PPT-Feedforward semantic segmentation with zoom-out features

Author : luanne-stotts | Published Date : 2018-09-21

Mostajabi Yadollahpour and Shakhnarovich Toyota Technological Institute at Chicago Main Ideas Casting semantic segmentation as classifying a set of superpixels

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Feedforward semantic segmentation with zoom-out features: Transcript

Mostajabi Yadollahpour and Shakhnarovich Toyota Technological Institute at Chicago Main Ideas Casting semantic segmentation as classifying a set of superpixels Extracting CNN features from different levels of spatial context around the . Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother*, Philip H. S. Torr. Torr Vision Group, University of Oxford. *The . Technische Universität . Dresden. Traditional Goal. By: A’laa . Kryeem. Lecturer: . Hagit. Hel-Or. What is . Segmentation from . Examples. ?. Segment an image based on one (or more) correctly segmented image(s) assumed to be from the same . domain. -concepts and facts. -several similar models describe . the organization . of semantic . memory. 1) Collins & . Quillian’s. Hierarchical Network Model. -nodes to represent individual items, ideas, organized hierarchically. Katrin Erk. University of Texas at . Austin. Meaning in Context Symposium. München. September 2015. Joint work with Gemma . Boleda. Semantic features by example: . Katz & Fodor. Different meanings of a word characterized by lists of semantic features. Anurag Arnab. Collaborators: . sadeep. . Jayasumana. , . shuai. . zheng. , Philip . torr. Introduction. Semantic Segmentation. Labelling every pixel in an image. A key part of Scene Understanding. Second-Order Pooling. João Carreira. 1,2. , Rui Caseiro. 1. , Jorge Batista. 1. , Cristian Sminchisescu. 2. 1. . Institute of Systems and Robotics. ,. . University of Coimbra. 2. . Faculty of Mathematics and Natural . Mostajabi. , . Yadollahpour. . and . Shakhnarovich. Toyota . Technological Institute at Chicago. Main Ideas. Casting semantic segmentation as classifying a set of . superpixels. .. Extracting CNN features from different levels of spatial context around the . Paper by John McCormac, Ankur Handa, Andrew Davison, and Stefan Leutenegger Dyson Robotics Lab, Imperial College London. Presentation by Chris Conte. Hey robot, go fetch me a Twix from the snack bar. person 1. person 2. horse 1. horse 2. R-CNN: Regions with CNN features. Input. image. Extract region. proposals (~2k / image). Compute CNN. features. Classify regions. (linear SVM). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. SkinWhole. . Slide Images. Hongming. . Xua. , Cheng . Lub. , Richard . Berendtc. , Naresh . Jhac. , . Mrinal. . Mandala. University of Alberta. Highlights. A framework for whole slide skin image analysis. person. grass. trees. motorbike. road. Evaluation metric. Pixel classification!. Accuracy?. Heavily unbalanced. Common classes are over-emphasized. Intersection over Union. Average across classes and images. Altered time for OH tomorrow: 9:00-10:00 am.. Please complete mid-semester feedback. Semantic Segmentation. The Task. person. grass. trees. motorbike. road. Evaluation metric. Pixel classification!. Accuracy?. Juan Carlos . Niebles. and Ranjay Krishna. Stanford Vision and Learning Lab. What we will learn today. Introduction to segmentation and clustering. Gestalt theory for perceptual grouping. Agglomerative clustering. Rushikesh. . Chopade. , Aditya . Stanam, University of Iowa. , & Shrikant Pawar.. Department of Geology and . GeophysicsIndian. Institute of Technology, . KharagpurKharagpur. West Bengal 721302 India .

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