CED: Color Event Camera Dataset

Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called 'events'. Event cameras offer advantages over conventional frame-based cameras in terms of latency, high dynamic range (HDR) and temporal resolution. Until recently, event cameras have been limited to outputting events in the intensity channel, however, recent advances have resulted in the development of color event cameras, such as the Color-DAVIS346. In this work, we present and release the first Color Event Camera Dataset (CED), containing 50 minutes of footage with both color frames and events. CED features a wide variety of indoor and outdoor scenes, which we hope will help drive forward event-based vision research. We also present an extension of the event camera simulator ESIM that enables simulation of color events. Finally, we present an evaluation of three state-of-the-art image reconstruction methods that can be used to convert the Color-DAVIS346 into a continuous-time, HDR, color video camera to visualise the event stream, and for use in downstream vision applications.Comment: Conference on Computer Vision and Pattern Recognition Workshop

Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies

Many computer vision applications apply background suppression techniques for the detection and segmentation of moving objects in a scene. While these algorithms tend to work well in controlled conditions they often fail when applied to unconstrained real-world environments. This paper describes a system that detects and removes erroneously segmented foreground regions that are close to a ground plane. These regions include shadows, changing background objects and other low-lying objects such as leaves and rubbish. The system uses a set-up of two or more cameras and requires no 3D reconstruction or depth analysis of the regions. Therefore, a strong camera calibration of the set-up is not necessary. A geometric constraint called a homography is exploited to determine if foreground points are on or above the ground plane. The system takes advantage of the fact that regions in images off the homography plane will not correspond after a homography transformation. Experimental results using real world scenes from a pedestrian tracking application illustrate the effectiveness of the proposed approach

A full photometric and geometric model for attached webcam/matte screen devices

International audienceWe present a thorough photometric and geometric study of the multimedia devices composed of both a matte screen and an attached camera, where it is shown that the light emitted by an image displayed on the monitor can be expressed in closed-form at any point facing the screen, and that the geometric calibration of the camera attached to the screen can be simplified by introducing simple geometric constraints. These theoretical contributions are experimentally validated in a photometric stereo application with extended sources, where a colored scene is reconstructed while watching a collection of graylevel images displayed on the screen, providing a cheap and entertaining way to acquire realistic 3D-representations for, e.g., augmented reality

Unsupervised Monocular Depth Estimation with Left-Right Consistency

Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we innovate beyond existing approaches, replacing the use of explicit depth data during training with easier-to-obtain binocular stereo footage. We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data. Exploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone results in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency between the disparities produced relative to both the left and right images, leading to improved performance and robustness compared to existing approaches. Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.Comment: CVPR 2017 ora

Towards dynamic camera calibration for constrained flexible mirror imaging

Flexible mirror imaging systems consisting of a perspective camera viewing a scene reflected in a flexible mirror can provide direct control over image field-of-view and resolution. However, calibration of such systems is difficult due to the vast range of possible mirror shapes and the flexible nature of the system. This paper proposes the fundamentals of a dynamic calibration approach for flexible mirror imaging systems by examining the constrained case of single dimensional flexing. The calibration process consists of an initial primary calibration stage followed by in-service dynamic calibration. Dynamic calibration uses a linear approximation to initialise a non-linear minimisation step, the result of which is the estimate of the mirror surface shape. The method is easier to implement than existing calibration methods for flexible mirror imagers, requiring only two images of a calibration grid for each dynamic calibration update. Experimental results with both simulated and real data are presented that demonstrate the capabilities of the proposed approach

The Application of Stereoscopic PIV in a Liquid-fueled Gas Turbine Combustor

Strict regulations on aviation gas turbine engine emissions and fuel consumptio

Monocular multi-view stereo imaging system

In this study, we present a single-camera, multi-view stereo imaging system for capturing three-dimensional (3D) information. First, we design a monocular, multi-view stereo imaging device composed of a fisheye lens, and planar mirrors placed around the lens. The fisheye lens has a wide view-angle. The captured image includes a centered region of direct observation and surrounding regions of mirrored observations. These regions can be considered as images captured by multiple cameras at different positions and orientations. Therefore, the proposed device is equivalent to a synchronous multiple-cameras configuration, in which all the cameras share the same physical characteristics. In addition, we show how to place the mirrors in order to maximize the common view-angles, which is an important design consideration. Then, after calibrating the projection function of the fisheye lens, we obtain the positions and orientations of the virtual cameras from the external parameters. We also develop two multi-baseline stereo algorithms for the 3D measurement system. The first algorithm transforms the captured image to perspective images, and uses the traditional method to perform stereo determination. The second algorithm directly uses the original captured image along with an analysis of the epipolar geometry. Experimental results show that our system is more effective than traditional stereo methods that use a stereo pair, and it can achieve robust 3D reconstruction

Feature Based Calibration of a Network of Kinect Sensors

The availability of affordable depth sensors in conjunction with common RGB cameras, such as the Microsoft Kinect, can provide robots with a complete and instantaneous representation of the current surrounding environment. However, in the problem of calibrating multiple camera systems, traditional methods bear some drawbacks, such as requiring human intervention. In this thesis, we propose an automatic and reliable calibration framework that can easily estimate the extrinsic parameters of a Kinect sensor network. Our framework includes feature extraction, Random Sample Consensus and camera pose estimation from high accuracy correspondences. We also implement a robustness analysis of position estimation algorithms. The result shows that our system could provide precise data under certain amount noise. Keywords Kinect, Multiple Camera Calibration, Feature Points Extraction, Correspondence, RANSA