Specular Detection on Glossy Surface Using Geometric Characteristics of Specularity in Top-View Images

Abstract

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.

Parameter-Free Matrix Decomposition for Specular Reflections Removal in Endoscopic Images

“…In [17] , a specular lobe is identified at the tail end of the histogram for thoracoscopic images and is extracted to obtain the highlight pixel map. Kim et al [18] proposed using geometric characteristics such as the shape of the specular region to detect highlight pixels. With the aid of a thresholding operation, highlight pixels are identified in [4] and [19] using chromatic information.…”

Reduction of Specular Reflection Based on Linear Polarization Control for Fluorescence-Induced Diagnostic Evaluation

“…According to Snell’s law, for the light (η 1 ) irradiated from the LED (as shown in Figure 3 a), a part of the light (η 2 ) has a refraction angle (θ 2 ) which is absorbed by the tissue, and the remaining light is reflected (η r ) [ 27 ]. Therefore, η 1 is equivalent to η r (η 1 = η r (θ 1 = θ r )) [ 28 ]. Hence, the reflected specular (η r ) is generated in the form of diffused reflection, as shown in Figure 3 b [ 29 ].…”

Non-Lambertian surfaces and their challenges for visual SLAM

“…However, computer vision systems are known to operate less reliably over water and highly reflective, transparent surfaces [16], which are examples of non-Lambertian surfaces. The ongoing expansion of terrestrial autonomous driving also grapples with challenges posed by non-Lambertian surfaces [35].…”