Segmentationに関するYtraのブックマーク (8)
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Ytra 2016/11/04
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UberNet demo
This website demonstrates a deep learning system trained to address a diverse set of computer vision tasks, spanning low- mid- and high- level vision. Technical details can be found in the following report: Iasonas Kokkinos , UberNet : Training a ‘Universal’ Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory , arxiv , 2016 Try it out here
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COCO - Common Objects in Context
What is COCO? COCO is a new image recognition, segmentation, and captioning dataset. COCO has several features: Object segmentation Recognition in Context Multiple objects per image More than 300,000 images More than 2 Million instances 80 object categories 5 captions per image Keypoints on 100,000 people Tsung-Yi Lin Cornell Tech Genevieve Patterson MSR Matteo Ruggero Ronchi Caltech Yin Cui Corne
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CRF as RNN Semantic Image Segmentation
CRF as RNN Semantic Image Segmentation Live Demo Our work allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of the object. Currently we have trained this model to recognize 20 classes. The demo below allows you to test our algorithm on your own images – have a try and see if you can fool it, if you get some good examples you
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Graph Based Image Segmentation Tutorial
Tutorial Abstract: Image segmentation has come a long way. Using just a few simple grouping cues, one can now produce rather impressive segmentation on a large set of images. Behind this development, a major converging point is the use of graph based technique. Graph cut provides a clean, flexible formulation for image segmentation. It provides a convenient language to encode simple local segmenta
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