# Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks
## 1 Introduction  
Due to the high demand for scene-depth information in real-time applications, one-shot three dimensional (3D) imaging methods are attracting more and more researchers. Among them, the methods using a color stripe pattern have many outstanding advantages, such as robustness against occlusion and discontinuity problems, the ability to capture both dynamic and static scenes, and a short measurement time. Most of the color stripe patterns in structured light systems are based on the De Bruijn sequence [1; 2; 3; 4]. The De Bruijn sequence of \(n\) colors with a property window of size \(m\) is a sequence containing \(n\times m\) subsequences of length \(m\) that occur exactly once in the entire pattern. The differences in the different coding techniques are the number of colors used and the size of the property window. Typically, the size of the property window in a color stripe or slit projection pattern is \(1\times 5\) or \(1\times 7\). For capturing multi-colored objects, in [5], [PERSON] et al. used a filtering method that improved the color classification of the stripes and proposed a technique to combine the two- or three-axis coded stripe pattern to enhance the accurate color recognition of each stripe in the image of a deformed pattern. This way, the method was less sensitive to the characteristics of the scene. Instead of using a single pattern encoded with several colors, patterns encoded with only two colors in the RGB color space were introduced in [6; 7]. Although the use of fewer colors to encode the pattern makes feature point detection easier, more complex pattern decoding methods are required. In addition to the above advantages, the methods using a color stripe pattern are still challenged by detecting feature points, which are the points on the border between two adjacent color stripes in the deformed pattern images. To improve the density ofthree-dimensional maps, high-resolution color stripe patterns, which are made up of a large number of color stripes with a small width, should be used. In addition, more colors are required to encode the patterns. These issues make the patterns more sensitive to noise and color crosstalk. Moreover, these methods also suffer from the object surface characteristics, such as color, inter-reflectivity, textures, etc., when capturing objects with a complex surface structure under room lighting conditions. These problems cannot be solved by traditional edge detection methods. With an important role in practical image processing-based applications, so far, many studies on edge detection algorithms have been published. Most of the methods are applied to grayscale images because of their high accuracy [8; 9; 10; 11; 12]. In order to overcome the challenges of edge detection in color images, many methods of converting color images into grayscale images to take advantage of grayscale images in edge detection have been proposed. In [13], 10 methods for converting color images into grayscale images were performed to compare their influence on eight different edge detection algorithms. It was found that the resulting images depended not only on the edge detection method but also on the image conversion method. On the other hand, the methods of directly detecting the edges in color images have also improved. In [14], [PERSON] et al. proposed a multi-dimensional edge detection algorithm for detecting an edge in color images. First, the noise in RGB images is suppressed by applying the Wiener filter. Then the Bhattacharyya distances of four block pairs are calculated. Finally, the maximum values are used to determine the edges. The performance of this method in detecting weak edges is better than the traditional Sobel and Canny operators. Taking advantage of the Canny operator, an improved algorithm based on the concept of vectors was proposed in [15]. Their experimental results proved that this method outperformed the edge detection methods of gray images in the field of color edge detection, especially in outline detection and fake edge suppression.