is utilized, which assumes that light reflected at a surface point is linearly composed of diffuse and specular reflections. The major idea is to classify the boundary pixels of an image as specular or diffuse. A fuzzy integral process is proposed to classify boundary pixels based on their local evidences, including specular and diffuse estimation information. Based on the classification result of boundary pixels, an integration method is applied to reconstruct the specular and diffuse components of the input image. Unlike previous research, the proposed method has no color segmentation or iterative operations. For intrinsic images, the proposed approach first convolves an input image with a prescribed set of derivative filters. The pixels of the derivative images are next classified as reflectance or illumination according to three measures: chromatic, intensity contrast and edge sharpness, which are calculated in advance for each pixel from the input image. Finally, an integration process is applied to the classified derivative images to obtain the intrinsic images of the original image. The experimental results have demonstrated that the proposed methods can perform characteristic images decomposition from a single image effectively with small misadjustments and rapid convergence.

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