Limited by the encoder-decoder architecture, learning-based edge detectors usually have
difficulty predicting edge maps that satisfy both correctness and crispness. With the recent …

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 …

This paper proposes a novel zero-shot edge detection with SCESAME, which stands for
Spectral Clustering-based Ensemble for Segment Anything Model Estimation, based on the …

Edge segmentation is well-known to be subjective due to personalized annotation styles
and preferred granularity. However most existing deterministic edge detection methods …

Edge detection is of critical importance for middle-level and high-level tasks in computer
vision. Existing edge detection methods usually use VGG16 as the encoding network and …

Detecting edges in images suffers from the problems of (P1) heavy imbalance between
positive and negative classes as well as (P2) label uncertainty owing to disagreement …

Objective: Paraspinal muscle segmentation and reconstruction from MR images are critical
to implement quantitative assessment of chronic and recurrent low back pains. Due to …

Deep learning-based edge detection methods have shown great advantages and obtained
promising performance. However, most of the current methods only extract features from the …

The rapid development of the Vision Transformer backbones has enabled the capture of
feature information with global dependencies, leading to excellent performance in salient …

Achieving high-accuracy real-time recognition of marine red tide algae images is crucial in
implementing red tide algae early warning systems. However, due to uneven lighting and …