In computer vision, edge and object contour detection is essential for higher-level vision
tasks, such as shape matching, visual salience, image segmentation, and object recognition …

This paper proposes a Deep Learning based edge detector, which is inspired on both HED
(Holistically-Nested Edge Detection) and Xception networks. The proposed approach …

Edge detection is the basis of many computer vision applications. State of the art
predominantly relies on deep learning with two decisive factors: dataset content and network …

Prostate segmentation is an important step in prostate volume estimation, multi-modal image
registration, and patient-specific anatomical modeling for surgical planning and image …

Deep learning currently rules edge detection. However, the impressive progress heavily
relies on high-quality manually annotated labels which require a significant amount of labor …

As the basis of mid-level and high-level vision tasks, edge detection has great significance
in the field of computer vision. Edge detection methods based on deep learning usually …

Preserving edges in a noisy environment is a challenging task as even some of the latest
end-to-end deep learning (DL) algorithms continue to struggle in achieving high pixel-level …

The problem of removing illuminant variations to preserve the colours of objects (colour
constancy) has already been solved by the human brain using mechanisms that rely largely …

Abstract Deep Convolutional Neural Networks (DNNs) have achieved superhuman
accuracy on standard image classification benchmarks. Their success has reignited …

Neurophysiological evidence demonstrates that classical receptive field responses in the
primary visual cortex can be modulated by the non-classical receptive field. Although …