Boundary descriptors. Representation REPRESENTATION & DESCRIPTION. Descriptors. Moore boundary tracking
|
|
- Pearl Richards
- 5 years ago
- Views:
Transcription
1 Representation REPRESENTATION & DESCRIPTION After image segmentation the resulting collection of regions is usually represented and described in a form suitable for higher level processing. Most important representations are based on shape or texture. Desirable property: descriptors should be insensitive to changes in size, translation or rotation. Actual measurement of features in digital images makes use of many of the techniques discussed earlier, such as linear or morphological image operators. 1 Descriptors Moore boundary tracking Boundary descriptors Put boundary points of a binary region R (or its boundary) in a clockwise-sorted order. Needed for computation of boundary descriptors. 2 3
2 Chain codes Chain codes Freeman chain codes: strings of integers representing a boundary by a connected sequence of straight-line segments of specified length and direction. The direction of each line segment is coded using a numbering scheme adapted to the connectivity. (a) 8-directional chain code. (b) Digital boundary with resampling grid. (c) Resampled boundary. (d) 8-directional chain-coded boundary. The accuracy of the straight-line representation depends on the spacing of the sampling grid. 4 5 Chain codes: first difference Polygon representation First difference of a chain code: count number of direction changes (e.g., counterclockwise) that separate two adjacent code elements. chain code: (start lower left) first difference: (circular sequence) A digital boundary can also be approximated by a polygon, possibly with minimum length (MPP: minimum-perimeter polygon). Such rubber band approximations directly within the grey value image are known as active contour models, or snakes. 6 7
3 Minimum-perimeter polygon (MPP) Signatures (a) Light grey: region containing the boundary curve. (b) Convex (white) and concave (black) corner vertices. (c) Concave vertices moved to diagonal mirror locations. The MPP is indicated. 1-D representation of a boundary Example: distance r(θ) of centroid to boundary as a function of angle θ. 8 Often the boundary is first smoothed 9 Signature Characteristic Profile For each angle, compute difference between radial distance from ellipse center to ellipse and to contour, respectively. (Ciobanu and Du Buf, 2002.) 10 11
4 Convex and non-convex objects Boundary segments An object (binary image) is convex if for every pair of points p and q within the object, every point on the straight line segment that joins p and q is also within the object. Otherwise, we call it non-convex. p p To reduce the complexity of the boundary, decompose it into segments q q If the boundary encloses a region S, compute the convex hull H of S, and the convex deficiency D = H \ S. convex object non-convex object Mark points on the boundary at which a transition in and out of the convex deficiency occurs Boundary descriptors Curvature Length: no. of pixels along contour, length of MPP. Diameter: Diam(B) = max [D(p i, p j )] with p i, p j points on the i,j boundary B and D a distance measure. Major axis: line segment connecting the extreme points comprising the diameter Minor axis: line segment perpendicular to major axis such that rectangle defined by major and minor axis tightly encloses the boundary Eccentricity: ratio of lengths of major and minor axes Curvature Position along contour Curvature Position along contour Position along contour dashed: Gaussian smoothing, continuous: Adaptive Gaussian smoothing Curvature 14 15
5 Shape numbers Fourier descriptors Given a chain-coded boundary, its shape number is that particular cyclic permutation of the first difference which is lexicographically smallest among all the cyclic permutations Order n of shape number: no. of digits in its representation Represent boundary as a sequence s(k) = x(k) + i y(k), k = 0, 1,... K 1, where (x(k), y(k)) are coordinates of points on the contour. DFT of the vector (s(0), s(1),..., s(k 1)) yields K complex coefficients: the Fourier descriptors. Approximation: only use P < K of the Fourier coefficients and perform an inverse DFT Fourier descriptors Statistical moments Represent boundary as a function g(r). Let p(v i ), i = 1, 2,..., A 1 be the amplitude histogram of g(r). Mean: m = A 1 i=0 v i p(v i ) (a) Input (2868 points). (b)-(h) Approximations using 1434, 286, 144, 72, 36, 18, 8 Fourier coefficients. n th moment: µ n = A 1 i=0 (v i m) n p(v i ) n = 2, 3,
6 Descriptors Simple regional descriptors Mean, median, maximum, minimum grey level Regional descriptors Area A, perimeter P Compactness: Circularity ratio: For a circle, R c = 1 P 2 A R c = 4πA P 2 R c is invariant under translation, rotation, and scaling Topological descriptors Genus via erosions V H B Invariant to a large class of local deformations. Examples: number of connected components; number of holes Genus or Euler number: E = C H (number of connected components minus the number of holes). Computable by erosions or hit-or-miss transforms. g 4 (I): binary image I with 4-connected 1-pixels and 8- connected 0-pixels: g 4 (I) = # I # I V # I H + # I B Similar formulas for g 8 (I), also via hit-or-miss transforms. # I=no. of pixels of I, etc
7 Texture Texture descriptions Refers to the spatial distribution of discrete grey value variations, described in terms of: uniformity coarseness regularity directionality Three main approaches: 1. statistical: moments, co-occurrence matrix 2. structural, viewing a texture as an arrangement of texture primitives 3. spectral, using the Fourier transform to detect global periodicities Straw Raffia Statistical texture description: co-occurrence matrix Co-occurrence matrix Let Q be a spatial predicate defined on pixel pairs (p, q), such as: Q(p, q) q is a right neighbour of p. For an image with L levels and a binary spatial predicate Q, the L L co-occurrence matrix G is defined by: g(i, j) ={no. of pixel pairs with grey levels (z i, z j ) satisfying predicate Q}, 1 i, j L Predicate: Q(p, q) q is a right neighbour of p
8 Co-occurrence matrix Co-occurrence matrix: example Given a co-occurrence matrix G w.r.t. a spatial predicate Q n denotes the total number of pixels pairs satisfying Q (sum of the elements of G) texture 1 texture 2 texture 3 G1 G2 G3 The quantity pij = gij /n is an estimate of the probability that a pixel pair satisfying Q has values (zi, zj ). Properties of the distribution pij can be used to characterize the spatial properties represented by G 28 Structural texture description Textures generated from texture primitives (Brodatz texture collection, 29 Spectral texture description 30 Fourier spectra of random and ordered textures. 31
9 Spatial moment invariants Spatial moment invariants Central moments of an M N image f(x, y): µ pq = M 1 x=0 N 1 (x x) p (y y) q f(x, y), p, q = 1, 2,... y=0 with x = m 10 /m 00, y = m 01 /m 00, m pq = M 1 N 1 x=0 y=0 xp y q f(x, y). Normalized central moments: η pq = µ pq /µ γ 00 where γ = p+q Invariant moments: combinations φ 1,..., φ 7 of η pq which are invariant to translation, rotation and scale-change. Values of each invariant moment φ k are the same for all pictures
Lecture 18 Representation and description I. 2. Boundary descriptors
Lecture 18 Representation and description I 1. Boundary representation 2. Boundary descriptors What is representation What is representation After segmentation, we obtain binary image with interested regions
More informationLecture 8 Object Descriptors
Lecture 8 Object Descriptors Azadeh Fakhrzadeh Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University 2 Reading instructions Chapter 11.1 11.4 in G-W Azadeh Fakhrzadeh
More informationCoE4TN4 Image Processing
CoE4TN4 Image Processing Chapter 11 Image Representation & Description Image Representation & Description After an image is segmented into regions, the regions are represented and described in a form suitable
More information9 length of contour = no. of horizontal and vertical components + ( 2 no. of diagonal components) diameter of boundary B
8. Boundary Descriptor 8.. Some Simple Descriptors length of contour : simplest descriptor - chain-coded curve 9 length of contour no. of horiontal and vertical components ( no. of diagonal components
More informationChapter 11 Representation & Description
Chapter 11 Representation & Description The results of segmentation is a set of regions. Regions have then to be represented and described. Two main ways of representing a region: - external characteristics
More informationDigital Image Processing
Digital Image Processing Part 9: Representation and Description AASS Learning Systems Lab, Dep. Teknik Room T1209 (Fr, 11-12 o'clock) achim.lilienthal@oru.se Course Book Chapter 11 2011-05-17 Contents
More informationEECS490: Digital Image Processing. Lecture #23
Lecture #23 Motion segmentation & motion tracking Boundary tracking Chain codes Minimum perimeter polygons Signatures Motion Segmentation P k Accumulative Difference Image Positive ADI Negative ADI (ADI)
More informationMachine vision. Summary # 6: Shape descriptors
Machine vision Summary # : Shape descriptors SHAPE DESCRIPTORS Objects in an image are a collection of pixels. In order to describe an object or distinguish between objects, we need to understand the properties
More informationUlrik Söderström 21 Feb Representation and description
Ulrik Söderström ulrik.soderstrom@tfe.umu.se 2 Feb 207 Representation and description Representation and description Representation involves making object definitions more suitable for computer interpretations
More informationDigital Image Processing Chapter 11: Image Description and Representation
Digital Image Processing Chapter 11: Image Description and Representation Image Representation and Description? Objective: To represent and describe information embedded in an image in other forms that
More informationChapter 11 Representation & Description
Chain Codes Chain codes are used to represent a boundary by a connected sequence of straight-line segments of specified length and direction. The direction of each segment is coded by using a numbering
More informationTopic 6 Representation and Description
Topic 6 Representation and Description Background Segmentation divides the image into regions Each region should be represented and described in a form suitable for further processing/decision-making Representation
More informationImage representation. 1. Introduction
Image representation Introduction Representation schemes Chain codes Polygonal approximations The skeleton of a region Boundary descriptors Some simple descriptors Shape numbers Fourier descriptors Moments
More informationLecture 10: Image Descriptors and Representation
I2200: Digital Image processing Lecture 10: Image Descriptors and Representation Prof. YingLi Tian Nov. 15, 2017 Department of Electrical Engineering The City College of New York The City University of
More information- Low-level image processing Image enhancement, restoration, transformation
() Representation and Description - Low-level image processing enhancement, restoration, transformation Enhancement Enhanced Restoration/ Transformation Restored/ Transformed - Mid-level image processing
More informationPractical Image and Video Processing Using MATLAB
Practical Image and Video Processing Using MATLAB Chapter 18 Feature extraction and representation What will we learn? What is feature extraction and why is it a critical step in most computer vision and
More informationDigital Image Processing Fundamentals
Ioannis Pitas Digital Image Processing Fundamentals Chapter 7 Shape Description Answers to the Chapter Questions Thessaloniki 1998 Chapter 7: Shape description 7.1 Introduction 1. Why is invariance to
More informationImage and Multidimensional Signal Processing
Image and Multidimensional Signal Processing Professor William Hoff Dept of Electrical Engineering &Computer Science http://inside.mines.edu/~whoff/ Representation and Description 2 Representation and
More informationBasic 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 informationEE 584 MACHINE VISION
EE 584 MACHINE VISION Binary Images Analysis Geometrical & Topological Properties Connectedness Binary Algorithms Morphology Binary Images Binary (two-valued; black/white) images gives better efficiency
More informationECEN 447 Digital Image Processing
ECEN 447 Digital Image Processing Lecture 8: Segmentation and Description Ulisses Braga-Neto ECE Department Texas A&M University Image Segmentation and Description Image segmentation and description are
More informationExamination in Image Processing
Umeå University, TFE Ulrik Söderström 203-03-27 Examination in Image Processing Time for examination: 4.00 20.00 Please try to extend the answers as much as possible. Do not answer in a single sentence.
More informationLecture 6: Multimedia Information Retrieval Dr. Jian Zhang
Lecture 6: Multimedia Information Retrieval Dr. Jian Zhang NICTA & CSE UNSW COMP9314 Advanced Database S1 2007 jzhang@cse.unsw.edu.au Reference Papers and Resources Papers: Colour spaces-perceptual, historical
More informationOBJECT DESCRIPTION - FEATURE EXTRACTION
INF 4300 Digital Image Analysis OBJECT DESCRIPTION - FEATURE EXTRACTION Fritz Albregtsen 1.10.011 F06 1.10.011 INF 4300 1 Today We go through G&W section 11. Boundary Descriptors G&W section 11.3 Regional
More informationFeature description. IE PŁ M. Strzelecki, P. Strumiłło
Feature description After an image has been segmented the detected region needs to be described (represented) in a form more suitable for further processing. Representation of an image region can be carried
More informationSUMMARY PART I. What is texture? Uses for texture analysis. Computing texture images. Using variance estimates. INF 4300 Digital Image Analysis
INF 4 Digital Image Analysis SUMMARY PART I Fritz Albregtsen 4.. F 4.. INF 4 What is texture? Intuitively obvious, but no precise definition exists fine, coarse, grained, smooth etc Texture consists of
More informationCS443: Digital Imaging and Multimedia Binary Image Analysis. Spring 2008 Ahmed Elgammal Dept. of Computer Science Rutgers University
CS443: Digital Imaging and Multimedia Binary Image Analysis Spring 2008 Ahmed Elgammal Dept. of Computer Science Rutgers University Outlines A Simple Machine Vision System Image segmentation by thresholding
More informationAfdeling Toegepaste Wiskunde/ Division of Applied Mathematics Representation and description(skeletonization, shape numbers) SLIDE 1/16
Representation and description(skeletonization, shape numbers) SLIDE 1/16 Chapter 11: Representation and Description Asegmentedregioncanberepresentedby { boundarypixels internal pixels When shape is important,
More informationUNIVERSITY OF OSLO. Faculty of Mathematics and Natural Sciences
UNIVERSITY OF OSLO Faculty of Mathematics and Natural Sciences Exam: INF 4300 / INF 9305 Digital image analysis Date: Thursday December 21, 2017 Exam hours: 09.00-13.00 (4 hours) Number of pages: 8 pages
More informationIN5520 Digital Image Analysis. Two old exams. Practical information for any written exam Exam 4300/9305, Fritz Albregtsen
IN5520 Digital Image Analysis Two old exams Practical information for any written exam Exam 4300/9305, 2016 Exam 4300/9305, 2017 Fritz Albregtsen 27.11.2018 F13 27.11.18 IN 5520 1 Practical information
More informationAutoregressive and Random Field Texture Models
1 Autoregressive and Random Field Texture Models Wei-Ta Chu 2008/11/6 Random Field 2 Think of a textured image as a 2D array of random numbers. The pixel intensity at each location is a random variable.
More informationCS534 Introduction to Computer Vision Binary Image Analysis. Ahmed Elgammal Dept. of Computer Science Rutgers University
CS534 Introduction to Computer Vision Binary Image Analysis Ahmed Elgammal Dept. of Computer Science Rutgers University Outlines A Simple Machine Vision System Image segmentation by thresholding Digital
More information4 Parametrization of closed curves and surfaces
4 Parametrization of closed curves and surfaces Parametrically deformable models give rise to the question of obtaining parametrical descriptions of given pixel or voxel based object contours or surfaces,
More informationCOMP_4190 Artificial Intelligence Computer Vision. Computer Vision. Levels of Abstraction. Digital Images
COMP_49 Artificial Intelligence Computer Vision Jacky Baltes Department of Computer Science University of Manitoba Winnipeg, Manitoba Canada, RT N jacky@cs.umanitoba.ca http://www.cs.umanitoba.ca/~jacky
More informationFROM PIXELS TO REGIONS
Digital Image Analysis OBJECT REPRESENTATION FROM PIXELS TO REGIONS Fritz Albregtsen Today G & W Ch. 11.1 1 Representation Curriculum includes lecture notes. We cover the following: 11.1.1 Boundary following
More informationFeature Extraction and Image Processing, 2 nd Edition. Contents. Preface
, 2 nd Edition Preface ix 1 Introduction 1 1.1 Overview 1 1.2 Human and Computer Vision 1 1.3 The Human Vision System 3 1.3.1 The Eye 4 1.3.2 The Neural System 7 1.3.3 Processing 7 1.4 Computer Vision
More informationEdge and local feature detection - 2. Importance of edge detection in computer vision
Edge and local feature detection Gradient based edge detection Edge detection by function fitting Second derivative edge detectors Edge linking and the construction of the chain graph Edge and local feature
More informationCHAPTER 2 TEXTURE CLASSIFICATION METHODS GRAY LEVEL CO-OCCURRENCE MATRIX AND TEXTURE UNIT
CHAPTER 2 TEXTURE CLASSIFICATION METHODS GRAY LEVEL CO-OCCURRENCE MATRIX AND TEXTURE UNIT 2.1 BRIEF OUTLINE The classification of digital imagery is to extract useful thematic information which is one
More informationAnne Solberg
INF 4300 Digital Image Analysis OBJECT REPRESENTATION Anne Solberg 26.09.2012 26.09.2011 INF 4300 1 Today G & W Ch. 11.1 1 Representation Curriculum includes lecture notes. We cover the following: 11.1.1
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 WRI C225 Lecture 04 130131 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Histogram Equalization Image Filtering Linear
More informationMatching and Recognition in 3D. Based on slides by Tom Funkhouser and Misha Kazhdan
Matching and Recognition in 3D Based on slides by Tom Funkhouser and Misha Kazhdan From 2D to 3D: Some Things Easier No occlusion (but sometimes missing data instead) Segmenting objects often simpler From
More informationOCCHIO USA WHITE STONE VA TEL(866)
PARAMETERS : 79 Weight factors: 6 Parameter Other name Symbol Definition Formula Number Volume V The volume of the particle volume model. Equivalent Volume The volume of the sphere having the same projection
More informationMultimedia Information Retrieval
Multimedia Information Retrieval Prof Stefan Rüger Multimedia and Information Systems Knowledge Media Institute The Open University http://kmi.open.ac.uk/mmis Why content-based? Actually, what is content-based
More informationProcessing of binary images
Binary Image Processing Tuesday, 14/02/2017 ntonis rgyros e-mail: argyros@csd.uoc.gr 1 Today From gray level to binary images Processing of binary images Mathematical morphology 2 Computer Vision, Spring
More informationImage 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 informationTexture. Frequency Descriptors. Frequency Descriptors. Frequency Descriptors. Frequency Descriptors. Frequency Descriptors
Texture The most fundamental question is: How can we measure texture, i.e., how can we quantitatively distinguish between different textures? Of course it is not enough to look at the intensity of individual
More informationLecture 14 Shape. ch. 9, sec. 1-8, of Machine Vision by Wesley E. Snyder & Hairong Qi. Spring (CMU RI) : BioE 2630 (Pitt)
Lecture 14 Shape ch. 9, sec. 1-8, 12-14 of Machine Vision by Wesley E. Snyder & Hairong Qi Spring 2018 16-725 (CMU RI) : BioE 2630 (Pitt) Dr. John Galeotti The content of these slides by John Galeotti,
More informationCHAPTER 1 Introduction 1. CHAPTER 2 Images, Sampling and Frequency Domain Processing 37
Extended Contents List Preface... xi About the authors... xvii CHAPTER 1 Introduction 1 1.1 Overview... 1 1.2 Human and Computer Vision... 2 1.3 The Human Vision System... 4 1.3.1 The Eye... 5 1.3.2 The
More informationLog1 Contest Round 2 Theta Circles, Parabolas and Polygons. 4 points each
Name: Units do not have to be included. 016 017 Log1 Contest Round Theta Circles, Parabolas and Polygons 4 points each 1 Find the value of x given that 8 x 30 Find the area of a triangle given that it
More informationShape description and modelling
COMP3204/COMP6223: Computer Vision Shape description and modelling Jonathon Hare jsh2@ecs.soton.ac.uk Extracting features from shapes represented by connected components Recap: Connected Component A connected
More information09/11/2017. Morphological image processing. Morphological image processing. Morphological image processing. Morphological image processing (binary)
Towards image analysis Goal: Describe the contents of an image, distinguishing meaningful information from irrelevant one. Perform suitable transformations of images so as to make explicit particular shape
More informationDigital Image Processing
Digital Image Processing Third Edition Rafael C. Gonzalez University of Tennessee Richard E. Woods MedData Interactive PEARSON Prentice Hall Pearson Education International Contents Preface xv Acknowledgments
More informationECG782: 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 03 Image Processing Basics 13/01/28 http://www.ee.unlv.edu/~b1morris/ecg782/
More informationComputer Vision 2. SS 18 Dr. Benjamin Guthier Professur für Bildverarbeitung. Computer Vision 2 Dr. Benjamin Guthier
Computer Vision 2 SS 18 Dr. Benjamin Guthier Professur für Bildverarbeitung Computer Vision 2 Dr. Benjamin Guthier 1. IMAGE PROCESSING Computer Vision 2 Dr. Benjamin Guthier Content of this Chapter Non-linear
More informationProblem definition Image acquisition Image segmentation Connected component analysis. Machine vision systems - 1
Machine vision systems Problem definition Image acquisition Image segmentation Connected component analysis Machine vision systems - 1 Problem definition Design a vision system to see a flat world Page
More informationRegion-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 informationECG782: Multidimensional Digital Signal Processing
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spatial Domain Filtering http://www.ee.unlv.edu/~b1morris/ecg782/ 2 Outline Background Intensity
More informationCOMPUTER AND ROBOT VISION
VOLUME COMPUTER AND ROBOT VISION Robert M. Haralick University of Washington Linda G. Shapiro University of Washington A^ ADDISON-WESLEY PUBLISHING COMPANY Reading, Massachusetts Menlo Park, California
More informationEyeTech. Particle Size Particle Shape Particle concentration Analyzer ANKERSMID
EyeTech Particle Size Particle Shape Particle concentration Analyzer A new technology for measuring particle size in combination with particle shape and concentration. COMBINED LASERTECHNOLOGY & DIA Content
More informationSUPPLEMENTARY FILE S1: 3D AIRWAY TUBE RECONSTRUCTION AND CELL-BASED MECHANICAL MODEL. RELATED TO FIGURE 1, FIGURE 7, AND STAR METHODS.
SUPPLEMENTARY FILE S1: 3D AIRWAY TUBE RECONSTRUCTION AND CELL-BASED MECHANICAL MODEL. RELATED TO FIGURE 1, FIGURE 7, AND STAR METHODS. 1. 3D AIRWAY TUBE RECONSTRUCTION. RELATED TO FIGURE 1 AND STAR METHODS
More informationShape Modeling and Geometry Processing
252-0538-00L, Spring 2018 Shape Modeling and Geometry Processing Discrete Differential Geometry Differential Geometry Motivation Formalize geometric properties of shapes Roi Poranne # 2 Differential Geometry
More informationMorphological Image Processing
Morphological Image Processing Morphology Identification, analysis, and description of the structure of the smallest unit of words Theory and technique for the analysis and processing of geometric structures
More informationFigure 1: Workflow of object-based classification
Technical Specifications Object Analyst Object Analyst is an add-on package for Geomatica that provides tools for segmentation, classification, and feature extraction. Object Analyst includes an all-in-one
More informationMorphological Image Processing
Morphological Image Processing Binary image processing In binary images, we conventionally take background as black (0) and foreground objects as white (1 or 255) Morphology Figure 4.1 objects on a conveyor
More informationFiles Used in This Tutorial. Background. Feature Extraction with Example-Based Classification Tutorial
Feature Extraction with Example-Based Classification Tutorial In this tutorial, you will use Feature Extraction to extract rooftops from a multispectral QuickBird scene of a residential area in Boulder,
More informationCourse Number: Course Title: Geometry
Course Number: 1206310 Course Title: Geometry RELATED GLOSSARY TERM DEFINITIONS (89) Altitude The perpendicular distance from the top of a geometric figure to its opposite side. Angle Two rays or two line
More informationGeneric Fourier Descriptor for Shape-based Image Retrieval
1 Generic Fourier Descriptor for Shape-based Image Retrieval Dengsheng Zhang, Guojun Lu Gippsland School of Comp. & Info Tech Monash University Churchill, VIC 3842 Australia dengsheng.zhang@infotech.monash.edu.au
More informationUNIVERSITY OF OSLO. Faculty of Mathematics and Natural Sciences
UNIVERSITY OF OSLO Faculty of Mathematics and Natural Sciences Exam: INF 43 / INF 935 Digital image analysis Date: Thursday December 4, 4 Exam hours: 4.3-8.3 (4 hours) Number of pages: 6 pages Enclosures:
More informationComputer Vision I. Announcements. Fourier Tansform. Efficient Implementation. Edge and Corner Detection. CSE252A Lecture 13.
Announcements Edge and Corner Detection HW3 assigned CSE252A Lecture 13 Efficient Implementation Both, the Box filter and the Gaussian filter are separable: First convolve each row of input image I with
More informationMulti-dimensional Image Analysis
Multi-dimensional Image Analysis Lucas J. van Vliet www.ph.tn.tudelft.nl/~lucas Image Analysis Paradigm scene Image formation sensor pre-processing Image enhancement Image restoration Texture filtering
More informationECE 176 Digital Image Processing Handout #14 Pamela Cosman 4/29/05 TEXTURE ANALYSIS
ECE 176 Digital Image Processing Handout #14 Pamela Cosman 4/29/ TEXTURE ANALYSIS Texture analysis is covered very briefly in Gonzalez and Woods, pages 66 671. This handout is intended to supplement that
More informationImage Processing, Analysis and Machine Vision
Image Processing, Analysis and Machine Vision Milan Sonka PhD University of Iowa Iowa City, USA Vaclav Hlavac PhD Czech Technical University Prague, Czech Republic and Roger Boyle DPhil, MBCS, CEng University
More informationChapter 3 Image Registration. Chapter 3 Image Registration
Chapter 3 Image Registration Distributed Algorithms for Introduction (1) Definition: Image Registration Input: 2 images of the same scene but taken from different perspectives Goal: Identify transformation
More informationPolygons. Discuss with a partner what a POLYGON is. Write down the key qualities a POLYGON has. Share with the class what a polygon is?
Polygons Use a ruler to draw 3 different POLYGONS Discuss with a partner what a POLYGON is Write down the key qualities a POLYGON has Share with the class what a polygon is? *Can you find the area of each
More informationFeature extraction. Bi-Histogram Binarization Entropy. What is texture Texture primitives. Filter banks 2D Fourier Transform Wavlet maxima points
Feature extraction Bi-Histogram Binarization Entropy What is texture Texture primitives Filter banks 2D Fourier Transform Wavlet maxima points Edge detection Image gradient Mask operators Feature space
More informationTexture. Outline. Image representations: spatial and frequency Fourier transform Frequency filtering Oriented pyramids Texture representation
Texture Outline Image representations: spatial and frequency Fourier transform Frequency filtering Oriented pyramids Texture representation 1 Image Representation The standard basis for images is the set
More informationWe are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors
We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists 3,500 108,000 1.7 M Open access books available International authors and editors Downloads Our
More informationBinary Image Processing. Introduction to Computer Vision CSE 152 Lecture 5
Binary Image Processing CSE 152 Lecture 5 Announcements Homework 2 is due Apr 25, 11:59 PM Reading: Szeliski, Chapter 3 Image processing, Section 3.3 More neighborhood operators Binary System Summary 1.
More informationChain Code Histogram based approach
An attempt at visualizing the Fourth Dimension Take a point, stretch it into a line, curl it into a circle, twist it into a sphere, and punch through the sphere Albert Einstein Chain Code Histogram based
More informationMathematical Morphology and Distance Transforms. Robin Strand
Mathematical Morphology and Distance Transforms Robin Strand robin.strand@it.uu.se Morphology Form and structure Mathematical framework used for: Pre-processing Noise filtering, shape simplification,...
More informationMPEG-7 Visual shape descriptors
MPEG-7 Visual shape descriptors Miroslaw Bober presented by Peter Tylka Seminar on scientific soft skills 22.3.2012 Presentation Outline Presentation Outline Introduction to problem Shape spectrum - 3D
More informationVC 11/12 T14 Visual Feature Extraction
VC 11/12 T14 Visual Feature Extraction Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos Miguel Tavares Coimbra Outline Feature Vectors Colour Texture
More informationDirect Rendering. Direct Rendering Goals
May 2, 2005 Goals General Goals Small memory footprint Fast rendering High-quality results identical to those of Saffron V1 using distance-based anti-aliasing and alignment zones Goals Specific Goals Avoid
More informationEECS490: Digital Image Processing. Lecture #17
Lecture #17 Morphology & set operations on images Structuring elements Erosion and dilation Opening and closing Morphological image processing, boundary extraction, region filling Connectivity: convex
More informationImage Processing Fundamentals. Nicolas Vazquez Principal Software Engineer National Instruments
Image Processing Fundamentals Nicolas Vazquez Principal Software Engineer National Instruments Agenda Objectives and Motivations Enhancing Images Checking for Presence Locating Parts Measuring Features
More informationCorner Detection using Difference Chain Code as Curvature
Third International IEEE Conference on Signal-Image Technologies technologies and Internet-Based System Corner Detection using Difference Chain Code as Curvature Neeta Nain Vijay Laxmi Bhavitavya Bhadviya
More informationInteractive Math Glossary Terms and Definitions
Terms and Definitions Absolute Value the magnitude of a number, or the distance from 0 on a real number line Addend any number or quantity being added addend + addend = sum Additive Property of Area the
More informationCSCI 4620/8626. Coordinate Reference Frames
CSCI 4620/8626 Computer Graphics Graphics Output Primitives Last update: 2014-02-03 Coordinate Reference Frames To describe a picture, the world-coordinate reference frame (2D or 3D) must be selected.
More informationDEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING DS7201 ADVANCED DIGITAL IMAGE PROCESSING II M.E (C.S) QUESTION BANK UNIT I 1. Write the differences between photopic and scotopic vision? 2. What
More informationCS4733 Class Notes, Computer Vision
CS4733 Class Notes, Computer Vision Sources for online computer vision tutorials and demos - http://www.dai.ed.ac.uk/hipr and Computer Vision resources online - http://www.dai.ed.ac.uk/cvonline Vision
More informationLECTURE 4: FEATURE EXTRACTION DR. OUIEM BCHIR
LECTURE 4: FEATURE EXTRACTION DR. OUIEM BCHIR RGB COLOR HISTOGRAM HSV COLOR MOMENTS hsv_image = rgb2hsv(rgb_image) converts the RGB image to the equivalent HSV image. RGB is an m-by-n-by-3 image array
More informationRobust Shape Retrieval Using Maximum Likelihood Theory
Robust Shape Retrieval Using Maximum Likelihood Theory Naif Alajlan 1, Paul Fieguth 2, and Mohamed Kamel 1 1 PAMI Lab, E & CE Dept., UW, Waterloo, ON, N2L 3G1, Canada. naif, mkamel@pami.uwaterloo.ca 2
More informationTexture Analysis. Selim Aksoy Department of Computer Engineering Bilkent University
Texture Analysis Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr Texture An important approach to image description is to quantify its texture content. Texture
More informationDTU M.SC. - COURSE EXAM Revised Edition
Written test, 16 th of December 1999. Course name : 04250 - Digital Image Analysis Aids allowed : All usual aids Weighting : All questions are equally weighed. Name :...................................................
More informationSUMMARY PART I. Variance, 2, is directly a measure of roughness. A bounded measure of smoothness is
Digital Image Analsis SUMMARY PART I Fritz Albregtsen 4..6 Teture description of regions Remember: we estimate local properties (features) to be able to isolate regions which are similar in an image (segmentation),
More informationChapter 3. Sukhwinder Singh
Chapter 3 Sukhwinder Singh PIXEL ADDRESSING AND OBJECT GEOMETRY Object descriptions are given in a world reference frame, chosen to suit a particular application, and input world coordinates are ultimately
More informationEvaluation of MPEG-7 shape descriptors against other shape descriptors
Multimedia Systems 9: 15 3 (23) Digital Object Identifier (DOI) 1.17/s53-2-75-y Multimedia Systems Springer-Verlag 23 Evaluation of MPEG-7 shape descriptors against other shape descriptors Dengsheng Zhang,
More informationMorphological Image Processing
Morphological Image Processing Binary dilation and erosion" Set-theoretic interpretation" Opening, closing, morphological edge detectors" Hit-miss filter" Morphological filters for gray-level images" Cascading
More information4-Border and 4-Boundary
4-Border and 4-Boundary set S = black and white pixels; set M S = black pixels invalid edges = all edges between M and M = S \ M p M 4-inner pixel iff A 4 (p) M (shown in gray) p M 4-border pixel iff p
More informationDigital Image Processing. Lecture # 15 Image Segmentation & Texture
Digital Image Processing Lecture # 15 Image Segmentation & Texture 1 Image Segmentation Image Segmentation Group similar components (such as, pixels in an image, image frames in a video) Applications:
More information