利用可調層數的多解析影像結構 的快速立體影像比對法則

A FAST STEREO MATCHING ALGORITHM USING ADAPTIVE MIPMAP LEVELS

林帝睿(Di-Ruei Lin)

指導教授 ： 鄭嘉慶

大同大學/電機資訊學院/通訊工程研究所/碩士(2005年)

摘要

立體影像做景深估測在電腦視覺領域中一直以來都是一個相當重要的研究課題。在這篇論文中,我們提出一個可以利用一般電腦來實現的有效率的景深估測的演算法，這個演算法是以 Yang 和 Pollefey所提出的多層多解析度影像結構(MML, Multiple Mipmap Levels)演算法為基礎。本質上，多層多解析度影像結構的方法是利用一個固定大小的關連性視窗來估測景深。關聯性為主的方法中最主要的問題是選擇窗口的大小。在低紋理區，窗口不能太小否則將會無法涵蓋足夠的結構來決定景深.而在不同物體的邊界處，大的視窗將會涵蓋不同景深的物體而導致錯誤的估測。
針對這個多層多解析度影像結構演算法的問題我们提出一個依據影像強度變化來調整多解析影像層數的景深估測法。在影像變化強的區域，我們使用較高解析度的多解析影像層數來估測景深;當在影像強度變化小的區域，我們則使用低解析度的多解析影像層數。我們利用梯度來表示影像強度的變化因為這可以簡單而且有效率的實現。
我們的演算法顯示可以改進在低紋理區和不同物體的邊界處的景深估測。我們成功的用一些立體影像來測試我們的系統，並且獲得令人滿意的實驗結果。

關鍵字

多層多解析影像

並列摘要

Depth from stereo has been one of the most actively researched topics in computer vision. In this work, we present an effective and efficient depth estimation algorithm suitable for implementation on a commodity PC. The proposed algorithm is based on the MML (Multiple Mipmap Levels) algorithm presented by Yang and Pollefeys[1]. Functionally, the MML method used correlation windows of fixed size for depth estimation. The main problem of correlation-based method is on the selection of window size. In areas of low texture, the window size cannot be too small, or it would not cover enough structure to resolve the disparity. On boundaries of distinct objects, large windows might cover objects of different depths which would result in false estimation.
We propose a method aiming at solving the problem of the MML algorithm by adaptively adjust mipmap levels based on the intensity variation of the image. In areas of high intensity variation, we use higher-resolution mipmap levels for the disparity estimation; while in areas of low intensity variation, we use lower-resolution mipmap levels. We compute gradient as an alternative representation of intensity variation due to the simplicity and efficiency of implementation.
The proposed algorithm shows that it is able to improve the disparity estimation in areas of low texture and on boundaries of distinct objects. We successfully demonstrate the system by processing several stereo images pairs and obtain satisfactory experimental results.

並列關鍵字

adaptive mipmap levels ； MML

參考文獻

[1] Ruigang Yang and Marc Pollefeys, “Multi-resolution real-time stereo on commodity graphics hardware,” Proceedings of the IEEE CVPR 2003, vol.1, P.P. 211-217.

[2] A Ruigang Yang, Marc Pollefeys, Hua Yang, Greg Welch ,”A Unified Approach to Real-Time,Multi-Resolution, Multi-Baseline 2D View Synthesis and 3D Depth Estimation using Commodity Graphics Hardware,” Department of Computer Science,University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S.A., August 6,2003.

[3] T. kanade and M. Okutomi, “A stereo matching algorithm with an adaptive window: Theory and experiment,” IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 16, P. 920 September 1994.

[5] Ebroul Izquierdo M., member, IEEE “Disparity/Segmentation Analysis: Matching with an Adaptive Window and Depth Driven Segmentation,” IEEE Transaction on Circuits and Systems for Video Technology, vol. 9, no. 4. June 1999.

[6] Burt, P.J., and Adelson, E.H., “The Laplacian Pyramid as a Compact Image Code,” IEEE Transaction Communication, Vol. COMM-31, pp. 532-540 April 1983.