computer vision basics · 2008. 1. 9. · computer vision basics ... a binary image b 2. a...
TRANSCRIPT
![Page 1: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/1.jpg)
Computer Vision Basics
• Image Terminology
• Binary Operations
• Filtering
• Edge Operators
![Page 2: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/2.jpg)
Digital Image Terminology:
0 0 0 0 1 0 00 0 1 1 1 0 00 1 95 96 94 93 920 0 92 93 93 92 920 0 93 93 94 92 930 1 92 93 93 93 930 0 94 95 95 96 95
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)
![Page 3: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/3.jpg)
The Three Stages of Computer Vision
• low-level
• mid-level
• high-level
image image
image features
features analysis
![Page 4: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/4.jpg)
Low-Level
blurring
sharpening
![Page 5: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/5.jpg)
Low-Level
Canny
ORT
Mid-Leveloriginal image edge image
edge image circular arcs and line segments
datastructure
![Page 6: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/6.jpg)
Mid-level
K-meansclustering
original color image regions of homogeneous color
(followed byconnectedcomponentanalysis)
datastructure
![Page 7: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/7.jpg)
edge image
consistentline clusters
low-level
mid-level
high-level
Low- to High-Level
Building Recognition
![Page 8: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/8.jpg)
Binary Image Analysis• used in a variety of applications:
part inspectionrivetingfish countingdocument processing
• consists of a set of image analysis operationsthat are used to produce or process binaryimages, usually images of 0’s and 1’s.
000100100010000001111000100000010010001000
![Page 9: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/9.jpg)
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?
![Page 10: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/10.jpg)
Useful Operations1. 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 ...
![Page 11: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/11.jpg)
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
pixelcounts
0 256
![Page 12: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/12.jpg)
Automatic Thresholding: Otsu’s Method
Assumption: the histogram is bimodal
t
Method: find the threshold t that minimizesthe weighted sum of within-group variancesfor the two groups that result from separatingthe gray tones at value t.
Grp 1 Grp 2
Works well if the assumption holds.
![Page 13: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/13.jpg)
Thresholding Example
original image pixels above threshold
![Page 14: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/14.jpg)
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
![Page 15: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/15.jpg)
• ErosionErosion 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
![Page 16: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/16.jpg)
Structuring Elements
A structuring element is a shape mask used inthe basic morphological operations.
They can be any shape and size that isdigitally representable, and each has an origin.
boxhexagon disk something
box(length,width) disk(diameter)
![Page 17: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/17.jpg)
Dilation with Structuring Elements
The arguments to dilation and erosion are
1. a binary image B2. a structuring element S
dilate(B,S) takes binary image B, places the originof structuring element S over each 1-pixel, and ORsthe structuring element S into the output image atthe corresponding position.
0 0 0 00 1 1 00 0 0 0
11 1
0 1 1 00 1 1 10 0 0 0
originBS
dilate
B ⊕
S
![Page 18: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/18.jpg)
Erosion with Structuring Elements
erode(B,S) takes a binary image B, places the origin of structuring element S over every pixel position, andORs a binary 1 into that position of the output image only ifevery position of S (with a 1) covers a 1 in B.
0 0 1 1 00 0 1 1 00 0 1 1 01 1 1 1 1
111
0 0 0 0 00 0 1 1 00 0 1 1 00 0 0 0 0
B S
origin
erode
B S
![Page 19: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/19.jpg)
Opening and Closing
• Closing is the compound operation of dilation followedby erosion (with the same structuring element)
• Opening is the compound operation of erosion followedby dilation (with the same structuring element)
![Page 20: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/20.jpg)
![Page 21: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/21.jpg)
Application: Gear Tooth Inspection
originalbinary image
detecteddefects
![Page 22: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/22.jpg)
Connected Components LabelingOnce 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
![Page 23: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/23.jpg)
Methods for CC Analysis1. 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 thelabel of its equivalence class.
![Page 24: Computer Vision Basics · 2008. 1. 9. · Computer Vision Basics ... a binary image B 2. a structuring element S dilate(B,S) takes binary image B, places the origin. of structuring](https://reader036.vdocuments.net/reader036/viewer/2022081410/609fc2c3610d4849fe025393/html5/thumbnails/24.jpg)
Labelings shown as Pseudo-Color
connectedcomponentsof 1’s fromcleaned,thresholdedimage
connectedcomponentsof clusterlabels