LambdaNet: A Fully Convolutional Architecture for Directiona

Best Paper Imaging and Multimedia Analytics in a Web and Mobile World 2020 Conference

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection
Bryan Blakeslee, Rochester Institute of Technology (US)

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".

Bryan Blakeslee is a recent graduate of Rochester Institute of Technology’s computer engineering program. His areas of interest are deep learning and embedded systems.

8/26/2020 - 8/26/2020

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