/
Edge detection Edge detection

Edge detection - PowerPoint Presentation

phoebe-click
phoebe-click . @phoebe-click
Follow
451 views
Uploaded On 2016-03-19

Edge detection - PPT Presentation

Goal Identify sudden changes discontinuities in an image Intuitively most semantic and shape information from the image can be encoded in the edges More compact than pixels Ideal artists line drawing but artist is also using objectlevel knowledge ID: 261965

derivative edge image source edge derivative source image edges gradient direction threshold canny seitz filter detector thresholding high convolution steve discontinuity filters

Share:

Link:

Embed:

Download Presentation from below link

Download Presentation The PPT/PDF document "Edge detection" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

Slide1

Edge detection

Goal:

Identify sudden changes (discontinuities) in an imageIntuitively, most semantic and shape information from the image can be encoded in the edgesMore compact than pixelsIdeal: artist’s line drawing (but artist is also using object-level knowledge)

Source: D. LoweSlide2

Origin of edges

Edges are caused by a variety of factors:

depth discontinuity

surface color discontinuity

illumination discontinuity

surface normal discontinuity

Source: Steve SeitzSlide3

Characterizing edges

An edge is a place of rapid change in the image intensity function

image

intensity function

(along horizontal scanline)

first derivative

edges correspond to

extrema of derivativeSlide4

Derivatives with convolution

For 2D function

f(x,y), the partial derivative is:For discrete data, we can approximate using finite differences:

To implement above as convolution, what would be the associated filter?

Source:

K.

GraumanSlide5

Partial derivatives of an image

Which shows changes with respect to x?

-1 1

1 -1

or

-1 1Slide6

Finite difference filters

Other approximations of derivative filters exist:

Source: K. GraumanSlide7

The gradient points in the direction of most rapid increase in intensity

Image gradient

The gradient of an image:

The gradient direction is given by

Source: Steve Seitz

The

edge strength

is given by the gradient magnitude

How does this direction relate to the direction of the edge?Slide8

Effects of noise

Consider a single row or column of the imagePlotting intensity as a function of position gives a signal

Where is the edge?

Source: S. SeitzSlide9

Solution: smooth first

To find edges, look for peaks in

f

g

f * g

Source: S. SeitzSlide10

Differentiation is convolution, and convolution is associative:

This saves us one operation:

Derivative theorem of convolution

f

Source: S. SeitzSlide11

Derivative of Gaussian filter

Are these filters separable?

x

-direction

y

-directionSlide12

Derivative of Gaussian filter

Which one finds horizontal/vertical edges?

x

-direction

y

-directionSlide13

Smoothed derivative removes noise, but blurs edge. Also finds edges at different “scales”

1 pixel

3 pixels

7 pixels

Scale of Gaussian derivative filter

Source: D. ForsythSlide14

Review: Smoothing vs. derivative filters

Smoothing filters

Gaussian: remove “high-frequency” components; “low-pass” filterCan the values of a smoothing filter be negative?What should the values sum to?One: constant regions are not affected by the filter

Derivative filters

Derivatives of Gaussian

Can the values of a derivative filter be negative?

What should the values sum to

?

Zero:

n

o response in constant regions

High absolute value at points of high contrastSlide15

The Canny edge detector

original image

Slide credit: Steve SeitzSlide16

The Canny edge detector

norm of the gradientSlide17

The Canny edge detector

thresholdingSlide18

The Canny edge detector

thresholding

How to turn these thick regions of the gradient into curves?Slide19

Non-maximum suppression

Check if pixel is local maximum along gradient direction, select single max across width of the edge

requires checking interpolated pixels p and rSlide20

The Canny edge detector

thinning

(non-maximum suppression)

Problem: pixels along this edge didn’t survive the

thresholdingSlide21

Hysteresis thresholding

Use a high threshold to start edge curves, and a low threshold to continue them.

Source:

Steve

SeitzSlide22

Hysteresis thresholding

original image

high threshold

(strong edges)

low threshold

(weak edges)

hysteresis threshold

Source: L.

Fei-FeiSlide23

Recap: Canny edge detector

Filter image with derivative of Gaussian

Find magnitude and orientation of gradientNon-maximum suppression:Thin wide “ridges” down to single pixel widthLinking and

thresholding

(

hysteresis

):

Define two thresholds: low and high

Use the high threshold to start edge curves and the low threshold to continue them

MATLAB:

edge(image, ‘canny’);

J. Canny,

A Computational Approach To Edge Detection

, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986. Slide24

Edge detection is just the beginning…

Berkeley segmentation database:

http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/

image

human segmentation

gradient magnitudeSlide25

Background

Texture

Shadows

Low-level edges vs. perceived contours

Kristen Grauman, UT-Austin