Edge detection is one of the most important and fundamental problems in the field of
computer vision and image processing. Edge contours extracted from images are widely …

To maintain the reliability, availability, and sustainability of electricity supply, electricity
companies regularly perform visual inspections on their transmission and distribution …

Abstract Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level
performance in edge detection with the rich and abstract edge representation capacities …

Convolutional neural networks have made significant progresses in edge detection by
progressively exploring the context and semantic features. However, local details are …

The commonly used method to reduce the dose is to reduce the tube current. The number of
photons received by the detector decreases, making the CT image obtained by analytical …

In recent years, many deep learning techniques have been applied to the image inpainting
problem: the task of filling incomplete regions of an image. However, these models struggle …

Over the last few years, deep learning techniques have yielded significant improvements in
image inpainting. However, many of these techniques fail to reconstruct reasonable …

Cracks are typical line structures that are of interest in many computer-vision applications. In
practice, many cracks, eg, pavement cracks, show poor continuity and low contrast, which …

Object detection generally requires sliding-window classifiers in tradition or anchor-based
predictions in modern deep learning approaches. However, either of these approaches …

We provide a two-way integration for the widely-adopted ControlNet by integrating external
condition generation algorithms into a single dense prediction method and by integrating its …