| Summary: | 博士 === 國立臺灣師範大學 === 資訊工程研究所 === 97 === 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.
|
|---|
