PPT-Semantic Segmentation The Task

person grass trees motorbike road Evaluation metric Pixel classification Accuracy Heavily unbalanced Common classes are overemphasized Intersection over Union Average across classes and images. 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. 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 . Deep Learning Seminar. Topaz Gilad, 2016. Semantic Image Segmentation With DCNN and Fully. Connected CRFs. Liang-. Chieh. Chen et al.. ICLR 2015. 1. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. . 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 . 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. Analyzing Semantic Segmentation Using Hybrid Human-Machine CRFs Roozbeh Mottaghi 1 , Sanja Fidler 2 , Jian Yao 2 , Raquel Urtasun 2 , Devi Parikh 3 1 UCLA 2 TTI Chicago YIBO CAO Email: | LinkedI n : https://www.linkedin.com/in/yibo - cao - 28b06817b/ Tel: 412 - 708 - 5295 | Personal Webpage: https://yibo - cao.github.io/ EDUCATION Carnegie Mellon University Pitts 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 .