影像感測器之色彩濾波陣列補插與輪式機器人之視覺追蹤控制設計

Abstract

本論文的目標為發展一新穎的色彩濾波陣列(Color Filter Array, CFA)補插演算法用以解決單一影像感測器之色彩重現問題，以及一具有強健性的視覺追蹤控制系統用以解決輪式移動型機器人之以視覺為基礎的移動控制問題。目前大部分的數位相機均採用一貝爾模式(Bayer Pattern)之CFA覆蓋於單一影像感測器上，用以擷取一貝爾模式之馬賽克影像(Bayer Mosaic Image)。一般所見的全彩影像則是將擷取到的貝爾模式之馬賽克影像，經由一色彩重現處理後，重建出所有像素上的色彩資訊。此色彩重現處理一般稱為CFA補插或CFA解馬賽克。為了重建出具有高逼真度的全彩影像，本論文提出一新穎的異次投影硬性決定(Heterogeneity-Projection Hard-Decision, HPHD)演算法並結合一新的以色差為基礎之邊緣適應性(Color-Difference Based Edge-Adaptive, CDEA)CFA補插方法來解決貝爾模式之馬賽克影像的色彩重現問題。在此所提出的HPHD演算法主要目的為估測出最佳的補插方向並且執行硬性決定捕差法，此方法的主要特點在於補插的方向決定於執行補插動作之前，如此可提高色彩補插的效率。另一方面，所提出的CDEA CFA補插方法致力於將綠色色彩平面之高頻資訊加入其他色彩平面中，用以重現出較少色彩遺物的色彩值。 在視覺追蹤控制的設計中，本論文提出一應用於配有一傾斜式相機之輪式移動型機器人的強健型視覺追蹤控制系統，此系統包含一視覺追蹤控制器(Visual Tracking Controller, VTC)及一視覺狀態估測器(Visual State Estimator, VSE)。首先，一新穎的雙重Jacobian視覺互動模型被推導出來，用以幫助VTC及VSE的設計。VSE主要目的為在影像平面中直接估測出最佳系統狀態以及目標物之移動，VTC則利用VSE所估測出的結果，接著計算出機器人的控制速度。為了掌握實際視覺追蹤系統中所遭受到的不確定性，VSE可以克服由影像雜訊及短暫遮蔽不確定性所造成之干擾。另一方面，VTC不但對於系統參數之不確定性具有某種程度的強健性，並且也能克服速度命令中無法模型化的量化影響。因此，結合所提出的VTC及所提出的VSE，此視覺追蹤控制系統對於影像雜訊、系統參數、速度量化及短暫遮蔽所造成之不確定性影響皆具有強健性。電腦模擬及實驗結果驗證所提出的視覺追蹤控制系統之效果，包含追蹤性能、系統收斂性及系統強健性。

The objective of this thesis is to develop a novel color filter array (CFA) interpolation algorithm for color reproduction of a single image sensor and a robust visual tracking control system for vision-based motion control of a wheeled mobile robot. Most digital cameras employ a single image sensor covered with a Bayer CFA to capture a Bayer mosaic image. A full-color image is then reconstructed from the captured Bayer mosaic image through a color reproduction process, commonly known as CFA interpolation or CFA demosaicing. To reconstruct the full-color images with high fidelity, a novel heterogeneity-projection hard-decision (HPHD) algorithm combined with a new color-difference based edge-adaptive (CDEA) CFA interpolation method is proposed for color reproduction of Bayer mosaic images. The proposed HPHD algorithm aims to estimate the optimal interpolation direction and perform hard-decision interpolation, in which the direction of interpolation is decided before performing the interpolation. On the other hand, the proposed CDEA CFA interpolation method devotes to reproduce color values with fewer color artifacts by adding the high-frequency information of green channel to other color channels. Compared with three recently reported CFA interpolation techniques, the proposed HPHD-CDEA method outperforms all of them in both quantitative and visual comparisons by utilizing twenty-five natural images from Kodak PhotoCD. In the design of visual tracking control, a robust visual tracking control system, which consists of a visual tracking controller (VTC) and a visual state estimator (VSE), is proposed for a wheeled mobile robot equipped with a tilt camera. A novel dual-Jacobian visual interaction model is first derived to help the design of VTC and VSE. The VSE aims to estimate the optimal system state and target motion in the image plane directly, and the VTC then calculates the robot’s control velocities by using the estimation results from VSE. To handle the uncertainties encountered in practical visual tracking control system, the VSE can overcome the disturbances caused by both image noise and temporary occlusion uncertainties. On the other hand, the VTC not only possesses some degree of robustness against the system model uncertainties, but also overcomes the unmodelled quantization effect in the velocity commands. Therefore, by combining the proposed VTC with the proposed VSE, the visual tracking control system is robust to the uncertainties of image noise, system model, velocity quantization and temporary occlusion. Computer simulations and experimental results validate the effectiveness of the proposed visual tracking control system, in terms of tracking performance, system convergence, and robustness.