Bowling for Calibration: An Undemanding Camera Calibration Procedure Using a Sphere

Camera calibration is a critical problem in computer vision. This paper presents a new method for extrinsic parameters computation: images of a ball rolling on a flat plane in front of the camera are used to compute roll and pitch angles. The calibration is achieved by an iterative Inverse Perspective Mapping (IPM) process that uses an estimation on ball gradient invariant as a stop condition. The method is quick and as easy to use as throw a ball and is particularly suited to be used to quickly calibrate vision systems in unfriendly environments where a grid is not available. The algorithm correctness is demonstrated and its accuracy is computed using both computer generated and real images

MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem

In this paper, a statistically optimal solution to the Perspective-n-Point (PnP) problem is presented. Many solutions to the PnP problem are geometrically optimal, but do not consider the uncertainties of the observations. In addition, it would be desirable to have an internal estimation of the accuracy of the estimated rotation and translation parameters of the camera pose. Thus, we propose a novel maximum likelihood solution to the PnP problem, that incorporates image observation uncertainties and remains real-time capable at the same time. Further, the presented method is general, as is works with 3D direction vectors instead of 2D image points and is thus able to cope with arbitrary central camera models. This is achieved by projecting (and thus reducing) the covariance matrices of the observations to the corresponding vector tangent space.Comment: Submitted to the ISPRS congress (2016) in Prague. Oral Presentation. Published in ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 131-13

3D Reconstruction with Low Resolution, Small Baseline and High Radial Distortion Stereo Images

In this paper we analyze and compare approaches for 3D reconstruction from low-resolution (250x250), high radial distortion stereo images, which are acquired with small baseline (approximately 1mm). These images are acquired with the system NanEye Stereo manufactured by CMOSIS/AWAIBA. These stereo cameras have also small apertures, which means that high levels of illumination are required. The goal was to develop an approach yielding accurate reconstructions, with a low computational cost, i.e., avoiding non-linear numerical optimization algorithms. In particular we focused on the analysis and comparison of radial distortion models. To perform the analysis and comparison, we defined a baseline method based on available software and methods, such as the Bouguet toolbox [2] or the Computer Vision Toolbox from Matlab. The approaches tested were based on the use of the polynomial model of radial distortion, and on the application of the division model. The issue of the center of distortion was also addressed within the framework of the application of the division model. We concluded that the division model with a single radial distortion parameter has limitations

Visual Information Retrieval in Endoscopic Video Archives

In endoscopic procedures, surgeons work with live video streams from the inside of their subjects. A main source for documentation of procedures are still frames from the video, identified and taken during the surgery. However, with growing demands and technical means, the streams are saved to storage servers and the surgeons need to retrieve parts of the videos on demand. In this submission we present a demo application allowing for video retrieval based on visual features and late fusion, which allows surgeons to re-find shots taken during the procedure.Comment: Paper accepted at the IEEE/ACM 13th International Workshop on Content-Based Multimedia Indexing (CBMI) in Prague (Czech Republic) between 10 and 12 June 201

Bowling for Calibration: An Undemanding Camera Calibration Procedure Using a Sphere

Camera calibration is a critical problem in computer vision. This paper presents a new method for extrinsic parameters computation: images of a ball rolling on a flat plane in front of the camera are used to compute roll and pitch angles. The calibration is achieved by an iterative Inverse Perspective Mapping (IPM) process that uses an estimation on ball gradient invariant as a stop condition. The method is quick and as easy to use as throw a ball and is particularly suited to be used to quickly calibrate vision systems in unfriendly environments where a grid is not available. The algorithm correctness is demonstrated and its accuracy is computed using both computer generated and real images