Though deep learning-based object detection methods have achieved promising results on
the conventional datasets, it is still challenging to locate objects from the low-quality images …

Convolutional neural network (CNN)-based autonomous driving object detection algorithms
have excellent detection results on conventional datasets, but the detector performance can …

Most modern commodity imaging systems we use directly for photography—or indirectly rely
on for downstream applications—employ optical systems of multiple lenses that must …

The race for miniature color cameras using flat meta-optics has rapidly developed the end-to-
end design framework using neural networks. Although a large body of work has shown the …

Semantic segmentation on driving-scene images is vital for autonomous driving. Although
encouraging performance has been achieved on daytime images, the performance on …

Real-world scenes have a dynamic range of up to 280 dB that today's imaging sensors
cannot directly capture. Existing live vision pipelines tackle this fundamental challenge by …

Cameras are the primary sensor in automated driving systems. They provide high
information density and are optimal for detecting road infrastructure cues laid out for human …

Despite their proficiency with typical environmental datasets, deep learning-based object
detection algorithms struggle when faced with diverse adverse weather conditions …

Camera traps serve as a valuable tool for wildlife monitoring, generating a vast collection of
images for ecologists to conduct ecological investigations, such as species identification and …

Many industrial and scientific applications require optimization of one or more objectives by
tuning dozens or hundreds of input parameters. While Bayesian optimization has been a …