Computer Vision and Pattern Recognition 2020
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Abstract
This special issue covers a wide range of topics from the area of Computer Vision, Pattern Recognition, and Machine Learning.This breadth of scope is reflected by the papers included in this special issue, which touch topics including geometric Computer Vision, medical image processing, physical scene understanding, and interpretability of deep neural networks.This special issue consists of extended versions of the best papers originally presented at the 42nd German Conference on Pattern Recognition (DAGM GCPR 2020), held virtually between September 28th and October 1st, 2020.This special issue consists of 4 papers that are briefly discussed as follows:The first article, by Annika Hagemann, Moritz Knorr, Holger Janssen, and Christoph Stiller on "Inferring bias and uncertainty in camera calibration" introduces an evaluation scheme to capture the fundamental error sources in camera calibration: systematic errors (biases) and uncertainty (variance).The proposed bias detection method is able to detect systematic errors and can thus be used to reveal inaccuracies in the calibration setup.It can thus be used for camera model selection.A novel resampling-based uncertainty estimator enables uncertainty estimation under non-ideal conditions and thereby extends the classical covariance estimator.In "Assignment Flow For Order-Constrained OCT Segmentation", Dmitrij Sitenko, Bastian Boll, and Christoph Schnörr propose a novel, purely data driven geometric approach to order-constrained 3D Optical Coherence Tomography retinal cell layer segmentation which takes as input data in any metric space and provides basic operations that can be effectively computed in parallel.Compared to established