Specular Detection on Glossy Surface Using Geometric Characteristics of Specularity in Top-View Images
| Autor: | Whoi-Yul Kim, Moonsoo Ra, Seung-Hyun Kim |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Surface (mathematics)
Computer science specularity estimation 02 engineering and technology lcsh:Chemical technology Biochemistry Article Analytical Chemistry Line segment gradient vector 0502 economics and business 0202 electrical engineering electronic engineering information engineering False positive paradox lcsh:TP1-1185 Computer vision Specular reflection Electrical and Electronic Engineering Instrumentation 050210 logistics & transportation Pixel Plane (geometry) business.industry line detection 05 social sciences 020207 software engineering Atomic and Molecular Physics and Optics Specularity Line (geometry) Artificial intelligence business |
| Zdroj: | Sensors Volume 21 Issue 6 Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 2079, p 2079 (2021) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21062079 |
| Popis: | In an autonomous driving assistance system (ADAS), top views effectively represent objects around the vehicle on a 2D plane. Top-view images are therefore widely used to detect lines in ADAS applications such as lane-keeping assistance and parking assistance. Because line detection is a crucial step for these applications, the false positive detection of lines can lead to failure of the system. Specular reflections from a glossy surface are often the cause of false positives, and since certain specular patterns resemble actual lines in the top-view image, their presence induces false positive lines. Incorrect positions of the lines or parking stalls can thus be obtained. To alleviate this problem, we propose two methods to estimate specular pixels in the top-view image. The methods use a geometric property of the specular region: the shape of the specular region is stretched long in the direction of the camera as the distance between the camera and the light source becomes distant, resulting in a straight line. This property can be used to distinguish the specular region in images. One estimates the pixel-wise probability of the specularity using gradient vectors obtained from an edge detector and the other estimates specularity using the line equation of each line segment obtained by line detection. To evaluate the performance of the proposed method, we added our methods as a pre-processing step to existing parking stall detection methods and investigated changes in their performance. The proposed methods improved line detection performance by accurately estimating specular components in the top-view images. |
| Databáze: | OpenAIRE |
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