# Efficient Filtering for Edge Extraction under Perspective Effect
## 5 Experiments
As shown in Figure 13a, besides several lane markings, there are many unexpected white lines with unexpected orientations. The osDoB can suppress these lines. In this case, such a result can also be obtained by the Canny detector, which combines with an orientation map. We can remove these edges by determining whether these edges possess the expected orientation. We can also remove them at this stage (after edge detection stage). In the second case, the osDoB will play a very important role. A single edge point may possess more than one orientation [19]. For traditional edge detection, such as the Canny edge detector, the orientation with the most intense response will be labeled as an edge point with that orientation. However, sometimes the orientation of one point will lower the intensity of the response to the expected orientation, and that point has a consistent orientation along a local line. As shown in Figure 13b, the edges in the boundary of the lane marking have the same orientation, but some of them show higher responses at other orientations. This is caused by heavy shadows. For the Canny detector, these edges can give a point with the wrong orientation, and such edges may be removed in the non-maxima suppression stage. This is possible because they lack consistent orientations, as shown in the lower part of Figure 13b. For the proposed method, an orientation tuning strategy is employed, and points with weak expected orientations can be extracted. Assigning proper scale for each point is very important for achieving maximal SNR using such a strategy. From these comparisons, we observe that assigning a proper scale for each location is also important. As shown in Figure 12d, the osDoB can suppress the edges with unexpected orientations, but some of them are still present. This is because the response of these points is very heavy at smaller scales. When we assign a proper scale to each point (osDoB), we observe that most of the unexpected edges are removed. The results shown in Figure 12f also supports this conclusion. In the input image, there is a passenger car which possesses many edges and lines with unexpected orientations. Most of these edges can be suppressed by assigning the proper scale. The quantitative results are shown in Figure 14a,b. We compare oDoB, and osDoB with edge detectors, such as Canny, Sobel, Prewitt and Roberts. The ROC curves of these models are shown in Figure 14a. We also adopt the AUC (area under the curve) of the ROC curve as the evaluation metric and depict the value of the corresponding AUC of each curve. By comparing quantitatively, we can see that oDoB achieves compatible performance with Canny. The osDoB has the highest AUC, with a 10% higher AUC than Canny. The osDoB can always obtain the highest hitting rate among these edge detectors when a specific false positive rate is given. From the DSC curves, which are shown in Figure 14b, we can draw the conclusion that orientation tuning can produce better performance. Tuning both the orientation and the scale is needed and is able to produce the best performance. The optimal threshold for osDoB is 0.26.