- 期刊
滅點偵測之研究－基於θ-直方圖統計分析法
A Study of Vanishing Point Detection-Based on θ-Histogram Analysis
摘要
本文提出基於θ-直方圖(θ-histogram)統計分析的滅點偵測(vanishing point detection)方法。此方法可適用於攝影時任一旋轉角不為零度或接近零度所拍攝的近景建物影像滅點位置偵測，不需要另外的已知條件及人工介入。經實驗證明，六幅室內模型影像利用本文之方法偵測滅點位置推算出的物空間長度RMSE值平均為±0.0041m，相對精度約達1/48~1/21；室外的實際建物影像，推算的物空間長度RMSE值為加±0.161 m，相對精度約為1/64~1/27。此方法在加入以驗後方差估計原理導出的選擇權迭代法［李德仁，1992］後，即使用來計算滅點位置的直線有約13%不正確，推算物空間長度的RMSE值也能達到切±0.166m，相對精度約為1/62~1/28，相當程度地確保了此法滅點偵測成果的可靠度。
並列摘要
Using vanishing point to estimate orientation parameters does not need control point's information. It is applied for the object reconstruction, navigation of autonomous vehicles or robots, camera calibration and length measurement. This research present a method of vanishing point detection based on θ-histogram analysis. It is suitable for close-range building image and without manual interference. If orientation parameters have known can infer object length on single image. For criterion of experimentation result, we compare inferential length with real length and compute RMSE. Average RMSE of using our method in the six model images is ±0.0041 m (relative accuracy is 1/48~1/21). RMSE of using our method in a real building image is ±0.0161 m (relative accuracy is 1/64-1/27). Even if in the set of lines for vanishing point detection has 13% error, RMSE still can achieve ±0.166 m (relative accuracy is 1/62~1/28). This result shows that our vanishing point detection method is robust.