Computer and Robot Vision I

Slides:



Advertisements
Similar presentations
電腦視覺 Computer and Robot Vision I
Advertisements

Course Syllabus 1.Color 2.Camera models, camera calibration 3.Advanced image pre-processing Line detection Corner detection Maximally stable extremal regions.
CDS 301 Fall, 2009 Image Visualization Chap. 9 November 5, 2009 Jie Zhang Copyright ©
Chapter 9: Morphological Image Processing
Course Syllabus 1.Color 2.Camera models, camera calibration 3.Advanced image pre-processing Line detection Corner detection Maximally stable extremal regions.
Each pixel is 0 or 1, background or foreground Image processing to
Introduction to Morphological Operators
Morphological Image Processing Md. Rokanujjaman Assistant Professor Dept of Computer Science and Engineering Rajshahi University.
Provides mathematical tools for shape analysis in both binary and grayscale images Chapter 13 – Mathematical Morphology Usages: (i)Image pre-processing.
Digital Image Processing, 2nd ed. © 2002 R. C. Gonzalez & R. E. Woods Chapter 9 Morphological Image Processing Chapter 9 Morphological.
Morphology Structural processing of images Image Processing and Computer Vision: 33 Morphological Transformations Set theoretic methods of extracting.
Chapter 9 Morphological Image Processing. Preview Morphology: denotes a branch of biology that deals with the form and structure of animals and planets.
1 © 2010 Cengage Learning Engineering. All Rights Reserved. 1 Introduction to Digital Image Processing with MATLAB ® Asia Edition McAndrew ‧ Wang ‧ Tseng.
Morphological Image Processing
2007Theo Schouten1 Morphology Set theory is the mathematical basis for morphology. Sets in Euclidic space E 2 (or rather Z 2 : the set of pairs of integers)
E.G.M. PetrakisBinary Image Processing1 Binary Image Analysis Segmentation produces homogenous regions –each region has uniform gray-level –each region.
Lecture 5. Morphological Image Processing. 10/6/20152 Introduction ► ► Morphology: a branch of biology that deals with the form and structure of animals.
Morphological Image Processing
MATHEMATICAL MORPHOLOGY I.INTRODUCTION II.BINARY MORPHOLOGY III.GREY-LEVEL MORPHOLOGY.
Mathematical Morphology Lecture 14 Course book reading: GW Lucia Ballerini Digital Image Processing.
Chapter 9.  Mathematical morphology: ◦ A useful tool for extracting image components in the representation of region shape.  Boundaries, skeletons,
Mathematical Morphology Set-theoretic representation for binary shapes
Course Syllabus 1.Color 2.Camera models, camera calibration 3.Advanced image pre-processing Line detection Corner detection Maximally stable extremal regions.
Digital Image Processing Chapter 9: Morphological Image Processing 5 September 2007 Digital Image Processing Chapter 9: Morphological Image Processing.
Morphological Image Processing
Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.
Chapter 3 cont’d. Binary Image Analysis. Binary image morphology (nonlinear image processing)
Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.
DIGITAL IMAGE PROCESSING Instructors: Dr J. Shanbehzadeh Mostafa Mahdijo Mostafa Mahdijo ( J.Shanbehzadeh.
Digital Image Processing CSC331 Morphological image processing 1.
Morphological Image Processing การทำงานกับรูปภาพด้วยวิธีมอร์โฟโลจิคัล
Erosion: Erosion is used for shrinking of element A by using element B
CS654: Digital Image Analysis
References Books: Chapter 11, Image Processing, Analysis, and Machine Vision, Sonka et al Chapter 9, Digital Image Processing, Gonzalez & Woods.
CS654: Digital Image Analysis
CDS 301 Fall, 2008 Image Visualization Chap. 9 November 11, 2008 Jie Zhang Copyright ©
Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.
1 Mathematic Morphology used to extract image components that are useful in the representation and description of region shape, such as boundaries extraction.
Morphological Image Processing Robotics. 2/22/2016Introduction to Machine Vision Remember from Lecture 12: GRAY LEVEL THRESHOLDING Objects Set threshold.
 Mathematical morphology is a tool for extracting image components that are useful in the representation and description of region shape, such as boundaries,
Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.
BYST Morp-1 DIP - WS2002: Morphology Digital Image Processing Morphological Image Processing Bundit Thipakorn, Ph.D. Computer Engineering Department.
曾令祺 性別:男 籍貫:大陸湖北省 Facebook/ :
Lecture(s) 3-4. Morphological Image Processing. 3/13/20162 Introduction ► ► Morphology: a branch of biology that deals with the form and structure of.
Morphological Image Processing (Chapter 9) CSC 446 Lecturer: Nada ALZaben.
Morphological Image Processing
Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.
Lecture 11+x+1 Chapter 9 Morphological Image Processing.
Mathematical Morphology
Digital Image Processing CP-7008 Lecture # 09 Morphological Image Processing Fall 2011.
Computer and Robot Vision I
Computer and Robot Vision I
Morphological Image Processing
HIT and MISS.
Introduction to Morphological Operators
Computer Vision Chapter 9
CS Digital Image Processing Lecture 5
Binary Image processing بهمن 92
Computer and Robot Vision I
Computer and Robot Vision I
Computer and Robot Vision I
Morphological Image Processing
Binary Image Analysis: Part 2 Readings: Chapter 3:
Chapter 6 Neighborhood Operators
ECE 692 – Advanced Topics in Computer Vision
Digital Image Processing Lecture 14: Morphology
Computer and Robot Vision I
Computer and Robot Vision I
DIGITAL IMAGE PROCESSING Elective 3 (5th Sem.)
Morphological Filters Applications and Extension Morphological Filters
Presentation transcript:

Computer and Robot Vision I Chapter 5 Mathematical Morphology Presented by: 傅楸善 & 董子嘉 0925708818 D04944016@ntu.edu.tw 指導教授: 傅楸善 博士 Digital Camera and Computer Vision Laboratory Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan, R.O.C.

5.1 Introduction mathematical morphology works on shape shape: prime carrier of information in machine vision morphological operations: simplify image data, preserve essential shape characteristics, eliminate irrelevancies shape: correlates directly with decomposition of object, object features, object surface defects, assembly defects DC & CV Lab. CSIE NTU

5.2 Binary Morphology set theory: language of binary mathematical morphology sets in mathematical morphology: represent shapes Euclidean N-space: EN discrete Euclidean N-space: ZN N=2: hexagonal grid, square grid DC & CV Lab. CSIE NTU

5.2 Binary Morphology (cont’) dilation, erosion: primary morphological operations opening, closing: composed from dilation, erosion opening, closing: related to shape representation, decomposition, primitive extraction DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation dilation: combines two sets by vector addition of set elements dilation of A by B: addition commutative  dilation commutative: binary dilation: Minkowski addition DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) A: referred as set, image B: structuring element: kernel dilation by disk: isotropic swelling or expansion DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) dilation by kernel without origin: might not have common pixels with A translation of dilation: always can contain A DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) =lena.bin.128= DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) =lena.bin.dil= By structuring element : DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) N4: set of four 4-neighbors of (0,0) but not (0,0,) 4-isolated pixels removed only points in I with at least one of its 4-neighbors remain At: translation of A by the point t DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) dilation: union of translates of kernel addition associative dilation associative associativity of dilation: chain rule: iterative rule dilation of translated kernel: translation of dilation DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) dilation distributes over union dilating by union of two sets: the union of the dilation DC & CV Lab. CSIE NTU

5.2.1 Binary Dilation (cont’) dilating A by kernel with origin guaranteed to contain A extensive: operators whose output contains input dilation extensive when kernel contains origin dilation preserves order increasing: preserves order DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion erosion: morphological dual of dilation erosion of A by B: set of all x s.t. erosion: shrink: reduce: DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) =lena.bin.128= DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) =Lena.bin.ero= DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) erosion of A by B: set of all x for which B translated to x contained in A if B translated to x contained in A then x in A B erosion: difference of elements a and b DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) dilation: union of translates erosion: intersection of negative translates DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) Minkowski subtraction: close relative to erosion Minkowski subtraction: erosion: shrinking of the original image antiextensive: operated set contained in the original set erosion antiextensive: if origin contained in kernel DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) if then because eroding A by kernel without origin can have nothing in common with A DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) dilating translated set results in a translated dilation eroding by translated kernel results in negatively translated erosion dilation, erosion: increasing DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) eroding by larger kernel produces smaller result Dilation, erosion similar that one does to foreground, the other to background similarity: duality dual: negation of one equals to the other on negated variables DeMorgan’s law: duality between set union and intersection DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) negation of a set: complement negation of a set in two possible ways in morphology logical sense: set complement geometric sense: reflection: reversing of set orientation DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) complement of erosion: dilation of the complement by reflection Theorem 5.1: Erosion Dilation Duality DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) Corollary 5.1: erosion of intersection of two sets: intersection of erosions DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) erosion of a kernel of union of two sets: intersection of erosions erosion of kernel of intersection of two sets: contains union of erosions no stronger DC & CV Lab. CSIE NTU

DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) chain rule for erosion holds when kernel decomposable through dilation duality does not imply cancellation on morphological equalities containment relationship holds DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) genus g(I): number of connected components minus number of holes of I, 4-connected for object, 8-connected for background 8-connected for object, 4-connected for background DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) DC & CV Lab. CSIE NTU

5.2.2 Binary Erosion (cont’) DC & CV Lab. CSIE NTU

Joke DC & CV Lab. CSIE NTU

5.2.3 Hit-and-Miss Transform hit-and-miss: selects corner points, isolated points, border points hit-and-miss: performs template matching, thinning, thickening, centering hit-and-miss: intersection of erosions J,K kernels satisfy hit-and-miss of set A by (J,K) hit-and-miss: to find upper right-hand corner DC & CV Lab. CSIE NTU

5.2.3 Hit-and-Miss Transform (cont’) DC & CV Lab. CSIE NTU

DC & CV Lab. CSIE NTU

5.2.3 Hit-and-Miss Transform (cont’) J locates all pixels with south, west neighbors of A K locates all pixels of Ac with south, west neighbors in Ac J and K displaced from one another Hit-and-miss: locate particular spatial patterns DC & CV Lab. CSIE NTU

5.2.3 Hit-and-Miss Transform (cont’) hit-and-miss: to compute genus of a binary image DC & CV Lab. CSIE NTU

5.2.3 Hit-and-Miss Transform (cont’) DC & CV Lab. CSIE NTU

5.2.3 Hit-and-Miss Transform (cont’) hit-and-miss: thickening and thinning hit-and-miss: counting hit-and-miss: template matching DC & CV Lab. CSIE NTU

5.2.4 Dilation and Erosion Summary DC & CV Lab. CSIE NTU

5.2.4 Dilation and Erosion Summary (cont’) DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing dilation and erosion: usually employed in pairs B K: opening of image B by kernel K B K: closing of image B by kernel K B open under K: B open w.r.t. K: B= B K B close under K: B close w.r.t. K: B= B K DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) =lena.bin.128= DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) =lena.bin.open= DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) morphological opening, closing: no relation to topologically open, closed sets opening characterization theorem A K: selects points covered by some translation of K, entirely contained in A DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) opening with disk kernel: smoothes contours, breaks narrow isthmuses opening with disk kernel: eliminates small islands, sharp peaks, capes closing by disk kernel: smoothes contours, fuses narrow breaks, long, thin gulfs closing with disk kernel: eliminates small holes, fill gaps on the contours DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) =lena.bin.128= DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) =lena.bin.close= DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) unlike erosion and dilation: opening invariant to kernel translation opening antiextensive like erosion and dilation: opening increasing DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) A K: those pixels covered by sweeping kernel all over inside of A F: shape with body and handle L: small disk structuring element with radius just larger than handle width extraction of the body and handle by opening and taking the residue DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) extraction of trunk and arms with vertical and horizontal kernels DC & CV Lab. CSIE NTU

DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) extraction of base, trunk, horizontal and vertical areas DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) noisy background line segment removal DC & CV Lab. CSIE NTU

DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) DC & CV Lab. CSIE NTU

DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) decomposition into parts DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) closing: dual of opening like opening: closing invariant to kernel translation closing extensive like dilation, erosion, opening: closing increasing DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) opening idempotent closing idempotent if L K not necessarily follows that DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) DC & CV Lab. CSIE NTU

5.2.5 Opening and Closing (cont’) closing may be used to detect spatial clusters of points DC & CV Lab. CSIE NTU

Joke DC & CV Lab. CSIE NTU

5.2.6 Morphological Shape Feature Extraction morphological pattern spectrum: shape-size histogram [Maragos 1987] DC & CV Lab. CSIE NTU

5.2.7 Fast Dilations and Erosions decompose kernels to make dilations and erosions fast DC & CV Lab. CSIE NTU

5.3 Connectivity morphology and connectivity: close relation DC & CV Lab. CSIE NTU

5.3.1 Separation Relation S separation if and only if S symmetric, exclusive, hereditary, extensive DC & CV Lab. CSIE NTU

5.3.2 Morphological Noise Cleaning and Connectivity images perturbed by noise can be morphologically filtered to remove some noise DC & CV Lab. CSIE NTU

5.3.3 Openings, Holes, and Connectivity opening can create holes in a connected set that is being opened DC & CV Lab. CSIE NTU

5.3.4 Conditional Dilation select connected components of image that have nonempty erosion conditional dilation J defined iteratively J0 = J J are points in the regions we want to select conditional dilation J =Jm where m is the smallest index Jm=Jm-1 DC & CV Lab. CSIE NTU

DC & CV Lab. CSIE NTU

5.4 Generalized Openings and Closings generalized opening: any increasing, antiextensive, idempotent operation generalized closing: any increasing, extensive, idempotent operation DC & CV Lab. CSIE NTU

Joke End DC & CV Lab. CSIE NTU

5.5 Gray Scale Morphology binary dilation, erosion, opening, closing naturally extended to gray scale extension: uses min or max operation gray scale dilation: surface of dilation of umbra gray scale dilation: maximum and a set of addition operations gray scale erosion: minimum and a set of subtraction operations DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion top: top surface of A: denoted by umbra of f: denoted by DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) gray scale dilation: surface of dilation of umbras dilation of f by k: denoted by DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) gray scale erosion: surface of binary erosions of one umbra by the other umbra DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) =lena.im= DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) =lena.im.dil= DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) Structuring Elements: Octagon Value = 0 * DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) =lena.im= DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) =lena.im.ero= DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.1 Gray Scale Dilation and Erosion (cont’) DC & CV Lab. CSIE NTU

5.5.2 Umbra Homomorphism Theorems surface and umbra operations: inverses of each other, in a certain sense surface operation: left inverse of umbra operation DC & CV Lab. CSIE NTU

5.5.2 Umbra Homomorphism Theorems Proposition 5.1 Proposition 5.2 Proposition 5.3 DC & CV Lab. CSIE NTU

5.5.3 Gray Scale Opening and Closing gray scale opening of f by kernel k denoted by f k gray scale closing of f by kernel k denoted by f k DC & CV Lab. CSIE NTU

5.5.3 Gray Scale Opening and Closing (cont’) =lena.im.open= DC & CV Lab. CSIE NTU

5.5.3 Gray Scale Opening and Closing (cont’) =lena.im.close= DC & CV Lab. CSIE NTU

5.5.3 Gray Scale Opening and Closing (cont’) duality of gray scale dilation, erosion  duality of opening, closing DC & CV Lab. CSIE NTU

5.5.3 Gray Scale Opening and Closing (cont’) DC & CV Lab. CSIE NTU

joke DC & CV Lab. CSIE NTU

5.6 Openings, Closings, and Medians median filter: most common nonlinear noise-smoothing filter median filter: for each pixel, the new value is the median of a window median filter: robust to outlier pixel values, leaves sharp edges median root images: images remain unchanged after median filter DC & CV Lab. CSIE NTU

Original Image Median Root Image

5.7 Bounding Second Derivatives opening or closing a gray scale image simplifies the image complexity DC & CV Lab. CSIE NTU

5.8 Distance Transform and Recursive Morphology DC & CV Lab. CSIE NTU

5.8 Distance Transform and Recursive Morphology (cont’) Fig 5.39 (b) fire burns from outside but burns only down-ward and right-ward DC & CV Lab. CSIE NTU

5.9 Generalized Distance Transform DC & CV Lab. CSIE NTU

5.10 Medial Axis medial axis transform: medial axis with distance function DC & CV Lab. CSIE NTU

5.10.1 Medial Axis and Morphological Skeleton morphological skeleton of a set A by kernel K, where DC & CV Lab. CSIE NTU

5.10.1 Medial Axis and Morphological Skeleton (cont’) DC & CV Lab. CSIE NTU

5.10.1 Medial Axis and Morphological Skeleton (cont’) DC & CV Lab. CSIE NTU

5.10.1 Medial Axis and Morphological Skeleton (cont’) DC & CV Lab. CSIE NTU

5.11 Morphological Sampling Theorem Before sets are sampled for morphological processing, they must be morphologically simplified by an opening or a closing. Such sampled sets can be reconstructed in two ways: by either a closing or a dilation. DC & CV Lab. CSIE NTU

=========== Joke=========== DC & CV Lab. CSIE NTU

5.12 Summary morphological operations: shape extraction, noise cleaning, thickening morphological operations: thinning, skeletonizing DC & CV Lab. CSIE NTU

Homework Write programs which do binary morphological dilation, erosion, opening, closing, and hit-and-miss transform on a binary image (Due Nov. 1) Write programs which do gray-scale morphological dilation, erosion, opening, and closing on a gray-scale image (Due Nov. 15) DC & CV Lab. CSIE NTU