# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
###### Abstract  
Superpixel segmentation is a widely used preprocessing method in computer vision, but its performance is unsatisfactory for color images in cluttered indoor environments. In this work, a superpixel method named weighted coplanar feature clustering (WCFC) is proposed, which produces full coverage of superpixels in RGB-depth (RGBD) images of indoor scenes. Basically, a linear iterative clustering is adopted based on a cluster criterion that measures the color similarity, space proximity and geometric resemblance between pixels. However, to avoid the adverse impact of RGBD image flaws and to make full use of the depth information, WCFC first preprocesses the raw depth maps with an inpainting algorithm called a Cross-Bilateral Filter. Second, a coplanar feature is extracted from the refined RGBD image to represent the geometric similarities between pixels. Third, combined with the colors and positions of the pixels, the coplanar feature constructs the feature vector of the clustering method; thus, the distance measure, as the cluster criterion, is computed by normalizing the feature vectors. Finally, in order to extract the features of the RGBD image dynamically, a content-adaptive weight is introduced as a coefficient of the coplanar feature, which strikes a balance between the coplanar feature and other features. Experiments performed on the New York University (NYU) Depth V2 dataset demonstrate that WCFC outperforms the available state-of-the-art methods in terms of accuracy of superpixel segmentation, while maintaining a high speed. depth maps; RGB-depth; segmentation; simple linear iterative clustering superpixels 2020 2018 20 dark and dull, like low-contrast images because the light in the indoor environment is inadequate. In other cases, different objects of similar color appear in the same area of an image. For example, if there is a white ceramic pot in front of a white wall, then superpixel methods that depend only on color data can barely separate the pot from the background, especially in indoor environments. To make matters worse, this situation occurs in most indoor scene photographs, which makes it difficult to discern superpixels accurately using only color images. RGB-Depth (RGBD) images provide an opportunity to improve the performance of superpixel methods in those cases [13; 14]. PrimeSense-like devices, such as Microsoft Kinect, ASUS Xtion, Intel RealSense, etc., can produce RGBD images, which means generating color images and depth images simultaneously. Therefore, RGBD images provide more information than traditional color images, such as the depth at each pixel in the image region. In other words, the color and depth are correlated pixel by pixel. This makes it possible to solve the problem mentioned above. In practice, many boundaries can be detected through depth data, regardless of whether the exposure is sufficient, even in a dark room. Likewise, the color-close region can be more precisely divided. Therefore, more precise oversegmentation can be achieved on RGBD images. Although there are clear benefits brought by the RGBD images to superpixel methods, there are also some drawbacks [15]. First, because the depth is estimated by computing the results of infrared structured light, numerous artifacts or errors [16] are introduced into the depth images, such as shadows, holes and fleeting noises. Second, even if the depth map has no holes, which means each pixel has a depth value, it is still not dense. An RGBD image can be projected from the 2D space onto the 3D coordinate system to form a color point cloud. Most points are not completely surrounded by their neighborhoods in 3D space after projection, which is much different from the original color image. In other words, the point cloud is sparse. As a result, the following questions for superpixel segmentation arise:  
* Because of the sparcity of the RGBD images, simple extensions of color image superpixel methods cannot be directly applied.