Segmentation Kyongil Yoon. Segmentation Obtain a compact representation of what is helpful (in the image) No comprehensive theory of segmentation Human.

Slides:



Advertisements
Similar presentations
Computer Vision - A Modern Approach Set: Probability in segmentation Slides by D.A. Forsyth Missing variable problems In many vision problems, if some.
Advertisements

Segmentácia farebného obrazu
Foreground Background detection from video Foreground Background detection from video מאת : אבישג אנגרמן.
Adviser : Ming-Yuan Shieh Student ID : M Student : Chung-Chieh Lien VIDEO OBJECT SEGMENTATION AND ITS SALIENT MOTION DETECTION USING ADAPTIVE BACKGROUND.
Segmentation Course web page: vision.cis.udel.edu/~cv May 2, 2003  Lecture 29.
Human-Computer Interaction Human-Computer Interaction Segmentation Hanyang University Jong-Il Park.
Formation et Analyse d’Images Session 8
Lecture 6 Image Segmentation
Image segmentation. The goals of segmentation Group together similar-looking pixels for efficiency of further processing “Bottom-up” process Unsupervised.
Normalized Cuts and Image Segmentation Jianbo Shi and Jitendra Malik, Presented by: Alireza Tavakkoli.
Region Segmentation. Find sets of pixels, such that All pixels in region i satisfy some constraint of similarity.
Image Segmentation Chapter 14, David A. Forsyth and Jean Ponce, “Computer Vision: A Modern Approach”.
Segmentation and Clustering. Segmentation: Divide image into regions of similar contentsSegmentation: Divide image into regions of similar contents Clustering:
Computer Vision - A Modern Approach Set: Fitting Slides by D.A. Forsyth Fitting Choose a parametric object/some objects to represent a set of tokens Most.
Problem Sets Problem Set 3 –Distributed Tuesday, 3/18. –Due Thursday, 4/3 Problem Set 4 –Distributed Tuesday, 4/1 –Due Tuesday, 4/15. Probably a total.
Segmentation Divide the image into segments. Each segment:
Announcements Project 2 more signup slots questions Picture taking at end of class.
Fitting. We’ve learned how to detect edges, corners, blobs. Now what? We would like to form a higher-level, more compact representation of the features.
Computer Vision Segmentation Marc Pollefeys COMP 256 Some slides and illustrations from D. Forsyth, T. Darrel,...
Segmentation by Clustering Reading: Chapter 14 (skip 14.5) Data reduction - obtain a compact representation for interesting image data in terms of a set.
Image Segmentation Some slides: courtesy of O. Capms, Penn State, J.Ponce and D. Fortsyth, Computer Vision Book.
1 Integration of Background Modeling and Object Tracking Yu-Ting Chen, Chu-Song Chen, Yi-Ping Hung IEEE ICME, 2006.
Fitting a Model to Data Reading: 15.1,
Fitting. Choose a parametric object/some objects to represent a set of tokens Most interesting case is when criterion is not local –can’t tell whether.
Lecture 10: Robust fitting CS4670: Computer Vision Noah Snavely.
Announcements vote for Project 3 artifacts Project 4 (due next Wed night) Questions? Late day policy: everything must be turned in by next Friday.
Computer Vision Segmentation Marc Pollefeys COMP 256 Some slides and illustrations from D. Forsyth, T. Darrel,...
Segmentation Slides Credit: Jim Rehg, G.Tech. Christopher Rasmussen, UD John Spletzer, Lehigh Also, Slides adopted from material provided by David Forsyth.
Computer Vision - A Modern Approach Set: Segmentation Slides by D.A. Forsyth Segmentation and Grouping Motivation: not information is evidence Obtain a.
Fitting.
כמה מהתעשייה? מבנה הקורס השתנה Computer vision.
1 Activity and Motion Detection in Videos Longin Jan Latecki and Roland Miezianko, Temple University Dragoljub Pokrajac, Delaware State University Dover,
Graph-based Segmentation
Image Segmentation Image segmentation is the operation of partitioning an image into a collection of connected sets of pixels. 1. into regions, which usually.
Computer Vision James Hays, Brown
Segmentation Techniques Luis E. Tirado PhD qualifying exam presentation Northeastern University.
Characterizing activity in video shots based on salient points Nicolas Moënne-Loccoz Viper group Computer vision & multimedia laboratory University of.
Segmentation by Clustering
Segmentation using eigenvectors Papers: “Normalized Cuts and Image Segmentation”. Jianbo Shi and Jitendra Malik, IEEE, 2000 “Segmentation using eigenvectors:
Computer Vision Lecture 5. Clustering: Why and How.
Segmentation Course web page: vision.cis.udel.edu/~cv May 7, 2003  Lecture 31.
Chapter 14: SEGMENTATION BY CLUSTERING 1. 2 Outline Introduction Human Vision & Gestalt Properties Applications – Background Subtraction – Shot Boundary.
CSE 185 Introduction to Computer Vision Pattern Recognition 2.
CS654: Digital Image Analysis Lecture 25: Hough Transform Slide credits: Guillermo Sapiro, Mubarak Shah, Derek Hoiem.
Fitting: The Hough transform
CS654: Digital Image Analysis Lecture 30: Clustering based Segmentation Slides are adapted from:
Clustering.
EECS 274 Computer Vision Model Fitting. Fitting Choose a parametric object/some objects to represent a set of points Three main questions: –what object.
Image Segmentation Shengnan Wang
Final Review Course web page: vision.cis.udel.edu/~cv May 21, 2003  Lecture 37.
Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos VC 15/16 – TP10 Advanced Segmentation Miguel Tavares.
Fitting.
Motion Segmentation at Any Speed Shrinivas J. Pundlik Department of Electrical and Computer Engineering, Clemson University, Clemson, SC.
Normalized Cuts and Image Segmentation Patrick Denis COSC 6121 York University Jianbo Shi and Jitendra Malik.
Image Mosaicing with Motion Segmentation from Video Augusto Roman, Taly Gilat EE392J Final Project 03/20/01.
Grouping and Segmentation. Sometimes edge detectors find the boundary pretty well.
K-Means Segmentation.
Video object segmentation and its salient motion detection using adaptive background generation Kim, T.K.; Im, J.H.; Paik, J.K.;  Electronics Letters 
Image Representation and Description – Representation Schemes
Miguel Tavares Coimbra
Fitting.
Motion Detection And Analysis
Computer Vision Lecture 12: Image Segmentation II
Segmentation and Grouping
Computer Vision - A Modern Approach
Grouping.
Emel Doğrusöz Esra Ataer Muhammet Baştan Tolga Can
Image Segmentation CS 678 Spring 2018.
Announcements Project 4 out today (due Wed March 10)
Announcements Project 1 is out today help session at the end of class.
Presentation transcript:

Segmentation Kyongil Yoon

Segmentation Obtain a compact representation of what is helpful (in the image) No comprehensive theory of segmentation Human vision: Grouping and Gestalt –Proximity, similarity, common fate, common region, parallelism, closure, symmetry, continuity, familiar configuration

Segmentation by clustering Partitioning vs. grouping Applications –Background subtraction –Shot boundary detection Image segmentation by clustering pixels –Using simple clustering Agglomerative clustering (clustering by merging) Divisive clustering (clustering by splitting) –K-means –Using graph-theoretic clustering Affinity measure Normalized cut –cut(A,B)/assoc(A,V) + cut(A,B)/assoc(B,V)

K-Means Choose k data points to act as cluster centers Until the cluster centers are unchanged Allocate each data point to cluster whose center is nearest Now ensure that every cluster has at least one data point; possible techniques for doing this include supplying empty clusters with a point chosen at random from points far from their cluster center. Replace the cluster centers with the mean of the elements in their clusters. end

Graph Eigenvectors Construct an affinity matrix Compute the eigenvalues and eigenvectors of the affinity matrix Until there are sufficient clusters Take the eigenvector corresponding to the largest unprocessed eigenvalue; zero all components corresponding to elements that have already been clustered, and threshold the remaining components to determine which element belongs to this cluster, choosing a threshold by clustering the components, or using a threshold fixed in advance. If all elements have been accounted for, there are sufficient clusters end

Segmentation by fitting a model To assert that pixels belong together to conform to some model –Large scale explicit model Hough transform –Three problems: What is the line? Which points belong to which line? How many lines? –Point space line space x*cos(t) + y*sin(t) + r = 0, (t, r) line space Half-infinite cylinder –Quantization errors, difficulties with noise Fitting lines, fitting curves –Least square –Total least square

Segmentation by fitting a model(2) Two big problems –Robustness: what if one data point is FAR, and all others fill well? –Missing data: which point is noise and which point is not? Robustness –Outliers: Improve the model either by giving the noise “heavier tails” or allowing an explicit outlier model –M-estimators Assuming that somewhere in the collection of process close to our model is the real process, and it just happens to be the one that makes the estimator produce the worst possible estimates

Segmentation by fitting a model(3) RANSAC (RAMdom SAmple Consensus) –Searching for a random sample that leads to a fit on which many of the data points agree –Determine n : the smallest # of points required k : the # of iterations required t : the threshold used to identify a point that fits well d : the # of nearby points required Until k iterations have occurred Pick n sample points Fit to that set of n points For each data point outside the sample Test distance; if the distance < t, it is close If there are d or more points close, this is a good fit. Refit the line using all these points End Use the best fit from this collection, using the fitting error as a criterion –Need to choose 3 parameters # of samples required (n) Telling whether a point is close (t) # of points that must agree (d)

Segmentation E. Borenstein and S. Ullman. Class-specific, top-down segmentation, In Proc. 7th Europ. Conf. Comput. Vision, May 2002 J. Freixenet, X. Munoz, D. Raba, J. Marti, and X. Cufi, Yet another survey on image segmentation: region and boundary information integration, University of Girona, Institute of Information and Applications, ECCV 2002, LNCS 2352, pp , 2002 Harwood, D., Chang, S., Davis, L.S., Interpreting aerial photographs by segmentation and search, IUW(87), pp D. C. Alexander and B. F. Buxton. Statistical modeling of colour data, International Journal of Computer Vision, 44(2): , September Friedman, N. and Russell, S Image segmentation in video sequences: A probabilistic approach, In Proceedings 13. Conf. on Uncertainty in Articial Intelligence Ahmed Elgammal, David Harwood, Larry Davis, Non-parametric model for background subtraction, 6th European Conference on Computer Vision. Dublin, Ireland, June/July 2000 T. H. Chalidabhongse, K. Kim, D. Harwood and L. Davis, A perturbation method for evaluating background subtraction algorithms, Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, Nice, France, Oct , 2003 (in conjunction with ICCV'03) J. Shi and J. Malik. Normalized cuts and image segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'97), pages , 1997 J. Shi and J. Malik, "Motion segmentation and tracking using normalized cuts", in International Conference on Computer Vision, January 1998, Bombay, India E. Sharon, A. Brandt and R. Basri, Fast Multiscale Image Segmentation, Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp , 2000 C. Stauffer and W.E.L. Grimson. Adaptive background mixture models for real-time tracking. In CVPR99, pages II: , 1999 Weiss, Y., Segmentation using eigenvectors: A unifying view, Proc. 7th Int. Conf. Computer Vision, 1999,