CURE-OR: Challenging Unreal and Real Environments for Object Recognition

By Dogancan Temel, Jinsol Lee, Ghassan Al-Regib. 2018

45 citations recorded by the source corpus.

In this paper, we introduce a large-scale, controlled, and multi-platform object recognition dataset denoted as Challenging Unreal and Real Environments for Object Recognition (CURE-OR). 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. We utilize CURE-OR dataset to test recognition APIs—Amazon Rekognition and Microsoft Azure Computer Vision—and show that their performance significantly degrades under challenging conditions. Moreover, we investigate the relationship between object recognition and image quality and show that objective quality algorithms can estimate recognition performance under certain photometric challenging conditions. The dataset is publicly available at https://ghassanalregib.com/cure-or/ https://ghassanalregib.com/cure-or/.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

On Robustness and Transferability of Convolutional Neural Networks (2020, background): “Subsequent studies have investigated brittleness to similar corruptions [53, 76], as well as to impulse noise [31], photometric perturbations [62], and small shifts and other transformations [2, 17, 74].”
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations (2019, background): “Temel et al. (2017; 2018); Temel & AlRegib (2018) propose different corrupted datasets for object and trafﬁc sign recognition.”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “Second, common perturbations such as blurriness or shifts in illumination, tend to reduce performance (Dodge and Karam, 2017; Temel et al., 2018; Hendrycks and Dietterich, 2019).”
A Survey of the Four Pillars for Small Object Detection: Multiscale Representation, Contextual Information, Super-Resolution, and Region Proposal (2022, background): “(f ) CURE-OR sample [54].”