# Efficient Filtering for Edge Extraction under Perspective Effect
## 6 Conclusions  
We address the problem of edge extraction according to the top-down information about certain targets under perspective effect. The top-down information contains two parts: one is the intrinsic and extrinsic parameters of the imaging process; the other is prior to the targets in the scene (in this paper, we consider edge extraction for the purposes of target detection). Once we have this information, we have more of a chance to extract the local edges from the real contours of the target, while ignoring the edges caused by noise or other irrelevant events, such as the texture, shadows, shading, and even edges from nearby  
Figure 14: The quantitative comparison results.  
Figure 13: Illustration about the importance of orientation tuning. (**a**) Salient edges other than expected orientation appeared. (**b**) Multiple orientations caused by both of the lane markings and the shadows.  
objects. In contrast to traditional methods, we introduced the top-down information into the low-level feature extraction stage as early as possible.  
In this paper, we argue that the most important aspects to enhance a local edge are scales (localized target size) and the expected orientation (localized target orientation). We assigned special orientation and scale for each location in the image before edge extraction. To compute the response of an edge detector at many orientations and arbitrary scale in real time, we proposed the osDoB. To illustrate that our osDoB can be tuned to arbitrary orientation and scale in real time, we solve the lane detection problem in challenging scenes. Experimental results show that performance can be greatly improved by the introduction of global appearance information during local feature detection.  
We argue that the scale and orientation should have more physical meaning connected with the real target information. The same problem came with other local features in computer vision literature, \"similar to other concepts used in computer vision, such as \"texture\" and \"face\", the notion of contour is the result of common human experience rather than a formal mathematical definition\" [6]. Although, in this work, we assign scale and orientation for local edge detection in rather a hard manner, it is also possible for us to assign scale and orientation for local feature detectors in a soft manner, such as applying the Gestalt principle [43], which can be addressed in future work. osDoB can be used as an efficient local filter for special orientation and arbitrary scale, and it has many usages in applications such as local edge extraction for local feature descriptors.  
Conceptualization, J.L. and X.A.; methodology, J.L. and X.A.; software, J.L.; validation, J.L.; writing--original draft preparation, J.L. and X.A.; writing--review and editing, J.L. and X.A. Both authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China under Grant 61973311.  
The results of our method are available on request by contacting the corresponding author.  
The authors declare no conflict of interest.