Regime Change Detection Using Directional Change Indicators

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

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Recurrence statistics for anomalous diffusion regime change detection

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2018.07.014 ◽

2018 ◽

Vol 128 ◽

pp. 380-394 ◽

Cited By ~ 6

Author(s):

Grzegorz Sikora ◽

Agnieszka Wyłomańska ◽

Diego Krapf

Keyword(s):

Change Detection ◽

Anomalous Diffusion ◽

Regime Change ◽

Diffusion Regime ◽

Recurrence Statistics

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Regime Change Detection in Irregularly Sampled Time Series

Advances in Nonlinear Geosciences ◽

10.1007/978-3-319-58895-7_18 ◽

2017 ◽

pp. 357-368 ◽

Cited By ~ 1

Author(s):

Norbert Marwan ◽

Deniz Eroglu ◽

Ibrahim Ozken ◽

Thomas Stemler ◽

Karl-Heinz Wyrwoll ◽

...

Keyword(s):

Time Series ◽

Change Detection ◽

Regime Change

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Multi-directional change detection between point clouds

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2020.12.002 ◽

2021 ◽

Vol 172 ◽

pp. 95-113

Author(s):

Jack G. Williams ◽

Katharina Anders ◽

Lukas Winiwarter ◽

Vivien Zahs ◽

Bernhard Höfle

Keyword(s):

Change Detection ◽

Point Clouds ◽

Directional Change

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Vivid Imagers Are Better at Detecting Salient Changes

Journal of Individual Differences ◽

10.1027/1614-0001.27.4.218 ◽

2006 ◽

Vol 27 (4) ◽

pp. 218-228 ◽

Cited By ~ 13

Author(s):

Paul Rodway ◽

Karen Gillies ◽

Astrid Schepman

Keyword(s):

Individual Differences ◽

Change Detection ◽

Visual Imagery ◽

Detection Task ◽

Level Of Detail ◽

Detection Accuracy ◽

Change Detection Task ◽

Term Change ◽

High Level

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17 s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

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