Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Lazy snapping
1.109
Zitationen
4
Autoren
2004
Jahr
Abstract
In this paper, we present Lazy Snapping , an interactive image cutout tool. Lazy Snapping separates coarse and fine scale processing, making object specification and detailed adjustment easy . Moreover, Lazy Snapping provides instant visual feedback, snapping the cutout contour to the true object boundary efficiently despite the presence of ambiguous or low contrast edges. Instant feedback is made possible by a novel image segmentation algorithm which combines graph cut with pre-computed over-segmentation. A set of intuitive user interface (UI) tools is designed and implemented to provide flexible control and editing for the users. Usability studies indicate that Lazy Snapping provides a better user experience and produces better segmentation results than the state-of-the-art interactive image cutout tool, Magnetic Lasso in Adobe Photoshop.
Ähnliche Arbeiten
Deep Residual Learning for Image Recognition
2016 · 216.185 Zit.
U-Net: Convolutional Networks for Biomedical Image Segmentation
2015 · 86.000 Zit.
ImageNet classification with deep convolutional neural networks
2017 · 75.547 Zit.
Very Deep Convolutional Networks for Large-Scale Image Recognition
2014 · 75.405 Zit.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
2016 · 52.698 Zit.