Evaluation of global image thresholding for change detection

By Paul L. Rosin, Efstathios V. Ioannidis. Pattern Recognition Letters, 2003

411 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5e0e44655617c28afb7986f14eabdfb5633d22e3

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Multitask learning for large-scale semantic change detection (2018, background): “The authors of Hussain et al. (2013) and Rosin and Ioannidis (2003) noted that the performance of such algorithms is scene dependent.”
Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction (2020, methodology): “Moreover, the quality of DIs is quantitatively evaluated by the receiver operating characteristics (ROC) plot and the area under curve (AUC) of ROC [68].”
CDnet 2014: An Expanded Change Detection Benchmark Dataset (2014, background): “Rosin-Ioannidis, 2003 [24] Report results for 8 method with home-made videos showing balls rolling on the floor.”
Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (2016, background): “Overall error (OE) is the sum of FN and FP [48].”
Changedetection.net: A new change detection benchmark dataset (2012, background): “• Rosin and Ioannidis, 2003 [21] use a labeling program that automatically locates moving objects based on their position in space and properties such as color, size, shape, etc.”
Image change detection algorithms: a systematic survey (2005, methodology): “Rosin [65], [66] surveyed and reported experiments on many different criteria for choosing .”
Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, neutral): “Various automated threshold selection algorithms have been proposed, and Rosin and Ioannidis (2003) argued that the performance of these algorithms is scene-dependent.”
A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images (2013, methodology): “Statistical class parameters have been estimated with the unsupervised method presented in [31] based on the expectation–maximization algorithm [31], [69], [91]–[93].”
Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art (2008, methodology): “Based on the above mentioned quantities, Rosin [136] described three methods for quantifying a classifier’s performance:”