Dimitris N. Metaxas
Rutgers University
826 Papers
7.5K Citations
Dimitris N. Metaxas is an academic researcher from Rutgers University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 91, co-authored 763 publications. Previous affiliations of Dimitris N. Metaxas include University of Maryland, College Park & Stony Brook University.
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Papers
•Posted Content
Self-Attention Generative Adversarial Networks
TL;DR: Self-Attention Generative Adversarial Network (SAGAN) as mentioned in this paper uses attention-driven, long-range dependency modeling for image generation tasks and achieves state-of-the-art results.
2.9K
•Proceedings Article
Self-Attention Generative Adversarial Networks
Han Zhang,Ian Goodfellow,Dimitris N. Metaxas,Augustus Odena +3 more
- 24 May 2019
TL;DR: The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset.
A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI
TL;DR: A novel region-based method for image segmentation, which is able to simultaneously segment the image and estimate the bias field, and the estimated bias field can be used for intensity inhomogeneity correction (or bias correction).
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
Han Zhang,Tao Xu,Hongsheng Li,Shaoting Zhang,Xiaogang Wang,Xiaolei Huang,Dimitris N. Metaxas +6 more
TL;DR: Zhang et al. as discussed by the authors proposed a two-stage generative adversarial network architecture, StackGAN-v1, which sketches the primitive shape and colors of a scene based on a given text description, yielding low-resolution images.
Dynamic 3D models with local and global deformations: deformable superquadrics
Demetri Terzopoulos,Dimitris N. Metaxas +1 more
- 04 Dec 1990
TL;DR: A physically-based approach is presented to fitting complex 3D shapes using a novel class of dynamic models which incorporate the global shape parameters of a conventional superellipsoid with the local degrees of freedom of a spline.
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