Recent advances on image edge detection: A comprehensive review
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 …
computer vision and image processing. Edge contours extracted from images are widely …
Segment anything
Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …
image segmentation. Using our efficient model in a data collection loop, we built the largest …
Low-light image enhancement via structure modeling and guidance
This paper proposes a new framework for low-light image enhancement by simultaneously
conducting the appearance as well as structure modeling. It employs the structural feature to …
conducting the appearance as well as structure modeling. It employs the structural feature to …
Fast segment anything
The recently proposed segment anything model (SAM) has made a significant influence in
many computer vision tasks. It is becoming a foundation step for many high-level tasks, like …
many computer vision tasks. It is becoming a foundation step for many high-level tasks, like …
Dense extreme inception network for edge detection
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 …
predominantly relies on deep learning with two decisive factors: dataset content and network …
The treasure beneath multiple annotations: An uncertainty-aware edge detector
Deep learning-based edge detectors heavily rely on pixel-wise labels which are often
provided by multiple annotators. Existing methods fuse multiple annotations using a simple …
provided by multiple annotators. Existing methods fuse multiple annotations using a simple …
Refined edge detection with cascaded and high-resolution convolutional network
Edge detection is represented as one of the most challenging tasks in computer vision, due
to the complexity of detecting the edges or boundaries in real-world images that contains …
to the complexity of detecting the edges or boundaries in real-world images that contains …
UTLNet: Uncertainty-aware transformer localization network for RGB-depth mirror segmentation
Mirror segmentation, an emerging discipline in the field of computer vision, involves the
identification and marking of mirrors in an image. Current mirror segmentation methods rely …
identification and marking of mirrors in an image. Current mirror segmentation methods rely …
Hybrid CNN-transformer features for visual place recognition
Y Wang, Y Qiu, P Cheng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Visual place recognition is a challenging problem in robotics and autonomous systems
because the scene undergoes appearance and viewpoint changes in a changing world …
because the scene undergoes appearance and viewpoint changes in a changing world …
Tiny and efficient model for the edge detection generalization
Most high-level computer vision tasks rely on low-level image operations as their initial
processes. Operations such as edge detection, image enhancement, and super-resolution …
processes. Operations such as edge detection, image enhancement, and super-resolution …