Computer Vision for RGB-D Sensors: Kinect and Its Applications Special issue on IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernectics Call for Paper: Depth cameras have been exploited in computer vision for several years, but the high price and the poor quality of such devices have limited their applicability. With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use as an off-the-shelf technology. The complementary nature of the depth and visual (RGB) information in the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision, including object and activity recognition, people tracking, 3D mapping and localization, etc. For a long time, researchers have been challenged by many problems such as detecting and identifying objects/humans in real-world situations. Traditional object segmentation and tracking algorithms based on RGB images are not always reliable when the environment is cluttered or the illumination conditions suddenly change, both of which occur frequently in a real-world setting. However, effectively combining depth and RGB data may provide new solutions to these problems, where object segmentation based on depth information is robust against environmental changes, and the accuracy of object tracking/identification can be improved by considering the depth, motion and appearance information of an object. Freely available SDKs and posture trackers for the Kinect modeling environments further encourage new solutions to classic problems in computer vision. Compared to conventional computer vision systems (based on RGB images), systems using the Kinect sensor face a number of specific challenges, including characterization of objects based on the RGB-Depth images; correlation between per-pixel depth and RGB information when one of them is missing or corrupted; and, semantic linkage and decision making based on the fused information. Compared to stereo vision or ToF techniques exploiting other depth sensors (i.e., Bumblebee camera or PMD camera), the algorithms designed for the Kinect sensor need to solve additional problems, though the overall depth sensing quality of the Kinect sensor is much better than the other two. These particular problems embody the intelligent computing of per-pixel depth from a noisy and sparse depth point cloud; spatially calibrating and correlating the depth image with the RGB images; data mining from the inhomogeneous depth map; and, designing the illumination patterns for handling light interference effects. This special issue is specifically dedicated to new algorithms and/or new applications based on the Kinect (or similar RGB-D) sensors. The key outcomes of the special issue will be a better understanding of: (1) the contributions of this new sensor within the computer vision community, (2) the possible applications of the Kinect sensor, and (3) the key challenges and solutions for research in this domain. Topics of interest include, but are not limited to: · Object detection and recognition · Segmentation and clustering · Human pose estimation · Human activity recognition and gesture recognition · 3D scene reconstruction · Human-computer interaction exploiting depth information · Robotic vision based on Kinect · Data mining based on RGB-D information · Intelligent computing for generating dense depth map · Decision making for fusing sensors · Adaptive and learning techniques for a Kinect network (multi-Kinect) · Transmission and visualization of 3D scenes · 3D integration and understanding in multimedia applications · Practical issues of deploying Kinect · Social and ethical issues of Kinect sensing in public and private spaces · Use of Kinect to acquire ground truth data in context-aware computing · Industrial applications Prospective authors should visit for information on paper submission. Manuscripts should be submitted using the Manuscript Central system at Please choose “SI: Vision for Kinect” as the manuscript type. Manuscripts will be peer reviewed according to the standard IEEE process. Important Dates: Submission of full papers 30 September 2012 Notification to authors 30 January 2013 Submission of revised papers 30 March 2013 Final decision on revised papers 30 May 2013 Tentative publication date Fourth quarter 2013 Guest Editors: Ling Shao, The University of Sheffield, UK Jungong Han, CWI, The Netherlands Dong Xu, Nanyang Technological University, Singapore Jamie Shotton, Microsoft Research Cambridge, UK