PPT-Data-driven Segmentation and Labeling of Freehand Sketches

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Zhe Huang Hongbo Fu Rynson WH Lau City University of Hong Kong Objective Derive partbased segmentation and labeling from a sketch Motivation Assemblybased

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Data-driven Segmentation and Labeling of Freehand Sketches: Transcript

Zhe Huang Hongbo Fu Rynson WH Lau City University of Hong Kong Objective Derive partbased segmentation and labeling from a sketch Motivation Assemblybased modeling . Diverse Data. M. Pawan Kumar. Stanford University. Semantic Segmentation. car. road. grass. tree. sky. Segmentation Models. car. road. grass. tree. sky. MODEL. w. x. y. P(. x. ,. y. ; . w. ). Learn accurate parameters. Segmentation and Optical Flow. Inspiration from psychology. The Gestalt school: Grouping is key to visual perception. “The whole is greater than the sum of its parts”. http://en.wikipedia.org/wiki/Gestalt_psychology. GLEN BISSETT. 2. 2014. Data driven professional scepticism. Outline. What is professional scepticism. ?. What is data. ?. How can one drive the other. ?. How does IDEA help?. What is professional scepticism?. 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. Dan Munoz . Drew Bagnell Martial Hebert. The Labeling Problem. 2. Input. Our Predicted Labels. Road. Tree. Fgnd. Bldg. Sky. The Labeling Problem. 3. The Labeling Problem. Needed: . better. . representation. Freehand sketching . . Interior designing sketching. name: . faiza. . fatima. . id: 201200994. instructor name : . arch.sarah. al-. mutlaq. . . Table of contents . Yassine Benajiba. 1. and . Imed. Zitouni. 2. 1 CCLS, Columbia University. 2 IBM T.J. Watson Research Center. ybenajiba@ccls.columbia.edu. , . izitouni@us.ibm.com. . Outline. The Arabic Language. ATB vs. Morph segmentation. Examples of Sketching Technique. Good Versus Poor Technique for Drawing Lines. Orthographic Sketches. A type of technical drawing that usually shows three different two dimensional views from different perspectives.. Answer Inference for Telegraphic Entity-seeking . Queries. EMNLP 2014. Mandar Joshi. Uma Sawant. Soumen Chakrabarti. IBM Research. IIT Bombay, Yahoo Labs. IIT Bombay. mandarj90@in.ibm.com. uma@cse.iitb.ac.in. https://www.google.com/. search?hl. =. en&site. =. imghp&tbm. =. isch&source. =. hp&biw. =1280&bih=887&q=. davinci sketches&oq. =. davinci sketches&gs_l. =img.3..0i10l8j0i5i10.1328.4173.0.4309.16.16.0.0.0.0.87.1097.16.16.0....0...1ac.1.52.img..0.16.1088.veaqsSj_8dY#facrc=_&. 2D Sketches. 1. 2D Sketches. Profiles:. Closed loop shape that is drawn on a flat 2D plane (referred to as a datum) and is used to create 3D objects.. 2D Profiles consist of:. Points. Lines. Circles. DDDM. “The practice of basing decisions on the analysis of data rather than purely on intuition.” Equally succinctly, data science “as the connective tissue between data-processing technologies (including those for . a User’s Perspective. Stephen Plaza. ?. EM. c. onnectome graph. Presentation goals. Summarize the state of EM segmentation for . connectomics. Better define segmentation evaluation objectives. Propose potential solutions and work directions. Find Accurate Company Information with Firmographic Segmentation: USP Data

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