Computer Vision Basics Image Terminology Binary Operations Filtering Edge Operators.

Slides:



Advertisements
Similar presentations
Binary Image Analysis Selim Aksoy Department of Computer Engineering Bilkent University
Advertisements

Computational Biology, Part 23 Biological Imaging II Robert F. Murphy Copyright  1996, 1999, All rights reserved.
Binary Image Analysis Selim Aksoy Department of Computer Engineering Bilkent University
July 27, 2002 Image Processing for K.R. Precision1 Image Processing Training Lecture 1 by Suthep Madarasmi, Ph.D. Assistant Professor Department of Computer.
1Ellen L. Walker ImageJ Java image processing tool from NIH Reads / writes a large variety of images Many image processing operations.
Digital Image Processing In The Name Of God Digital Image Processing Lecture3: Image enhancement M. Ghelich Oghli By: M. Ghelich Oghli
電腦視覺 Computer and Robot Vision I Chapter2: Binary Machine Vision: Thresholding and Segmentation Instructor: Shih-Shinh Huang 1.
Chapter 9: Morphological Image Processing
Each pixel is 0 or 1, background or foreground Image processing to
Common Low-level Operations for Processing & Enhancement
1 Binary Image Analysis Binary image analysis consists of a set of image analysis operations that are used to produce or process binary images, usually.
Binary Image Analysis: Part 2 Readings: Chapter 3: mathematical morphology region properties region adjacency 1.
CS 376b Introduction to Computer Vision 02 / 25 / 2008 Instructor: Michael Eckmann.
1. Binary Image B(r,c) 2 0 represents the background 1 represents the foreground
1 Introduction What IS computer vision? Where do images come from? the analysis of digital images by a computer.
图像处理技术讲座(10) Digital Image Processing (10) 灰度的数学形态学(2) Mathematical morphology in gray scale (2) 顾 力栩 上海交通大学 计算机系
E.G.M. PetrakisBinary Image Processing1 Binary Image Analysis Segmentation produces homogenous regions –each region has uniform gray-level –each region.
GUIDED BY: C.VENKATESH PRESENTED BY: S.FAHIMUDDIN C.VAMSI KRISHNA ASST.PROFESSOR M.V.KRISHNA REDDY (DEPT.ECE)
CSE554Binary PicturesSlide 1 CSE 554 Lecture 1: Binary Pictures Fall 2013.
CSE554Binary PicturesSlide 1 CSE 554 Lecture 1: Binary Pictures Fall 2014.
1. Binary Image B(r,c) 2 0 represents the background 1 represents the foreground
Chapter 3 Binary Image Analysis. Types of images ► Digital image = I[r][c] is discrete for I, r, and c.  B[r][c] = binary image - range of I is in {0,1}
Lecture 5. Morphological Image Processing. 10/6/20152 Introduction ► ► Morphology: a branch of biology that deals with the form and structure of animals.
Chapter 9.  Mathematical morphology: ◦ A useful tool for extracting image components in the representation of region shape.  Boundaries, skeletons,
Chap 3 : Binary Image Analysis. Counting Foreground Objects.
CS 6825: Binary Image Processing – binary blob metrics
CS 376b Introduction to Computer Vision 02 / 22 / 2008 Instructor: Michael Eckmann.
September 23, 2014Computer Vision Lecture 5: Binary Image Processing 1 Binary Images Binary images are grayscale images with only two possible levels of.
1 Binary Image Analysis Binary image analysis consists of a set of image analysis operations that are used to produce or process binary images, usually.
CS 1699: Intro to Computer Vision Edges and Binary Images Prof. Adriana Kovashka University of Pittsburgh September 15, 2015.
Image Segmentation and Morphological Processing Digital Image Processing in Life- Science Aviad Baram
AdeptSight Image Processing Tools Lee Haney January 21, 2010.
1 Regions and Binary Images Hao Jiang Computer Science Department Sept. 25, 2014.
Mathematical Morphology Mathematical morphology (matematická morfologie) –A special image analysis discipline based on morphological transformations of.
1 Regions and Binary Images Hao Jiang Computer Science Department Sept. 24, 2009.
DIGITAL IMAGE PROCESSING Instructors: Dr J. Shanbehzadeh Mostafa Mahdijo Mostafa Mahdijo ( J.Shanbehzadeh.
Digital Image Processing CSC331 Morphological image processing 1.
Low level Computer Vision 1. Thresholding 2. Convolution 3. Morphological Operations 4. Connected Component Extraction 5. Feature Extraction 1.
Digital Image Processing
Nottingham Image Analysis School, 23 – 25 June NITS Image Segmentation Guoping Qiu School of Computer Science, University of Nottingham
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.
BYST Morp-1 DIP - WS2002: Morphology Digital Image Processing Morphological Image Processing Bundit Thipakorn, Ph.D. Computer Engineering Department.
Machine Vision ENT 273 Hema C.R. Binary Image Processing Lecture 3.
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
1. Images as Functions 2 A Binary image is a function B(x,y) Є [0,1] Gray-tone image is a function: g(x,y) Є [0.,1,….L-1] A color image is represented.
COMP 9517 Computer Vision Binary Image Analysis 4/15/2018
Common Low-level Operations for Processing & Enhancement
CSE 554 Lecture 1: Binary Pictures
Binary Image Analysis Gokberk Cinbis
Introduction Computer vision is the analysis of digital images
CSE 455 Computer Vision Autumn 2014
EE 596 Machine Vision Autumn 2015
Lecture 8 Introduction to Binary Morphology
Binary Image Analysis: Part 1 Readings: Chapter 3: 3.1, 3.4, 3.8
Introduction Computer vision is the analysis of digital images
CSE 455 Computer Vision Autumn 2010
Binary Image Analysis used in a variety of applications:
Binary Image Analysis: Part 1 Readings: Chapter 3: 3.1, 3.4, 3.8
Introduction What IS computer vision?
Binary Image processing بهمن 92
Department of Computer Engineering
Binary Image Analysis: Part 2 Readings: Chapter 3:
ECE 692 – Advanced Topics in Computer Vision
Introduction Computer vision is the analysis of digital images
Computer and Robot Vision I
Morphological Operators
DIGITAL IMAGE PROCESSING Elective 3 (5th Sem.)
Binary Image Analysis used in a variety of applications:
Presentation transcript:

Computer Vision Basics Image Terminology Binary Operations Filtering Edge Operators

Digital Image Terminology: pixel (with value 94) its 3x3 neighborhood binary image gray-scale (or gray-tone) image color image multi-spectral image range image labeled image region of medium intensity resolution (7x7)

The Three Stages of Computer Vision low-level mid-level high-level image image features features analysis

Low-Level blurring sharpening

Low-Level Canny ORT Mid-Level original image edge image circular arcs and line segments data structure

Mid-level K-means clustering original color image regions of homogeneous color (followed by connected component analysis) data structure

edge image consistent line clusters low-level mid-level high-level Low- to High-Level Building Recognition

Binary Image Analysis used in a variety of applications: part inspection riveting fish counting document processing consists of a set of image analysis operations that are used to produce or process binary images, usually images of 0’s and 1’s

Example: red blood cell image Many blood cells are separate objects Many touch – bad! Salt and pepper noise from thresholding What operations are needed to clean it up?

Useful Operations 1.Thresholding a gray-tone image 2. Determining good thresholds 3. Filtering with mathematical morphology 4. Connected components analysis 5. Numeric feature extraction location features gray-tone features shape features...

Thresholding Background is black Healthy cherry is bright Bruise is medium dark Histogram shows two cherry regions (black background has been removed) gray-tone values pixel counts 0 256

Automatic Thresholding: Otsu’s Method Assumption: the histogram is bimodal t Method: find the threshold t that minimizes the weighted sum of within-group variances for the two groups that result from separating the gray tones at value t. Grp 1 Grp 2 Works well if the assumption holds.

Thresholding Example original image pixels above threshold

Dilation expands the connected sets of 1s of a binary image. It can be used for 1. growing features 2. filling holes and gaps Mathematical Morphology (Dilation, Erosion, Closing, Opening) Dilation

Erosion Erosion shrinks the connected sets of 1s of a binary image. It can be used for 1. shrinking features 2. Removing bridges, branches and small protrusions

Structuring Elements A structuring element is a shape mask used in the basic morphological operations. They can be any shape and size that is digitally representable, and each has an origin. box hexagon disk something box(length,width) disk(diameter)

Dilation with Structuring Elements The arguments to dilation and erosion are 1.a binary image B 2.a structuring element S dilate(B,S) takes binary image B, places the origin of structuring element S over each 1-pixel, and ORs the structuring element S into the output image at the corresponding position origin B S dilate B  S

Erosion with Structuring Elements erode(B,S) takes a binary image B, places the origin of structuring element S over every pixel position, and ORs a binary 1 into that position of the output image only if every position of S (with a 1) covers a 1 in B B S origin erode B S

Opening and Closing Closing is the compound operation of dilation followed by erosion (with the same structuring element) Opening is the compound operation of erosion followed by dilation (with the same structuring element)

Application: Gear Tooth Inspection original binary image detected defects

Connected Components Labeling Once you have a binary image, you can identify and then analyze each connected set of pixels. The connected components operation takes in a binary image and produces a labeled image in which each pixel has the integer label of either the background (0) or a component. original thresholded opening+closing components

Methods for CC Analysis 1. Recursive Tracking (almost never used) 2. Parallel Growing (needs parallel hardware) 3. Row-by-Row (most common) a. propagate labels down to the bottom, recording equivalences b. Compute equivalence classes c. Replace each labeled pixel with the label of its equivalence class.

Labelings shown as Pseudo-Color connected components of 1’s from cleaned, thresholded image connected components of cluster labels