Shuai Zheng et al. Dense Semantic Image Segmentation with Objects and Attributes. IEEE CVPR 2014.01/06/20141/6 Dense semantic image segmentation with objects.

Slides:



Advertisements
Similar presentations
POSE–CUT Simultaneous Segmentation and 3D Pose Estimation of Humans using Dynamic Graph Cuts Mathieu Bray Pushmeet Kohli Philip H.S. Torr Department of.
Advertisements

Mean-Field Theory and Its Applications In Computer Vision1 1.
Vibhav Vineet, Jonathan Warrell, Paul Sturgess, Philip H.S. Torr
1 Vibhav Vineet, Jonathan Warrell, Paul Sturgess, Philip H.S. Torr Improved Initialisation and Gaussian Mixture Pairwise Terms for Dense Random Fields.
Indoor Segmentation and Support Inference from RGBD Images Nathan Silberman, Derek Hoiem, Pushmeet Kohli, Rob Fergus.
SAIGON INTERIORS AMERICAN, FRENCH, GERMAN and PHILIPPINE OWNED.
The Layout Consistent Random Field for detecting and segmenting occluded objects CVPR, June 2006 John Winn Jamie Shotton.
KE CHEN 1, SHAOGANG GONG 1, TAO XIANG 1, CHEN CHANGE LOY 2 1. QUEEN MARY, UNIVERSITY OF LONDON 2. THE CHINESE UNIVERSITY OF HONG KONG CUMULATIVE ATTRIBUTE.
Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction Ľubor Ladický, Paul Sturgess, Christopher Russell, Sunando Sengupta, Yalin.
Scene Labeling Using Beam Search Under Mutex Constraints ID: O-2B-6 Anirban Roy and Sinisa Todorovic Oregon State University 1.
SPONSORED BY SA2014.SIGGRAPH.ORG Annotating RGBD Images of Indoor Scenes Yu-Shiang Wong and Hung-Kuo Chu National Tsing Hua University CGV LAB.
Large-Scale Object Recognition using Label Relation Graphs Jia Deng 1,2, Nan Ding 2, Yangqing Jia 2, Andrea Frome 2, Kevin Murphy 2, Samy Bengio 2, Yuan.
Large Scale Visual Recognition Challenge (ILSVRC) 2013: Detection spotlights.
GrabCut Interactive Foreground Extraction using Iterated Graph Cuts Carsten Rother Vladimir Kolmogorov Andrew Blake Microsoft Research Cambridge-UK.
Interactive Image Segmentation using Graph Cuts Mayuresh Kulkarni and Fred Nicolls Digital Image Processing Group University of Cape Town PRASA 2009.
Accurate Binary Image Selection From Inaccurate User Input Kartic Subr, Sylvain Paris, Cyril Soler, Jan Kautz University College London, Adobe Research,
Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection CVPR2013 POSTER.
Models for Scene Understanding – Global Energy models and a Style-Parameterized boosting algorithm (StyP-Boost) Jonathan Warrell, 1 Simon Prince, 2 Philip.
Unsupervised Learning of Categorical Segments in Image Collections *California Institute of Technology **Technion Marco Andreetto*, Lihi Zelnik-Manor**,
Robust Higher Order Potentials For Enforcing Label Consistency
TextonBoost : Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation J. Shotton*, J. Winn†, C. Rother†, and A.
Oxford Brookes Seminar Thursday 3 rd September, 2009 University College London1 Representing Object-level Knowledge for Segmentation and Image Parsing:
Original image: 512 pixels by 512 pixels. Probe is the size of 1 pixel. Picture is sampled at every pixel ( samples taken)
What Energy Functions Can be Minimized Using Graph Cuts? Shai Bagon Advanced Topics in Computer Vision June 2010.
An Iterative Optimization Approach for Unified Image Segmentation and Matting Hello everyone, my name is Jue Wang, I’m glad to be here to present our paper.
Measuring Uncertainty in Graph Cut Solutions Pushmeet Kohli Philip H.S. Torr Department of Computing Oxford Brookes University.
LOCUS Demo Stefan Zickler. Two “different” classes Class “Car Side Views” Class “Car Rears”
What, Where & How Many? Combining Object Detectors and CRFs
Manhattan-world Stereo Y. Furukawa, B. Curless, S. M. Seitz, and R. Szeliski 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.
3D Scene Models Object recognition and scene understanding Krista Ehinger.
A Bayesian Approach For 3D Reconstruction From a Single Image
3D LayoutCRF Derek Hoiem Carsten Rother John Winn.
Surface Stereo with Soft Segmentation Michael Bleyer 1, Carsten Rother 2, Pushmeet Kohli 2 1 Vienna University of Technology, Austria 2 Microsoft Research.
Minimizing Sparse Higher Order Energy Functions of Discrete Variables (CVPR’09) Namju Kwak Applied Algorithm Lab. Computer Science Department KAIST 1Namju.
“Secret” of Object Detection Zheng Wu (Summer intern in MSRNE) Sep. 3, 2010 Joint work with Ce Liu (MSRNE) William T. Freeman (MIT) Adam Kalai (MSRNE)
CVPR 2013 Diversity Tutorial Closing Remarks: What can we do with multiple diverse solutions? Dhruv Batra Virginia Tech.
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Creating the Illusion of Motion in 2D Images. Reynold J. Bailey & Cindy M. Grimm Goal To manipulate a static 2D image to produce the illusion of motion.
Associative Hierarchical CRFs for Object Class Image Segmentation Ľubor Ladický 1 1 Oxford Brookes University 2 Microsoft Research Cambridge Based on the.
A gallon of paint costs $12.99 and covers 400 sq ft. How many gallons are needed to paint two coats on the walls and ceiling (not the floor) of a rectangular.
Associative Hierarchical CRFs for Object Class Image Segmentation
Fully Convolutional Networks for Semantic Segmentation
Image Classification over Visual Tree Jianping Fan Dept of Computer Science UNC-Charlotte, NC
Jigsaws: joint appearance and shape clustering John Winn with Anitha Kannan and Carsten Rother Microsoft Research, Cambridge.
Quiz Week 8 Topical. Topical Quiz (Section 2) What is the difference between Computer Vision and Computer Graphics What is the difference between Computer.
Joint Object-Material Category Segmentation from Audio-Visual Cues
 AKA Carpenters Square  Used for measuring right angles  Most common use is for stairs and rafters.
Gaussian Conditional Random Field Network for Semantic Segmentation
WEEK IX Malcolm Collins-Sibley Mentor: Shervin Ardeshir.
MRFs (X1,X2) X3 X1 X2 4 (X2,X3,X3) X4. MRFs (X1,X2) X3 X1 X2 4 (X2,X3,X3) X4.
HFS: Hierarchical Feature Selection for Efficient Image Segmentation
Semantic Object and Instance Segmentation
Combining CNN with RNN for scene labeling (segmentation)
Saliency detection Donghun Yeo CV Lab..
Learning to Detect a Salient Object
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Normalized Cut Loss for Weakly-supervised CNN Segmentation
Computer Vision James Hays
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Chapter 10 Image Segmentation.
What do you think this is a picture of? Use evidence for your answer.
Deep Visual-Semantic Alignments for Generating Image Descriptions
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Semantic segmentation
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
The Image The pixels in the image The mask The resulting image 255 X
Image processing and computer vision pipeline for segmentation and cell detection. Image processing and computer vision pipeline for segmentation and cell.
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
3D Point Capsule Networks Lifting Capsule Networks to Raw 3D Data
Image #1 Image Analysis: What do you think is going on in this picture? Which person, thing, or event does this image relate to (which Word Wall term)?
Presentation transcript:

Shuai Zheng et al. Dense Semantic Image Segmentation with Objects and Attributes. IEEE CVPR /06/20141/6 Dense semantic image segmentation with objects and attributes 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

Shuai Zheng et al. Dense Semantic Image Segmentation with Objects and Attributes. IEEE CVPR /06/20142/6 Traditional Goal Object class segmentation Assigning an object class label to each pixel Wall Cabinet Box ceiling Floor Chair

Shuai Zheng et al. Dense Semantic Image Segmentation with Objects and Attributes. IEEE CVPR /06/20143/6 Our Goal Segmentation with Objects and Attributes Assigning an object class and a set of attribute labels to each pixel Wall: Wood, Painted, textured Cabinet: Glossy, shiny, Cube, plastic Box:cube ceiling: painted, textured Floor : pavement, textured, dry, flat Chair: Wood

Shuai Zheng et al. Dense Semantic Image Segmentation with Objects and Attributes. IEEE CVPR /06/20144/6 Hierarchical Multi-Label Factorial CRF Input ImageObject segmentation Attributes Segmentation Grid CRFFully-connected CRF Hierarchical Fully-connected CRF

Shuai Zheng et al. Dense Semantic Image Segmentation with Objects and Attributes. IEEE CVPR /06/20145/6 Hierarchical Joint Inference Painted attribute segmentation Object Classes Segmentation Cotton attribute segmentation Glossy attribute segmentation Input Image Picture Chair Floor Wall Cotton Glossy Painted

Shuai Zheng et al. Dense Semantic Image Segmentation with Objects and Attributes. IEEE CVPR /06/20146/6 Thank you! Posters P6 16:30-18:30, Thursday, June 26.