Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials - Philipp Krähenbühl
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Most state-of-the-art techniques for multi-class image segmentation and labeling use conditional random fields defined over pixels or image regions. While region-level models often feature dense pairwise connectivity, pixel-level models are considerably larger and have only permitted sparse graph structures. In this paper, w
CVPR2015読み会 "Joint Tracking and Segmentation of Multiple Targets"
第30回（前編） コンピュータビジョン勉強会＠関東 http://kantocv.connpass.com/event/16586/ CVPR2015読み会発表資料 "Joint Tracking and Segmentation of Multiple Targets"Read less
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Video Segmentation by Non-Local Consensus Voting
GitHub - lucasb-eyer/pydensecrf: Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.
Fully Convolutional Networks for Semantic Segmentation
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Automatic fish segmentation via double local thresholding for trawl-based underwater camera systems
