An Overview of Morphological Filtering
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Lecture 5: Binary Morphology
Lecture 5: Binary Morphology c Bryan S. Morse, Brigham Young University, 1998–2000 Last modified on January 15, 2000 at 3:00 PM Contents 5.1 What is Mathematical Morphology? ................................. 1 5.2 Image Regions as Sets ......................................... 1 5.3 Basic Binary Operations ........................................ 2 5.3.1 Dilation ............................................. 2 5.3.2 Erosion ............................................. 2 5.3.3 Duality of Dilation and Erosion ................................. 3 5.4 Some Examples of Using Dilation and Erosion . .......................... 3 5.5 Proving Properties of Mathematical Morphology .......................... 3 5.6 Hit-and-Miss .............................................. 4 5.7 Opening and Closing .......................................... 4 5.7.1 Opening ............................................. 4 5.7.2 Closing ............................................. 5 5.7.3 Properties of Opening and Closing ............................... 5 5.7.4 Applications of Opening and Closing .............................. 5 Reading SH&B, 11.1–11.3 Castleman, 18.7.1–18.7.4 5.1 What is Mathematical Morphology? “Morphology” can literally be taken to mean “doing things to shapes”. “Mathematical morphology” then, by exten- sion, means using mathematical principals to do things to shapes. 5.2 Image Regions as Sets The basis of mathematical morphology is the description of image regions as sets. For a binary image, we can consider the “on” (1) pixels to all comprise a set of values from the “universe” of pixels in the image. Throughout our discussion of mathematical morphology (or just “morphology”), when we refer to an image A, we mean the set of “on” (1) pixels in that image. The “off” (0) pixels are thus the set compliment of the set of on pixels. By Ac, we mean the compliment of A,or the off (0) pixels. -
Hit-Or-Miss Transform
Hit-or-miss transform • Used to extract pixels with specific neighbourhood configurations from an image • Grey scale extension exist • Uses two structure elements B1 and B2 to find a given foreground and background configuration, respectively C HMT B X={x∣B1x⊆X ,B2x⊆X } • Example: 1 Morphological Image Processing Lecture 22 (page 1) 9.4 The hit-or-miss transformation Illustration... Morphological Image Processing Lecture 22 (page 2) Objective is to find a disjoint region (set) in an image • If B denotes the set composed of X and its background, the• match/hit (or set of matches/hits) of B in A,is A B =(A X) [Ac (W X)] ¯∗ ª ∩ ª − Generalized notation: B =(B1,B2) • Set formed from elements of B associated with B1: • an object Set formed from elements of B associated with B2: • the corresponding background [Preceeding discussion: B1 = X and B2 =(W X)] − More general definition: • c A B =(A B1) [A B2] ¯∗ ª ∩ ª A B contains all the origin points at which, simulta- • ¯∗ c neously, B1 found a hit in A and B2 found a hit in A Hit-or-miss transform C HMT B X={x∣B1x⊆X ,B2x⊆X } • Can be written in terms of an intersection of two erosions: HMT X= X∩ X c B B1 B2 2 Hit-or-miss transform • Simple example usages - locate: – Isolated foreground pixels • no neighbouring foreground pixels – Foreground endpoints • one or zero neighbouring foreground pixels – Multiple foreground points • pixels having more than two neighbouring foreground pixels – Foreground contour points • pixels having at least one neighbouring background pixel 3 Hit-or-miss transform example • Locating 4-connected endpoints SEs for 4-connected endpoints Resulting Hit-or-miss transform 4 Hit-or-miss opening • Objective: keep all points that fit the SE. -
Introduction to Mathematical Morphology
COMPUTER VISION, GRAPHICS, AND IMAGE PROCESSING 35, 283-305 (1986) Introduction to Mathematical Morphology JEAN SERRA E. N. S. M. de Paris, Paris, France Received October 6,1983; revised March 20,1986 1. BACKGROUND As we saw in the foreword, there are several ways of approaching the description of phenomena which spread in space, and which exhibit a certain spatial structure. One such approach is to consider them as objects, i.e., as subsets of their space of definition. The method which derives from this point of view is. called mathematical morphology [l, 21. In order to define mathematical morphology, we first require some background definitions. Consider an arbitrary space (or set) E. The “objects” of this :space are the subsets X c E; therefore, the family that we have to handle theoretically is the set 0 (E) of all the subsets X of E. The set p(E) is incomparably less arbitrary than E itself; indeed it is constructured to be a Boolean algebra [3], that is: (i) p(E) is a complete lattice, i.e., is provided with a partial-ordering relation, called inclusion, and denoted by “ c .” Moreover every (finite or not) family of members Xi E p(E) has a least upper bound (their union C/Xi) and a greatest lower bound (their intersection f7 X,) which both belong to p(E); (ii) The lattice p(E) is distributiue, i.e., xu(Ynz)=(XuY)n(xuZ) VX,Y,ZE b(E) and is complemented, i.e., there exist a greatest set (E itself) and a smallest set 0 (the empty set) such that every X E p(E) possesses a complement Xc defined by the relationships: XUXC=E and xn xc= 0. -
Newsletternewsletter Official Newsletter of the International Association for Mathematical Geology
No. 71 December 2005 IAMGIAMG NewsletterNewsletter Official Newsletter of the International Association for Mathematical Geology Contents oronto - welcoming city on the shores of Lake Ontario and site of IAMG 2005 this last August. The organizers of the confer- ence chose well: Hart House of the University of Toronto, an President’s Forum .................................................................................3 Tivy-draped, Oxbridge style college building, recalling the British her- IAMG Journal Report ...........................................................................4 itage, was a very suitable venue for plenary talks in the “Great Hall”, topical sessions in various smaller rooms, spaces for work sessions, Association Business ............................................................................4 posters and the “music room” for reading, computer connections, and The Matrix Man ....................................................................................5 listening to the occasional piano player. Member News .......................................................................................5 Downtown Toronto turned out to be a nicely accessible city with Conference Reports ...............................................................................6 many places within walking distance from the university, and a good public transportation system of Toronto Photo Album ............................................................................7 subways and bus lines to reach Student Affairs ....................................................................................10 -
M-Idempotent and Self-Dual Morphological Filters ا
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 34, NO. 4, APRIL 2012 805 Short Papers___________________________________________________________________________________________________ M-Idempotent and Self-Dual and morphological filters (opening and closing, alternating filters, Morphological Filters alternating sequential filters (ASF)). The quest for self-dual and/or idempotent operators has been the focus of many investigators. We refer to some important work Nidhal Bouaynaya, Member, IEEE, in the area in chronological order. A morphological operator that is Mohammed Charif-Chefchaouni, and idempotent and self-dual has been proposed in [28] by using the Dan Schonfeld, Fellow, IEEE notion of the middle element. The middle element, however, can only be obtained through repeated (possibly infinite) iterations. Meyer and Serra [19] established conditions for the idempotence of Abstract—In this paper, we present a comprehensive analysis of self-dual and the class of the contrast mappings. Heijmans [8] proposed a m-idempotent operators. We refer to an operator as m-idempotent if it converges general method for the construction of morphological operators after m iterations. We focus on an important special case of the general theory of that are self-dual, but not necessarily idempotent. Heijmans and lattice morphology: spatially variant morphology, which captures the geometrical Ronse [10] derived conditions for the idempotence of the self-dual interpretation of spatially variant structuring elements. We demonstrate that every annular operator, in which case it will be called an annular filter. increasing self-dual morphological operator can be viewed as a morphological An alternative framework for morphological image processing that center. Necessary and sufficient conditions for the idempotence of morphological operators are characterized in terms of their kernel representation. -
Learning Deep Morphological Networks with Neural Architecture Search APREPRINT
LEARNING DEEP MORPHOLOGICAL NETWORKS WITH NEURAL ARCHITECTURE SEARCH APREPRINT Yufei Hu Nacim Belkhir U2IS, ENSTA Paris, Institut Polytechnique de Paris Safrantech, Safran Group [email protected] [email protected] Jesus Angulo Angela Yao CMM, Mines ParisTech, PSL Research University School of Computing National University of Singapore [email protected] [email protected] Gianni Franchi U2IS, ENSTA Paris, Institut Polytechnique de Paris [email protected] June 16, 2021 ABSTRACT Deep Neural Networks (DNNs) are generated by sequentially performing linear and non-linear processes. Using a combination of linear and non-linear procedures is critical for generating a sufficiently deep feature space. The majority of non-linear operators are derivations of activation functions or pooling functions. Mathematical morphology is a branch of mathematics that provides non-linear operators for a variety of image processing problems. We investigate the utility of integrating these operations in an end-to-end deep learning framework in this paper. DNNs are designed to acquire a realistic representation for a particular job. Morphological operators give topological descriptors that convey salient information about the shapes of objects depicted in images. We propose a method based on meta-learning to incorporate morphological operators into DNNs. The learned architecture demonstrates how our novel morphological operations significantly increase DNN performance on various tasks, including picture classification and edge detection. Keywords Mathematical Morphology; deep learning; architecture search. arXiv:2106.07714v1 [cs.CV] 14 Jun 2021 1 Introduction Over the last decade, deep learning has made several breakthroughs and demonstrated successful applications in various fields (e.g. -
Puupintojen Laadunvalvonta Ja Luokittelu
Image thresholding During the thresholding process, individual pixels in an image are marked as “object” pixels if their value is greater than some threshold value (assuming an object to be brighter than the background) and as “background” pixels otherwise. This convention is known as threshold above. Variants include threshold below, which is opposite of threshold above; threshold inside, where a pixel is labeled "object" if its value is between two thresholds; and threshold outside, which is the opposite of threshold inside (Shapiro, et al. 2001:83). Typically, an object pixel is given a value of “1” while a background pixel is given a value of “0.” Finally, a binary image is created by coloring each pixel white or black, depending on a pixel's label. Image thresholding Sezgin and Sankur (2004) categorize thresholding methods into the following six groups based on the information the algorithm manipulates (Sezgin et al., 2004): • histogram shape-based methods, where, for example, the peaks, valleys and curvatures of the smoothed histogram are analyzed • clustering-based methods, where the gray-level samples are clustered in two parts as background and foreground (object), or alternately are modeled as a mixture of two Gaussians • entropy-based methods result in algorithms that use the entropy of the foreground and background regions, the cross-entropy between the original and binarized image, etc. • object attribute-based methods search a measure of similarity between the gray-level and the binarized images, such as fuzzy shape similarity, edge coincidence, etc. • spatial methods [that] use higher-order probability distribution and/or correlation between pixels • local methods adapt the threshold value on each pixel to the local image characteristics." Mathematical morphology • Mathematical Morphology was born in 1964 from the collaborative work of Georges Matheron and Jean Serra, at the École des Mines de Paris, France. -
Morphological Image Compression Using Skeletons
Morphological Image Compression using Skeletons Nimrod Peleg Update: March 2008 What is Mathematical Morphology ? “... a theory for analysis of spatial structures which was initiated by George Matheron and Jean Serra. It is called Morphology since it aims at the analyzing the shape and the forms of the objects. It is Mathematical in the sense that the analysis is based on set theory, topology, lattice, random functions, etc. “ (Serra and Soille, 1994). Basic Operation: Dilation • Dilation: replacing every pixel with the maximum value of its neighborhood determined by the structure element (SE) X B x b|, x X b B Original SE Dilation Dilation demonstration Original Figure Dilated Image With Circle SE Dilation example Dilation (Israel Map, circle SE): Basic Operation: Erosion • Erosion: A Dual operation - replacing every pixel with the minimum value of its neighborhood determined by the SE C XBXB C Original SE Erosion Erosion demonstration Original Figure Eroded Image With Circle SE Erosion example Erosion (Lena, circle SE): Original B&W Lena Eroded Lena, SE radius = 4 Composed Operations: Opening • Opening: Erosion and than Dilation removes positive peaks narrower than the SE XBXBB () Original SE Opening Opening demonstration Original Figure Opened Image With Circle SE Opening example Opening (Lena, circle SE): Original B&W Lena Opened Lena, SE radius = 4 Composed Operations: Closing • Closing: Dilation and than Erosion removes negative peaks narrower than the SE XBXBB () Original SE Closing Closing demonstration Original Figure Closed Image With Circle SE Closing example • Closing (Lena, circle SE): Original B&W Lena Closed Lena, SE radius = 4 Cleaning “white” noise Opening & Closing Cleaning “black” noise Closing & Opening Conditioned dilation Y ()()XXBY • Example: Original Figure Opened Figure By Circle SE Conditioned Dilated Figure Opening By Reconstruction Rec( mask , kernel ) lim( mask ( kernel )) n n • Open by rec. -
Computer Vision – A
CS4495 Computer Vision – A. Bobick Morphology CS 4495 Computer Vision Binary images and Morphology Aaron Bobick School of Interactive Computing CS4495 Computer Vision – A. Bobick Morphology Administrivia • PS7 – read about it on Piazza • PS5 – grades are out. • Final – Dec 9 • Study guide will be out by Thursday hopefully sooner. CS4495 Computer Vision – A. Bobick Morphology Binary Image Analysis Operations that produce or process binary images, typically 0’s and 1’s • 0 represents background • 1 represents foreground 00010010001000 00011110001000 00010010001000 Slides: Linda Shapiro CS4495 Computer Vision – A. Bobick Morphology Binary Image Analysis Used in a number of practical applications • Part inspection • Manufacturing • Document processing CS4495 Computer Vision – A. Bobick Morphology What kinds of operations? • Separate objects from background and from one another • Aggregate pixels for each object • Compute features for each object CS4495 Computer Vision – A. Bobick Morphology Example: Red blood cell image • Many blood cells are separate objects • Many touch – bad! • Salt and pepper noise from thresholding • How useable is this data? CS4495 Computer Vision – A. Bobick Morphology Results of analysis • 63 separate objects detected • Single cells have area about 50 • Noise spots • Gobs of cells CS4495 Computer Vision – A. Bobick Morphology Useful Operations • Thresholding a gray-scale image • Determining good thresholds • Connected components analysis • Binary mathematical morphology • All sorts of feature extractors, statistics (area, centroid, circularity, …) CS4495 Computer Vision – A. Bobick Morphology Thresholding • Background is black • Healthy cherry is bright • Bruise is medium dark • Histogram shows two cherry regions (black background has been removed) Pixel counts Pixel 0 Grayscale values 255 CS4495 Computer Vision – A. Bobick Morphology Histogram-Directed Thresholding • How can we use a histogram to separate an image into 2 (or several) different regions? Is there a single clear threshold? 2? 3? CS4495 Computer Vision – A. -
Mathematical Morphology
Mathematical morphology Václav Hlaváč Czech Technical University in Prague Faculty of Electrical Engineering, Department of Cybernetics Center for Machine Perception http://cmp.felk.cvut.cz/˜hlavac, [email protected] Outline of the talk: Point sets. Morphological transformation. Skeleton. Erosion, dilation, properties. Thinning, sequential thinning. Opening, closing, hit or miss. Distance transformation. Morphology is a general concept 2/55 In biology: the analysis of size, shape, inner structure (and relationship among them) of animals, plants and microorganisms. In linguistics: analysis of the inner structure of word forms. In materials science: the study of shape, size, texture and thermodynamically distinct phases of physical objects. In signal/image processing: mathematical morphology – a theoretical model based on lattice theory used for signal/image preprocessing, segmentation, etc. Mathematical morphology, introduction 3/55 Mathematical morphology (MM): is a theory for analysis of planar and spatial structures; is suitable for analyzing the shape of objects; is based on a set theory, integral algebra and lattice algebra; is successful due to a a simple mathematical formalism, which opens a path to powerful image analysis tools. The morphological way . The key idea of morphological analysis is extracting knowledge from the relation of an image and a simple, small probe (called the structuring element), which is a predefined shape. It is checked in each pixel, how does this shape matches or misses local shapes in the image. MM founding fathers 4/55 Georges Matheron (∗ 1930, † 2000) Jean Serra (∗ 1940) Matheron, G. Elements pour une Theorie del Milieux Poreux Masson, Paris, 1967. Serra, J. Image Analysis and Mathematical Morphology, Academic Press, London 1982. -
Lecture Notes in Statistics 183 Edited by P
Lecture Notes in Statistics 183 Edited by P. Bickel, P. Diggle, S. Fienberg, U. Gather, I. Olkin, S. Zeger Michel Bilodeau Fernand Meyer Michel Schmitt (Editors) Space, Structure and Randomness Contributions in Honor of Georges Matheron in the Field of Geostatistics, Random Sets and Mathematical Morphology With 121 Figures Michel Bilodeau Fernand Meyer Michel Schmitt Ecole des Mines de Paris Ecole des Mines de Paris Ecole des Mines de Paris Centre de Morphologie Centre de Morphologie Direction des recherches Mathématique Mathématique 60 Boulevard Saint Michel 35 rue Saint Honoré 35 rue Saint Honoré 75272 Paris Cedex 06 77305 Fontainebleau 77305 Fontainebleau France France France [email protected] [email protected] [email protected] Library of Congress Control Number: 2005927580 ISBN 10: 0-387-20331-1 Printed on acid-free paper ISBN 13: 978-0387-20331-7 © 2005 Springer Science+Business Media, Inc. All rights reserved. This work may not be translated or copied in whole or in part without the written per- mission of the publisher (Springer Science+Business Media, Inc., 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. -
Morphological Image Processing
Digital Image Processing MORPHOLOGICAL Hamid R. Rabiee Fall 2015 Image Analysis 2 Morphological Image Processing Edge Detection Keypoint Detection Image Feature Extraction Texture Image Analysis Shape Analysis Color Image Processing Template Matching Image Segmentation Image Analysis 3 Figure 9.1 of Jain book Image Analysis 4 Ultimate aim of image processing applications: Automatic description, interpretation, or understanding the scene For example, an image understanding system should be able to send the report: The field of view contains a dirt road surrounded by grass. Image Analysis 5 Table 9.1 of Jain book Image Analysis 6 original binary erosion dilation opening closing Morphological Analysis Edge Detection Keypoint Detection 7 Morphological Image Processing (MIP) Outline 8 Preview Preliminaries Dilation Erosion Opening Closing Hit-or-miss transformation Outline (cont.) 9 Morphological algorithms Boundary Extraction Hole filling Extraction of connected components Convex hall Thinning Thickening Skeleton Outline (cont.) 10 Gray-scale morphology Dilation and erosion Opening and closing Rank filter, median filter, and majority filter Preview 11 Mathematical morphology a tool for extracting image components that are useful in the representation and description of region shape, such as boundaries, skeletons, and convex hull Can be used to extract attributes and “meaning” from images, unlike pervious image processing tools which their input and output were images. Morphological techniques such as morphological