Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

Sidescan sonars are increasingly used in underwater search and rescue for drowning
victims, wrecks and airplanes. Automatic object classification or detection methods can help …

Semantic segmentation is a challenging task which requires both solid unanimous global
context and rich spatial information. Recent methods ignore adaptively capturing of valid …

Purpose We investigated the parameter configuration in the automatic liver and tumor
segmentation using a convolutional neural network based on 2.5 D model. The …

Recently, the U-Net-like convolutional neural network architecture composed of encoder,
decoder, and symmetric skip-connection has shown impressive performance and has …

Recent studies have greatly promoted the development of semantic segmentation. Most
state-of-the-art methods adopt fully convolutional networks (FCNs) to accomplish this task, in …

The way features propagate in Fully Convolutional Networks is of momentous importance to
capture multi-scale contexts for obtaining precise segmentation masks. This paper proposes …

Road scene understanding is a critical component in an autonomous driving system.
Although the deep learning-based road scene segmentation can achieve very high …

Although human parsing has made great progress, it still faces a challenge, ie, how to
extract the whole foreground from similar or cluttered scenes effectively. In this paper, we …

CycleGAN is widely used in various image translations, such as thermal-infrared–visible-
image translation, near-infrared–visible-image translation, and shortwave-infrared–visible …