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Alan L. Yuille
Researcher at Johns Hopkins University
Publications - 863
Citations - 99340
Alan L. Yuille is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 119, co-authored 804 publications receiving 78054 citations. Previous affiliations of Alan L. Yuille include Tencent & Harvard University.
Papers
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TL;DR: This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models.
Posted Content
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TL;DR: DeepLab as discussed by the authors proposes atrous spatial pyramid pooling (ASPP) to segment objects at multiple scales by probing an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views.
Posted Content
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
TL;DR: This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF).
Proceedings Article
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
TL;DR: DeepLab as mentioned in this paper combines the responses at the final layer with a fully connected CRF to localize segment boundaries at a level of accuracy beyond previous methods, achieving 71.6% IOU accuracy in the test set.
Journal ArticleDOI
Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation
Song-Chun Zhu,Alan L. Yuille +1 more
TL;DR: A novel statistical and variational approach to image segmentation based on a new algorithm, named region competition, derived by minimizing a generalized Bayes/minimum description length (MDL) criterion using the variational principle is presented.