CURE-OR (Challenging Unreal and Real Environments for Object Recognition)

Introduced by Temel et al. in CURE-OR: Challenging Unreal and Real Environments for Object Recognition

CURE-OR is a large-scale, controlled, and multi-platform object recognition dataset denoted as Challenging Unreal and Real Environments for Object Recognition. In this dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture that are positioned in five different orientations and captured using five devices including a webcam, a DSLR, and three smartphone cameras in real-world (real) and studio (unreal) environments. The controlled challenging conditions include underexposure, overexposure, blur, contrast, dirty lens, image noise, resizing, and loss of color information.

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