The benefits of autonomous image segmentation are readily apparent in many applications
and garners interest from stakeholders in many fields. The wide range of benefits …

Semantic segmentation is a challenging task since it requires excessively more low-level
spatial information of the image compared to other computer vision problems. The accuracy …

Recent progress in computational photography has shown that we can acquire near-infrared
(NIR) information in addition to the normal visible (RGB) band, with only slight modifications …

With advancements in unmanned aerial vehicle (UAV) remote sensing technology, remote
sensing images have emerged as a critical source of research data across various domains …

Recent progress in computational photography has shown that we can acquire physical
information beyond visible (RGB) image representations. In particular, we can acquire near …

Infrared image segmentation is an essential problem in computer vision, especially when
images suffer from the weak illumination and low-resolution problems. In this paper, we …

Despite much research progress in image semantic segmentation, it remains challenging
under adverse environmental conditions caused by imaging limitations of the visible …

Infrared weak-illumination image segmentation is a complex task in computer vision and
intelligence education. In this work, we proposed a novel convolutional neural network for …

Robust and reliable semantic segmentation in complex scenes is crucial for many real-life
applications such as autonomous safe driving and nighttime rescue. In most approaches, it …

Infrared imaging has been used for detecting early-stage breast abnormalities. However,
manual interpretation of these images is subjective and error prone. Automated analysis of …