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題名 | Stereo Matching Using Synchronous Hopfield Neural Network=運用同步式霍普菲爾類神經網路求解雙影像對應問題 |
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作者 | 孫德修; Sun, Te-hsiu; |
期刊 | 工業工程學刊 |
出版日期 | 20090700 |
卷期 | 26:4 2009.07[民98.07] |
頁次 | 頁276-288 |
分類號 | 312.1 |
語文 | eng |
關鍵詞 | 雙影像對應; 雙影像系統; 同步式霍普菲爾類神經網路; 機器視覺; Stereo matching; Correspondence problem; Synchronous Hopfield neural network; Computer vision; |
中文摘要 | 深度資訊之取得為電腦視覺研究領域中之最要議題,而雙影像爲3D資訊取得技術之重要技術之一。本研究以同步式霍普菲爾類神經網路解決掃瞄式雙影性對應問題,首先,特性點以索伯運算子(Sobel operator)及自訂之臨界值於雙影像中分別取得,再將此資訊表示爲一雙影像對應最佳化問題之能量函數,此函數包含相異性、連續性、視差特性及單一性等條件,再以同步式霍普菲爾類神經網路求取此函數之最小植,最後再以一錯誤對應移除準則將錯誤之對應點移除。本研究以一般通用之雙影像作實驗驗證,實驗顯示本研究所提出方法可有效地解決該問題,並可應用於多種領域中。 |
英文摘要 | Deriving depth information has been an important issue in computer vision. In this area, stereo vision is an important technique for 3D information acquisition. This paper presents a scanline-based stereo matching technique using synchronous Hopfield neural networks (SHNN). Feature points are extracted and selected using the Sobel operator and a user-defined threshold for a pair of scanned images. Then, the scanline-based stereo matching problem is formulated as an optimization task where an energy function, includ-ing dissimilarity, continuity, disparity and uniqueness mapping properties, is minimized. Finally, the incorrect matches are eliminated by applying a false target removing rule. The proposed method is verified with an experiment using several commonly used stereo im-ages. The experimental results show that the proposed method solves effectively the stereo matching problem and is applicable to various areas. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。