Content-based Image and Video Retrieval. Image Segmentation

Size: px
Start display at page:

Download "Content-based Image and Video Retrieval. Image Segmentation"

Transcription

1 Content-based Image and Video Retrieval Vorlesung, SS 2011 Image Segmentation /

2 Image Segmentation One of the key problem in computer vision Identification of homogenous region in the image Partition an image into meaningful regions with respect to a particular application The segmentation is based on measurements taken from the image and might be greylevel, colour, texture, depth or motion (in video) Image segmentation and descriptors 2

3 Different Examples Search in image collections Find representations that make sense to the user and is related to picture content Video summarization / shot boundary detection Find similar frames, represent subsequences by key frame Finding people Specific detectors, part-based detectors Finding buildings Finding machine parts Background subtraction Image segmentation and descriptors 3

4 Motivation Before high-level reasoning on image, it can be broken down into it major structural components Necessary for extracting reasonable local features (color, texture, etc.) Simplify or change image representation into more meaningful one for ease of analysis Image segmentation and descriptors 4

5 Difficulties What is correct segmentation? No single correct answer Interpretation depends on prior world knowledge World knowledge is difficult to represent Alternative segmentations Image segmentation and descriptors 5

6 Correct Segmentation Image segmentation and descriptors 6

7 Good Segmentation? Typical assumptions (inspired from human vision): Intensity / color coherence Texture coherence Motion coherence Image segmentation and descriptors 7

8 Image Segmentation Categories: Pixel-based Segmentation Region-based Segmentation (Edge-based Segmentation) Graph-based Segmentation Superpixels Image segmentation and descriptors 8

9 Pixel-based Segmentation Thresholding Clustering Image segmentation and descriptors 9

10 Thresholding Determine the best threshold given a histogram of intensities Automatic thresholding P-tile method Mode method Local adaptive method Limitation of thresholding Use global information Ignore spatial relationships among pixels Image segmentation and descriptors 10

11 Thresholding Determine the best threshold given a histogram of intensities Simplest way to segment an image: separate light and dark regions T Image segmentation and descriptors 11

12 P-tile method Use the a priori knowledge about the size of the object: assume an object with size p Choose the threshold such that p% of the overall histogram is determined p% 100% Obviously limited use Image segmentation and descriptors 12

13 Mode-Method Find the peaks (modes) of the histogram and the local minimum between them Set threshold to the pixel value of the local minimum T 255 Not trivial to find peaks and local minimum on a noisy histogram Ignore local peaks Maximize peakiness Image segmentation and descriptors 13

14 Adaptive-Method One single global threshold does not work for uneven illumination Local adaptive method Divide an image into mxm subimages and determine a threshold for each subimage Image segmentation and descriptors 14

15 Double Threshoulding-Method Also called hysteresis thresholding Starting from a conservative initial threshold, determine the core parts of the object Continuing from this core part, grow this object by including neighboring pixels which are between and Image segmentation and descriptors 15

16 Clustering Process of partitioning a set of patterns into clusters find subsets of points which are close together Cluster pixels based on Intensity values Color properties Motion/optical flow properties Texture measurements etc. Input: set of measurements. Output: set of clusters and their centers Image segmentation and descriptors 16

17 Simple Clustering Approaches Agglomerative Clustering (Merging) 1. Make each point (pixel) a separate cluster 2. Merge clusters with smallest inter-cluster distance until clustering is satisfactory Divisive Clustering (Splitting) 1. Constract a single cluster using all points 2. Split clusters with largest inter-cluster distance until clustering is satisfactory Difficulties: Choice of inter-cluster distances Stopping criterion (how many cluster are there?) Image segmentation and descriptors 17

18 Segmentation by k-means Simple clustering methods use greedy approaches Alternative: Formulate an objective function that should be optimized Assuming that we know that there should be k-clusters, a good objective function would be Where x j is a point coordinate, c j is a cluster center If allocation of points to clusters where known, centers could be easily computed But this is not the case Image segmentation and descriptors 18

19 k-means algorithm Define iterative algorithm: Assume the cluster centers are known and allocate each point to closest cluster Assume allocation is known and choose new set of cluster centers. Each center is the mean of the points allocated to the that cluster Algorithm: Choose initial mean values for k regions Classify n pixels by assigning them to closest mean Recompute the means as the average of samples in their (new) classes Continue till there is no change in mean values Image segmentation and descriptors 19

20 Color Clustering Examples Clustering in RGB space Original images Segmented images 9 clusters 5 clusters 4 clusters Image segmentation and descriptors 20

21 Region-based Segmentation The main idea in region-based segmentation techniques is to identify different regions in an image that have similar features (gray level, colour, texture, etc.). There are two main region-based image segmentation techniques: Region merging Region splitting Image segmentation and descriptors 21

22 Formulation of Region-based Segmentation Let represent the entire image. Segmentation can be viewed as a process that partitions in to disjoint regions,, such that: a) b) is a connected region, c) for all d) for e) for where is a logical predicate over points in set and is the empty set. Image segmentation and descriptors 22

23 Region Merging Merge two adjacent regions if they have similar properties according to some criterion. What does similar mean? Examples: similar average values : small spread of gray values : Note: non-transitiv A similar to B, and B similar to C does not imply that A is similar to C. Image segmentation and descriptors 23

24 Region Merging Start with an initial segmentation e.g. By thresholding Form the Region Adjacency Graph (RAG) Regions are the nodes Adjacency relations are the links Initial segmentation RAG Image segmentation and descriptors 24

25 Region Merging For each region in the image do: Consider its adjacent regions and test if they are similar If they are similar, merge them and update the RAG Repeat the merging steps until there are no more merges. merge Image segmentation and descriptors 25

26 Region Splitting Quad-tree decomposition: Subdivide the entire image successively into smaller and smaller quadrant regions until for any region, The subdivision is represented with quad tree Image segmentation and descriptors 26

27 The Quadtree Representation Quadtrees: Trees where nodes have 4 children Build quadtree: Nodes represent regions Every time a region is split, it s node give birth to 4 children Leaves are nodes for uniform regions R Image segmentation and descriptors 27

28 Region Splitting & Merging Splitting only results in adjacent regions with identical properties The final result can be obtained through merging the quadtree Siblings that are similar can be merged Image segmentation and descriptors 28

29 Edge-based Segmentations Based on detection of discontinuities, and segment the image along the discontinuities 3 basic types of gray-level discontinuities: points, lines, edges Edge detection is the most common approach for detecting meaningful discontinuities Image segmentation and descriptors 29

30 Edge Linking and Boundary Detection From intensity discontinuities to more general segmentation For example, from edge pixels to line segments Local processing Analysis of small neighborhood Strength and direction of the gradient of edge pixels Global processing Analysis of the whole image Global relationships between pixels Hough Transform Image segmentation and descriptors 30

31 Visual descriptors 31

32 Graph-Based Segmentation Analysis images by graph theory, image is mapped onto a graph Each pixel (sub region) in the image correspond to a vertex Typical connection between the nodes is 4 (or 8) connections. Vertices and edges have weights Pixel gray level value is usually assigned to vertex weight Weights associated with each link are based on some property of the connected pixels, e.g. intensity differences. Pixel Vertex Link Four-way connected graph Image segmentation and descriptors 32

33 Graph-Based Segmentation Insertion of links between similar intensities creates subtrees for these objects Every sub tree represents a region of the image Region 1 Methods: Spanning tree Graph cuts Markov random fields Region 2 Image segmentation and descriptors 33

34 Recursive Shortest Spanning Tree (RSST) Methods which does not depend on global information will not achieve good segmentation (Morris et al. 1986) => Dynamic construction of image tree Algorithm: Image is mapped onto the graph Four way connected graph is used Absolute values of the gray level differences between vertices are assigned to link weights Link weight = 70 Image segmentation and descriptors 34

35 Recursive Shortest Spanning Tree (RSST) Nodes, whose link yields the smallest value, are merged Chosen link is saved as part of the tree Remove duplicated links during merging Update the changed weights of links that are touching to newly formed region Change link weights Before merging Smallest weighted link After merging Remove duplicated link Image segmentation and descriptors 35

36 Recursive Shortest Spanning Tree Beginning from the original image, where each pixel represents a different region Most similar neighboring regions are merged iteratively until a user-defined region number is left Image segmentation and descriptors 36

37 Graph-based Clustering Represents tokens (nodes) with a weighted graph Affinity matrix, A (similar nodes have higher entries) Cut up the graph to get subgraphs with strong links (Graph Cut) Affinity matrix Image segmentation and descriptors 37

38 Measuring Affinity Intensity Distance Color Image segmentation and descriptors 38

39 Scale Affects Affinity Image segmentation and descriptors 39

40 Solution via Eigenvectors Idea: We want a vector w giving the association between each element and a cluster We want elements within this cluster to, on the whole, have strong affinity with one another Maximize, with extra constraint Optimize by method of Lagrange multiplier : Solution is an eigenvalue problem Choose the eigenvector of A with the largest eigenvalue Image segmentation and descriptors 40

41 Solution via Eigenvectors points Affinity matrix Image segmentation and descriptors 41

42 Normalized Cuts Previous criterion: maximize within cluster similarity Normalized graph cuts: maximize within cluster similarity while maximizing cross cluster difference Given graph G=(V, E), sets A and B are a disjoint partition of the vertices set V Cut(A,B) is a measure of similarity between the two sets w corresponds to the weight of the link Image segmentation and descriptors 42

43 Normalized Cuts Cut is a measure of association Minimize cut = maximize disassociation A B Normalized cuts : to avoid bias for partitioning out small sets of points, normalize the cut with the total edge connections to all the nodes in the graph, e.g. A B Image segmentation and descriptors 43

44 Normalized Cuts Segmentation criterion: Finding the minimal normalized cut is NPcomplete, so computationally infeasible An approximation can be obtained by solving a generalized eigenvalue problem Approximation is very good in practice For details see: Jianbo Shi and Jitendra Malik, Normalized Cuts and Image Segmentation, PAMI, vol. 22, No. 8, 2000 Image segmentation and descriptors 44

45 Criterion becomes Normalized Cuts is the generalized Rayleigh quotient, which can be optimized by solving the generalized eigenvalue problem for the second smallest generalized eigenvector Image segmentation and descriptors 45

46 Segmentation Results Image segmentation and descriptors 46

47 Segmentation Results Image segmentation and descriptors 47

48 Results on color segmentation Image segmentation and descriptors 48

49 Superpixel Segmentation 49

50 From pixels to superpixels Superpixels are homogenous image regions that align well with object boundaries Reduce image complexity From 307,200 pixels (VGA) down to 1,000 superpixels Belong to class of oversegmentation algorithms Make more segments than there are objects Segments can then be used as building blocks 50

51 Example algorithms I Normalized cuts (Mori 2004, Mori et al. 2005) Tubopixels (Levinshtein et al. 2009) Placing superpixel seeds in the image Growing the seeds based on geometric flows 51

52 Example algorithms II Superpixel Lattices (Moore et al. 2008) Graph cuts with topological constraints SLIC (Achanta et al. 2010) Clustering approach based on k-means 52

53 SLIC Superpixels I Introduced by Achanta et al. (2010) Iterative algorithm based on k-means clustering with linear time complexity Pixels are assigned to superpixels based on a combination of the color distance in LABspace and Euclidean distance in image space l,a,b: LAB values, x,y: position, S: size of superpixels, m: weight of distance term 53

54 SLIC Superpixels II Initialization: Superpixel centers are distributed across the image Clusters (i.e. the superpixels) are initialized with the color of the pixel in its center (LAB-space) Iteration: For each superpixel, get all pixels in an area twice the size of the superpixel around its center and compute the distance of each pixel to the superpixel Assign each pixel to the best matching superpixel Recompute the superpixel characteristics (e.g. color mean, center position) Repeat until convergence 54

55 Example applications Mitochondria segmentation on EM-images (Achanta et al. 2010) Mincut-algorithm on superpixels Trail detection for robot navigation (Rasmussen and Scott 2008) Group superpixels based on color similarity Match triangles to boundaries of the groups to detect the trails 55

56 Visual descriptors 56

57 Example System: BlobWorld The problem: query images (e.g. from the WEB) using image information The solution: segment images into roughly uniform regions and search based on feature vectors The features: Color Texture Location (i.e. spatial compactness) The segmentation algorithm: Expectation Maximization (EM), similar in conception to K- means clustering algorithm Image segmentation and descriptors 57

58 Example segmentations Image segmentation and descriptors 58

59 Querying User selects a weighting of color vs. texture giving a diagonal weight matrix Given a blob with feature vector, compared to another vector using Mahalanobis distance: Image segmentation and descriptors 59

60 Image segmentation and descriptors 60

61 Image segmentation and descriptors 61

62 Image segmentation and descriptors 62

63 Image segmentation and descriptors 63

64 Visual descriptors 64

65 References Jianbo shi and Jitendra Malik, Normalized Cuts and Image Segmentation, PAMI, vol. 22, No. 8, 2000 C. Carson, S. Belongie, H. Greenspan, J. Marlik, Blobworld: image segmentation using expectationmaximization and its application to image querying, PAMI, vol. 24, issue. 8, pp Image segmentation and descriptors 65

66 References SLIC: R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and, S. Süsstrunk. SLIC Superpixels. EPFL Technical Report , June, Normalized cuts implementation: G. Mori. Guiding Model Search Using Segmentation. In Proc. International Conference on Computer Vision, pages , G. Mori, X. Ren, A. A. Efros, and J. Malik. Recovering Human Body Configurations: Combining Segmentation and Recognition. In Proc. Computer Vision and Pattern Recognition, pages , Superpixel Lattices: A. P. Moore, S. J. D. Prince, J. Warrell, U. Mohammed, and G. Jones. Superpixel Lattices. In Proc. Computer Vision and Pattern Recognition, pages 1-8, TurboPixels: A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi. TurboPixels: Fast Superpixels Using Geometric Flows. Transactions on Pattern Analysis and Machine Intelligence, 31(12): , C. Rasmussen and D. Scott. Shape-Guided Superpixel Grouping for Trail Detection and Tracking. In Proc. International Conference on Intelligent Robots and Systems, pages ,

EE 701 ROBOT VISION. Segmentation

EE 701 ROBOT VISION. Segmentation EE 701 ROBOT VISION Regions and Image Segmentation Histogram-based Segmentation Automatic Thresholding K-means Clustering Spatial Coherence Merging and Splitting Graph Theoretic Segmentation Region Growing

More information

CS 534: Computer Vision Segmentation and Perceptual Grouping

CS 534: Computer Vision Segmentation and Perceptual Grouping CS 534: Computer Vision Segmentation and Perceptual Grouping Ahmed Elgammal Dept of Computer Science CS 534 Segmentation - 1 Outlines Mid-level vision What is segmentation Perceptual Grouping Segmentation

More information

CS 534: Computer Vision Segmentation II Graph Cuts and Image Segmentation

CS 534: Computer Vision Segmentation II Graph Cuts and Image Segmentation CS 534: Computer Vision Segmentation II Graph Cuts and Image Segmentation Spring 2005 Ahmed Elgammal Dept of Computer Science CS 534 Segmentation II - 1 Outlines What is Graph cuts Graph-based clustering

More information

Superpixel Segmentation using Depth

Superpixel Segmentation using Depth Superpixel Segmentation using Depth Information Superpixel Segmentation using Depth Information David Stutz June 25th, 2014 David Stutz June 25th, 2014 01 Introduction - Table of Contents 1 Introduction

More information

human vision: grouping k-means clustering graph-theoretic clustering Hough transform line fitting RANSAC

human vision: grouping k-means clustering graph-theoretic clustering Hough transform line fitting RANSAC COS 429: COMPUTER VISON Segmentation human vision: grouping k-means clustering graph-theoretic clustering Hough transform line fitting RANSAC Reading: Chapters 14, 15 Some of the slides are credited to:

More information

6.801/866. Segmentation and Line Fitting. T. Darrell

6.801/866. Segmentation and Line Fitting. T. Darrell 6.801/866 Segmentation and Line Fitting T. Darrell Segmentation and Line Fitting Gestalt grouping Background subtraction K-Means Graph cuts Hough transform Iterative fitting (Next time: Probabilistic segmentation)

More information

Targil 12 : Image Segmentation. Image segmentation. Why do we need it? Image segmentation

Targil 12 : Image Segmentation. Image segmentation. Why do we need it? Image segmentation Targil : Image Segmentation Image segmentation Many slides from Steve Seitz Segment region of the image which: elongs to a single object. Looks uniform (gray levels, color ) Have the same attributes (texture

More information

Image Segmentation. Selim Aksoy. Bilkent University

Image Segmentation. Selim Aksoy. Bilkent University Image Segmentation Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Examples of grouping in vision [http://poseidon.csd.auth.gr/lab_research/latest/imgs/s peakdepvidindex_img2.jpg]

More information

Image Segmentation. Selim Aksoy. Bilkent University

Image Segmentation. Selim Aksoy. Bilkent University Image Segmentation Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Examples of grouping in vision [http://poseidon.csd.auth.gr/lab_research/latest/imgs/s peakdepvidindex_img2.jpg]

More information

Image Segmentation. Schedule. Jesus J Caban 11/2/10. Monday: Today: Image Segmentation Topic : Matting ( P. Bindu ) Assignment #3 distributed

Image Segmentation. Schedule. Jesus J Caban 11/2/10. Monday: Today: Image Segmentation Topic : Matting ( P. Bindu ) Assignment #3 distributed Image Segmentation Jesus J Caban Today: Schedule Image Segmentation Topic : Matting ( P. Bindu ) Assignment #3 distributed Monday: Revised proposal due Topic: Image Warping ( K. Martinez ) Topic: Image

More information

Computer Vision 5 Segmentation by Clustering

Computer Vision 5 Segmentation by Clustering Computer Vision 5 Segmentation by Clustering MAP-I Doctoral Programme Miguel Tavares Coimbra Outline Introduction Applications Simple clustering K-means clustering Graph-theoretic clustering Acknowledgements:

More information

DEPTH-ADAPTIVE SUPERVOXELS FOR RGB-D VIDEO SEGMENTATION. Alexander Schick. Fraunhofer IOSB Karlsruhe

DEPTH-ADAPTIVE SUPERVOXELS FOR RGB-D VIDEO SEGMENTATION. Alexander Schick. Fraunhofer IOSB Karlsruhe DEPTH-ADAPTIVE SUPERVOXELS FOR RGB-D VIDEO SEGMENTATION David Weikersdorfer Neuroscientific System Theory Technische Universität München Alexander Schick Fraunhofer IOSB Karlsruhe Daniel Cremers Computer

More information

Segmentation (continued)

Segmentation (continued) Segmentation (continued) Lecture 05 Computer Vision Material Citations Dr George Stockman Professor Emeritus, Michigan State University Dr Mubarak Shah Professor, University of Central Florida The Robotics

More information

Region-based Segmentation

Region-based Segmentation Region-based Segmentation Image Segmentation Group similar components (such as, pixels in an image, image frames in a video) to obtain a compact representation. Applications: Finding tumors, veins, etc.

More information

Applications. Foreground / background segmentation Finding skin-colored regions. Finding the moving objects. Intelligent scissors

Applications. Foreground / background segmentation Finding skin-colored regions. Finding the moving objects. Intelligent scissors Segmentation I Goal Separate image into coherent regions Berkeley segmentation database: http://www.eecs.berkeley.edu/research/projects/cs/vision/grouping/segbench/ Slide by L. Lazebnik Applications Intelligent

More information

arxiv: v1 [cs.cv] 14 Sep 2015

arxiv: v1 [cs.cv] 14 Sep 2015 gslicr: SLIC superpixels at over 250Hz Carl Yuheng Ren carl@robots.ox.ac.uk University of Oxford Ian D Reid ian.reid@adelaide.edu.au University of Adelaide September 15, 2015 Victor Adrian Prisacariu victor@robots.ox.ac.uk

More information

Idea. Found boundaries between regions (edges) Didn t return the actual region

Idea. Found boundaries between regions (edges) Didn t return the actual region Region Segmentation Idea Edge detection Found boundaries between regions (edges) Didn t return the actual region Segmentation Partition image into regions find regions based on similar pixel intensities,

More information

Segmentation by Clustering Reading: Chapter 14 (skip 14.5)

Segmentation by Clustering Reading: Chapter 14 (skip 14.5) Segmentation by Clustering Reading: Chapter 14 (skip 14.5) Data reduction - obtain a compact representation for interesting image data in terms of a set of components Find components that belong together

More information

Segmentation by Clustering. Segmentation by Clustering Reading: Chapter 14 (skip 14.5) General ideas

Segmentation by Clustering. Segmentation by Clustering Reading: Chapter 14 (skip 14.5) General ideas Reading: Chapter 14 (skip 14.5) Data reduction - obtain a compact representation for interesting image data in terms of a set of components Find components that belong together (form clusters) Frame differencing

More information

Normalized cuts and image segmentation

Normalized cuts and image segmentation Normalized cuts and image segmentation Department of EE University of Washington Yeping Su Xiaodan Song Normalized Cuts and Image Segmentation, IEEE Trans. PAMI, August 2000 5/20/2003 1 Outline 1. Image

More information

Processing and Others. Xiaojun Qi -- REU Site Program in CVMA

Processing and Others. Xiaojun Qi -- REU Site Program in CVMA Advanced Digital Image Processing and Others Xiaojun Qi -- REU Site Program in CVMA (0 Summer) Segmentation Outline Strategies and Data Structures Overview of Algorithms Region Splitting Region Merging

More information

Basic Algorithms for Digital Image Analysis: a course

Basic Algorithms for Digital Image Analysis: a course Institute of Informatics Eötvös Loránd University Budapest, Hungary Basic Algorithms for Digital Image Analysis: a course Dmitrij Csetverikov with help of Attila Lerch, Judit Verestóy, Zoltán Megyesi,

More information

Topic 4 Image Segmentation

Topic 4 Image Segmentation Topic 4 Image Segmentation What is Segmentation? Why? Segmentation important contributing factor to the success of an automated image analysis process What is Image Analysis: Processing images to derive

More information

Lecture 7: Segmentation. Thursday, Sept 20

Lecture 7: Segmentation. Thursday, Sept 20 Lecture 7: Segmentation Thursday, Sept 20 Outline Why segmentation? Gestalt properties, fun illusions and/or revealing examples Clustering Hierarchical K-means Mean Shift Graph-theoretic Normalized cuts

More information

Segmentation by Clustering

Segmentation by Clustering KECE471 Computer Vision Segmentation by Clustering Chang-Su Kim Chapter 14, Computer Vision by Forsyth and Ponce Note: Dr. Forsyth s notes are partly used. Jae-Kyun Ahn in Korea University made the first

More information

Title: Adaptive Region Merging Segmentation of Airborne Imagery for Roof Condition Assessment. Abstract:

Title: Adaptive Region Merging Segmentation of Airborne Imagery for Roof Condition Assessment. Abstract: Title: Adaptive Region Merging Segmentation of Airborne Imagery for Roof Condition Assessment Abstract: In order to perform residential roof condition assessment with very-high-resolution airborne imagery,

More information

CS 534: Computer Vision Segmentation and Perceptual Grouping

CS 534: Computer Vision Segmentation and Perceptual Grouping CS 534: Computer Vision Segmentation and Perceptual Grouping Spring 2005 Ahmed Elgammal Dept of Computer Science CS 534 Segmentation - 1 Where are we? Image Formation Human vision Cameras Geometric Camera

More information

Outline. Advanced Digital Image Processing and Others. Importance of Segmentation (Cont.) Importance of Segmentation

Outline. Advanced Digital Image Processing and Others. Importance of Segmentation (Cont.) Importance of Segmentation Advanced Digital Image Processing and Others Xiaojun Qi -- REU Site Program in CVIP (7 Summer) Outline Segmentation Strategies and Data Structures Algorithms Overview K-Means Algorithm Hidden Markov Model

More information

Digital Image Analysis and Processing

Digital Image Analysis and Processing Digital Image Analysis and Processing CPE 0907544 Image Segmentation Part II Chapter 10 Sections : 10.3 10.4 Dr. Iyad Jafar Outline Introduction Thresholdingh Fundamentals Basic Global Thresholding Optimal

More information

Example 2: Straight Lines. Image Segmentation. Example 3: Lines and Circular Arcs. Example 1: Regions

Example 2: Straight Lines. Image Segmentation. Example 3: Lines and Circular Arcs. Example 1: Regions Image Segmentation Image segmentation is the operation of partitioning an image into a collection of connected sets of pixels. 1. into regions, which usually cover the image Example : Straight Lines. into

More information

Example 1: Regions. Image Segmentation. Example 3: Lines and Circular Arcs. Example 2: Straight Lines. Region Segmentation: Segmentation Criteria

Example 1: Regions. Image Segmentation. Example 3: Lines and Circular Arcs. Example 2: Straight Lines. Region Segmentation: Segmentation Criteria Image Segmentation Image segmentation is the operation of partitioning an image into a collection of connected sets of pixels. 1. into regions, which usually cover the image Example 1: Regions. into linear

More information

The goals of segmentation

The goals of segmentation Image segmentation The goals of segmentation Group together similar-looking pixels for efficiency of further processing Bottom-up process Unsupervised superpixels X. Ren and J. Malik. Learning a classification

More information

Image Segmentation. Shengnan Wang

Image Segmentation. Shengnan Wang Image Segmentation Shengnan Wang shengnan@cs.wisc.edu Contents I. Introduction to Segmentation II. Mean Shift Theory 1. What is Mean Shift? 2. Density Estimation Methods 3. Deriving the Mean Shift 4. Mean

More information

Introduction to Medical Imaging (5XSA0) Module 5

Introduction to Medical Imaging (5XSA0) Module 5 Introduction to Medical Imaging (5XSA0) Module 5 Segmentation Jungong Han, Dirk Farin, Sveta Zinger ( s.zinger@tue.nl ) 1 Outline Introduction Color Segmentation region-growing region-merging watershed

More information

Robotics Programming Laboratory

Robotics Programming Laboratory Chair of Software Engineering Robotics Programming Laboratory Bertrand Meyer Jiwon Shin Lecture 8: Robot Perception Perception http://pascallin.ecs.soton.ac.uk/challenges/voc/databases.html#caltech car

More information

Segmentation and Grouping April 19 th, 2018

Segmentation and Grouping April 19 th, 2018 Segmentation and Grouping April 19 th, 2018 Yong Jae Lee UC Davis Features and filters Transforming and describing images; textures, edges 2 Grouping and fitting [fig from Shi et al] Clustering, segmentation,

More information

REGION BASED SEGEMENTATION

REGION BASED SEGEMENTATION REGION BASED SEGEMENTATION The objective of Segmentation is to partition an image into regions. The region-based segmentation techniques find the regions directly. Extract those regions in the image whose

More information

Image Analysis Image Segmentation (Basic Methods)

Image Analysis Image Segmentation (Basic Methods) Image Analysis Image Segmentation (Basic Methods) Christophoros Nikou cnikou@cs.uoi.gr Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall, 2008. Computer Vision course

More information

Segmentation. Bottom Up Segmentation

Segmentation. Bottom Up Segmentation Segmentation Bottom up Segmentation Semantic Segmentation Bottom Up Segmentation 1 Segmentation as clustering Depending on what we choose as the feature space, we can group pixels in different ways. Grouping

More information

Color Image Segmentation

Color Image Segmentation Color Image Segmentation Yining Deng, B. S. Manjunath and Hyundoo Shin* Department of Electrical and Computer Engineering University of California, Santa Barbara, CA 93106-9560 *Samsung Electronics Inc.

More information

Segmentation and low-level grouping.

Segmentation and low-level grouping. Segmentation and low-level grouping. Bill Freeman, MIT 6.869 April 14, 2005 Readings: Mean shift paper and background segmentation paper. Mean shift IEEE PAMI paper by Comanici and Meer, http://www.caip.rutgers.edu/~comanici/papers/msrobustapproach.pdf

More information

CHAPTER 4. ANALYSIS of GRAPH THEORETICAL IMAGE SEGMENTATION METHODS

CHAPTER 4. ANALYSIS of GRAPH THEORETICAL IMAGE SEGMENTATION METHODS CHAPTER 4 ANALYSIS of GRAPH THEORETICAL IMAGE SEGMENTATION METHODS 4.1 Introduction In Graph based methods image is represented by undirected weighted graph where the nodes are pixels or pixel regions.

More information

Segmentation and Grouping

Segmentation and Grouping Segmentation and Grouping How and what do we see? Fundamental Problems ' Focus of attention, or grouping ' What subsets of pixels do we consider as possible objects? ' All connected subsets? ' Representation

More information

Object Extraction Using Image Segmentation and Adaptive Constraint Propagation

Object Extraction Using Image Segmentation and Adaptive Constraint Propagation Object Extraction Using Image Segmentation and Adaptive Constraint Propagation 1 Rajeshwary Patel, 2 Swarndeep Saket 1 Student, 2 Assistant Professor 1 2 Department of Computer Engineering, 1 2 L. J. Institutes

More information

2D image segmentation based on spatial coherence

2D image segmentation based on spatial coherence 2D image segmentation based on spatial coherence Václav Hlaváč Czech Technical University in Prague Center for Machine Perception (bridging groups of the) Czech Institute of Informatics, Robotics and Cybernetics

More information

Outline. Segmentation & Grouping. Examples of grouping in vision. Grouping in vision. Grouping in vision 2/9/2011. CS 376 Lecture 7 Segmentation 1

Outline. Segmentation & Grouping. Examples of grouping in vision. Grouping in vision. Grouping in vision 2/9/2011. CS 376 Lecture 7 Segmentation 1 Outline What are grouping problems in vision? Segmentation & Grouping Wed, Feb 9 Prof. UT-Austin Inspiration from human perception Gestalt properties Bottom-up segmentation via clustering Algorithms: Mode

More information

Image Processing and Image Analysis VU

Image Processing and Image Analysis VU Image Processing and Image Analysis 052617 VU Yll Haxhimusa yll.haxhimusa@medunwien.ac.at vda.univie.ac.at/teaching/ipa/17w/ Outline What are grouping problems in vision? Inspiration from human perception

More information

identified and grouped together.

identified and grouped together. Segmentation ti of Images SEGMENTATION If an image has been preprocessed appropriately to remove noise and artifacts, segmentation is often the key step in interpreting the image. Image segmentation is

More information

Image Segmentation continued Graph Based Methods

Image Segmentation continued Graph Based Methods Image Segmentation continued Graph Based Methods Previously Images as graphs Fully-connected graph node (vertex) for every pixel link between every pair of pixels, p,q affinity weight w pq for each link

More information

Image segmentation. Stefano Ferrari. Università degli Studi di Milano Methods for Image Processing. academic year

Image segmentation. Stefano Ferrari. Università degli Studi di Milano Methods for Image Processing. academic year Image segmentation Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Methods for Image Processing academic year 2017 2018 Segmentation by thresholding Thresholding is the simplest

More information

Image Analysis - Lecture 5

Image Analysis - Lecture 5 Texture Segmentation Clustering Review Image Analysis - Lecture 5 Texture and Segmentation Magnus Oskarsson Lecture 5 Texture Segmentation Clustering Review Contents Texture Textons Filter Banks Gabor

More information

Image Segmentation. 1Jyoti Hazrati, 2Kavita Rawat, 3Khush Batra. Dronacharya College Of Engineering, Farrukhnagar, Haryana, India

Image Segmentation. 1Jyoti Hazrati, 2Kavita Rawat, 3Khush Batra. Dronacharya College Of Engineering, Farrukhnagar, Haryana, India Image Segmentation 1Jyoti Hazrati, 2Kavita Rawat, 3Khush Batra Dronacharya College Of Engineering, Farrukhnagar, Haryana, India Dronacharya College Of Engineering, Farrukhnagar, Haryana, India Global Institute

More information

Object Segmentation. Jacob D. Furst DePaul CTI

Object Segmentation. Jacob D. Furst DePaul CTI Object Segmentation Jacob D. Furst DePaul CTI Image Segmentation Segmentation divides an image into regions or objects (segments) The degree of segmentation is highly application dependent Segmentation

More information

From Pixels to Blobs

From Pixels to Blobs From Pixels to Blobs 15-463: Rendering and Image Processing Alexei Efros Today Blobs Need for blobs Extracting blobs Image Segmentation Working with binary images Mathematical Morphology Blob properties

More information

Segmentation of Images

Segmentation of Images Segmentation of Images SEGMENTATION If an image has been preprocessed appropriately to remove noise and artifacts, segmentation is often the key step in interpreting the image. Image segmentation is a

More information

Ulrik Söderström 16 Feb Image Processing. Segmentation

Ulrik Söderström 16 Feb Image Processing. Segmentation Ulrik Söderström ulrik.soderstrom@tfe.umu.se 16 Feb 2011 Image Processing Segmentation What is Image Segmentation? To be able to extract information from an image it is common to subdivide it into background

More information

This is an author-deposited version published in : Eprints ID : 15223

This is an author-deposited version published in :   Eprints ID : 15223 Open Archive TOULOUSE Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited

More information

Accelerated gslic for Superpixel Generation used in Object Segmentation

Accelerated gslic for Superpixel Generation used in Object Segmentation Accelerated gslic for Superpixel Generation used in Object Segmentation Robert Birkus Supervised by: Ing. Wanda Benesova, PhD. Institute of Applied Informatics Faculty of Informatics and Information Technologies

More information

Segmentation and Grouping

Segmentation and Grouping CS 1699: Intro to Computer Vision Segmentation and Grouping Prof. Adriana Kovashka University of Pittsburgh September 24, 2015 Goals: Grouping in vision Gather features that belong together Obtain an intermediate

More information

Texture. Texture is a description of the spatial arrangement of color or intensities in an image or a selected region of an image.

Texture. Texture is a description of the spatial arrangement of color or intensities in an image or a selected region of an image. Texture Texture is a description of the spatial arrangement of color or intensities in an image or a selected region of an image. Structural approach: a set of texels in some regular or repeated pattern

More information

Operators-Based on Second Derivative double derivative Laplacian operator Laplacian Operator Laplacian Of Gaussian (LOG) Operator LOG

Operators-Based on Second Derivative double derivative Laplacian operator Laplacian Operator Laplacian Of Gaussian (LOG) Operator LOG Operators-Based on Second Derivative The principle of edge detection based on double derivative is to detect only those points as edge points which possess local maxima in the gradient values. Laplacian

More information

Contents.

Contents. Contents Brief introduction to Image segmentation Types of Image segmentation Region growing and Shrinking (split /merge ) method Applications of Image segmentation Results 1 http://astro.temple.edu/~siddu

More information

Image Segmentation continued Graph Based Methods. Some slides: courtesy of O. Capms, Penn State, J.Ponce and D. Fortsyth, Computer Vision Book

Image Segmentation continued Graph Based Methods. Some slides: courtesy of O. Capms, Penn State, J.Ponce and D. Fortsyth, Computer Vision Book Image Segmentation continued Graph Based Methods Some slides: courtesy of O. Capms, Penn State, J.Ponce and D. Fortsyth, Computer Vision Book Previously Binary segmentation Segmentation by thresholding

More information

Superpixel Segmentation using Linear Spectral Clustering

Superpixel Segmentation using Linear Spectral Clustering Superpixel Segmentation using Linear Spectral Clustering Zhengqin Li Jiansheng Chen Department of Electronic Engineering, Tsinghua University, Beijing, China li-zq12@mails.tsinghua.edu.cn jschenthu@mail.tsinghua.edu.cn

More information

Using the Kolmogorov-Smirnov Test for Image Segmentation

Using the Kolmogorov-Smirnov Test for Image Segmentation Using the Kolmogorov-Smirnov Test for Image Segmentation Yong Jae Lee CS395T Computational Statistics Final Project Report May 6th, 2009 I. INTRODUCTION Image segmentation is a fundamental task in computer

More information

Segmentation Computer Vision Spring 2018, Lecture 27

Segmentation Computer Vision Spring 2018, Lecture 27 Segmentation http://www.cs.cmu.edu/~16385/ 16-385 Computer Vision Spring 218, Lecture 27 Course announcements Homework 7 is due on Sunday 6 th. - Any questions about homework 7? - How many of you have

More information

Lecture 11: E-M and MeanShift. CAP 5415 Fall 2007

Lecture 11: E-M and MeanShift. CAP 5415 Fall 2007 Lecture 11: E-M and MeanShift CAP 5415 Fall 2007 Review on Segmentation by Clustering Each Pixel Data Vector Example (From Comanciu and Meer) Review of k-means Let's find three clusters in this data These

More information

Computer Vision. Image Segmentation. 10. Segmentation. Computer Engineering, Sejong University. Dongil Han

Computer Vision. Image Segmentation. 10. Segmentation. Computer Engineering, Sejong University. Dongil Han Computer Vision 10. Segmentation Computer Engineering, Sejong University Dongil Han Image Segmentation Image segmentation Subdivides an image into its constituent regions or objects - After an image has

More information

A Graph Theoretic Approach to Image Database Retrieval

A Graph Theoretic Approach to Image Database Retrieval A Graph Theoretic Approach to Image Database Retrieval Selim Aksoy and Robert M. Haralick Intelligent Systems Laboratory Department of Electrical Engineering University of Washington, Seattle, WA 98195-2500

More information

CS 664 Segmentation. Daniel Huttenlocher

CS 664 Segmentation. Daniel Huttenlocher CS 664 Segmentation Daniel Huttenlocher Grouping Perceptual Organization Structural relationships between tokens Parallelism, symmetry, alignment Similarity of token properties Often strong psychophysical

More information

Clustering CS 550: Machine Learning

Clustering CS 550: Machine Learning Clustering CS 550: Machine Learning This slide set mainly uses the slides given in the following links: http://www-users.cs.umn.edu/~kumar/dmbook/ch8.pdf http://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap8_basic_cluster_analysis.pdf

More information

BSB663 Image Processing Pinar Duygulu. Slides are adapted from Selim Aksoy

BSB663 Image Processing Pinar Duygulu. Slides are adapted from Selim Aksoy BSB663 Image Processing Pinar Duygulu Slides are adapted from Selim Aksoy Image matching Image matching is a fundamental aspect of many problems in computer vision. Object or scene recognition Solving

More information

CS 2770: Computer Vision. Edges and Segments. Prof. Adriana Kovashka University of Pittsburgh February 21, 2017

CS 2770: Computer Vision. Edges and Segments. Prof. Adriana Kovashka University of Pittsburgh February 21, 2017 CS 2770: Computer Vision Edges and Segments Prof. Adriana Kovashka University of Pittsburgh February 21, 2017 Edges vs Segments Figure adapted from J. Hays Edges vs Segments Edges More low-level Don t

More information

6. Object Identification L AK S H M O U. E D U

6. Object Identification L AK S H M O U. E D U 6. Object Identification L AK S H M AN @ O U. E D U Objects Information extracted from spatial grids often need to be associated with objects not just an individual pixel Group of pixels that form a real-world

More information

CS 231A CA Session: Problem Set 4 Review. Kevin Chen May 13, 2016

CS 231A CA Session: Problem Set 4 Review. Kevin Chen May 13, 2016 CS 231A CA Session: Problem Set 4 Review Kevin Chen May 13, 2016 PS4 Outline Problem 1: Viewpoint estimation Problem 2: Segmentation Meanshift segmentation Normalized cut Problem 1: Viewpoint Estimation

More information

Classification. Vladimir Curic. Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University

Classification. Vladimir Curic. Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University Classification Vladimir Curic Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University Outline An overview on classification Basics of classification How to choose appropriate

More information

Digital Image Processing. Prof. P.K. Biswas. Department of Electronics & Electrical Communication Engineering

Digital Image Processing. Prof. P.K. Biswas. Department of Electronics & Electrical Communication Engineering Digital Image Processing Prof. P.K. Biswas Department of Electronics & Electrical Communication Engineering Indian Institute of Technology, Kharagpur Image Segmentation - III Lecture - 31 Hello, welcome

More information

Grouping and Segmentation

Grouping and Segmentation 03/17/15 Grouping and Segmentation Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem Today s class Segmentation and grouping Gestalt cues By clustering (mean-shift) By boundaries (watershed)

More information

International Journal of Advanced Research in Computer Science and Software Engineering

International Journal of Advanced Research in Computer Science and Software Engineering Volume 2, Issue 9, September 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A New Method

More information

Image Segmentation. Segmentation is the process of partitioning an image into regions

Image Segmentation. Segmentation is the process of partitioning an image into regions Image Segmentation Segmentation is the process of partitioning an image into regions region: group of connected pixels with similar properties properties: gray levels, colors, textures, motion characteristics

More information

A Modified Approach for Image Segmentation in Information Bottleneck Method

A Modified Approach for Image Segmentation in Information Bottleneck Method A Modified Approach for Image Segmentation in Information Bottleneck Method S.Dhanalakshmi 1 and Dr.T.Ravichandran 2 Associate Professor, Department of Computer Science & Engineering, SNS College of Technology,Coimbatore-641

More information

CSSE463: Image Recognition Day 21

CSSE463: Image Recognition Day 21 CSSE463: Image Recognition Day 21 Sunset detector due. Foundations of Image Recognition completed This wee: K-means: a method of Image segmentation Questions? An image to segment Segmentation The process

More information

Segmentation & Clustering

Segmentation & Clustering EECS 442 Computer vision Segmentation & Clustering Segmentation in human vision K-mean clustering Mean-shift Graph-cut Reading: Chapters 14 [FP] Some slides of this lectures are courtesy of prof F. Li,

More information

Image Segmentation Techniques

Image Segmentation Techniques A Study On Image Segmentation Techniques Palwinder Singh 1, Amarbir Singh 2 1,2 Department of Computer Science, GNDU Amritsar Abstract Image segmentation is very important step of image analysis which

More information

Part 3: Image Processing

Part 3: Image Processing Part 3: Image Processing Image Filtering and Segmentation Georgy Gimel farb COMPSCI 373 Computer Graphics and Image Processing 1 / 60 1 Image filtering 2 Median filtering 3 Mean filtering 4 Image segmentation

More information

Clustering. CS294 Practical Machine Learning Junming Yin 10/09/06

Clustering. CS294 Practical Machine Learning Junming Yin 10/09/06 Clustering CS294 Practical Machine Learning Junming Yin 10/09/06 Outline Introduction Unsupervised learning What is clustering? Application Dissimilarity (similarity) of objects Clustering algorithm K-means,

More information

Image Analysis Lecture Segmentation. Idar Dyrdal

Image Analysis Lecture Segmentation. Idar Dyrdal Image Analysis Lecture 9.1 - Segmentation Idar Dyrdal Segmentation Image segmentation is the process of partitioning a digital image into multiple parts The goal is to divide the image into meaningful

More information

Machine Learning : Clustering, Self-Organizing Maps

Machine Learning : Clustering, Self-Organizing Maps Machine Learning Clustering, Self-Organizing Maps 12/12/2013 Machine Learning : Clustering, Self-Organizing Maps Clustering The task: partition a set of objects into meaningful subsets (clusters). The

More information

Segmentation algorithm for monochrome images generally are based on one of two basic properties of gray level values: discontinuity and similarity.

Segmentation algorithm for monochrome images generally are based on one of two basic properties of gray level values: discontinuity and similarity. Chapter - 3 : IMAGE SEGMENTATION Segmentation subdivides an image into its constituent s parts or objects. The level to which this subdivision is carried depends on the problem being solved. That means

More information

Saliency Detection in Aerial Imagery

Saliency Detection in Aerial Imagery Saliency Detection in Aerial Imagery using Multi-scale SLIC Segmentation Samir Sahli 1, Daniel A. Lavigne 2 and Yunlong Sheng 1 1- COPL, Image Science group, Laval University, Quebec, Canada 2- Defence

More information

Image Segmentation. Srikumar Ramalingam School of Computing University of Utah. Slides borrowed from Ross Whitaker

Image Segmentation. Srikumar Ramalingam School of Computing University of Utah. Slides borrowed from Ross Whitaker Image Segmentation Srikumar Ramalingam School of Computing University of Utah Slides borrowed from Ross Whitaker Segmentation Semantic Segmentation Indoor layout estimation What is Segmentation? Partitioning

More information

Image retrieval based on region shape similarity

Image retrieval based on region shape similarity Image retrieval based on region shape similarity Cheng Chang Liu Wenyin Hongjiang Zhang Microsoft Research China, 49 Zhichun Road, Beijing 8, China {wyliu, hjzhang}@microsoft.com ABSTRACT This paper presents

More information

EDGE BASED REGION GROWING

EDGE BASED REGION GROWING EDGE BASED REGION GROWING Rupinder Singh, Jarnail Singh Preetkamal Sharma, Sudhir Sharma Abstract Image segmentation is a decomposition of scene into its components. It is a key step in image analysis.

More information

Prof. Fanny Ficuciello Robotics for Bioengineering Visual Servoing

Prof. Fanny Ficuciello Robotics for Bioengineering Visual Servoing Visual servoing vision allows a robotic system to obtain geometrical and qualitative information on the surrounding environment high level control motion planning (look-and-move visual grasping) low level

More information

Dr. Ulas Bagci

Dr. Ulas Bagci CAP5415-Computer Vision Lecture 11-Image Segmentation (BASICS): Thresholding, Region Growing, Clustering Dr. Ulas Bagci bagci@ucf.edu 1 Image Segmentation Aim: to partition an image into a collection of

More information

Segmentation and Grouping April 21 st, 2015

Segmentation and Grouping April 21 st, 2015 Segmentation and Grouping April 21 st, 2015 Yong Jae Lee UC Davis Announcements PS0 grades are up on SmartSite Please put name on answer sheet 2 Features and filters Transforming and describing images;

More information

ECG782: Multidimensional Digital Signal Processing

ECG782: Multidimensional Digital Signal Processing Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spring 2014 TTh 14:30-15:45 CBC C313 Lecture 10 Segmentation 14/02/27 http://www.ee.unlv.edu/~b1morris/ecg782/

More information

Colorado School of Mines. Computer Vision. Professor William Hoff Dept of Electrical Engineering &Computer Science.

Colorado School of Mines. Computer Vision. Professor William Hoff Dept of Electrical Engineering &Computer Science. Professor William Hoff Dept of Electrical Engineering &Computer Science http://inside.mines.edu/~whoff/ 1 Image Segmentation Some material for these slides comes from https://www.csd.uwo.ca/courses/cs4487a/

More information

Keywords Image Segmentation, Pixels, Superpixels, Superpixel Segmentation Methods

Keywords Image Segmentation, Pixels, Superpixels, Superpixel Segmentation Methods Volume 4, Issue 9, September 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Superpixel

More information

Graph based Image Segmentation using improved SLIC Superpixel algorithm

Graph based Image Segmentation using improved SLIC Superpixel algorithm Graph based Image Segmentation using improved SLIC Superpixel algorithm Prasanna Regmi 1, B.J.M. Ravi Kumar 2 1 Computer Science and Systems Engineering, Andhra University, AP, India 2 Computer Science

More information