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CS4670: Computer Vision
CS4670: Computer Vision
Edge detection
Edge detection
Origin of Edges
Origin of Edges
Images as functions…
Images as functions…
Characterizing edges
Characterizing edges
Image derivatives
Image derivatives
Image gradient
Image gradient
Image gradient
Image gradient
Effects of noise
Effects of noise
Solution: smooth first
Solution: smooth first
Associative property of convolution
Associative property of convolution
2D edge detection filters
2D edge detection filters
Derivative of Gaussian filter
Derivative of Gaussian filter
The Sobel operator
The Sobel operator
Sobel operator: example
Sobel operator: example
Example
Example
Finding edges
Finding edges
Finding edges
Finding edges
Questions
Questions

Презентация на тему: «Имидж лик или личина по мхк». Автор: Noah Snavely. Файл: «Имидж лик или личина по мхк.ppt». Размер zip-архива: 4044 КБ.

Имидж лик или личина по мхк

содержание презентации «Имидж лик или личина по мхк.ppt»
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1 CS4670: Computer Vision

CS4670: Computer Vision

Lecture 2: Edge detection

Noah Snavely

From Sandlot Science

2 Edge detection

Edge detection

Convert a 2D image into a set of curves Extracts salient features of the scene More compact than pixels

TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAA

3 Origin of Edges

Origin of Edges

Edges are caused by a variety of factors

surface normal discontinuity

depth discontinuity

surface color discontinuity

illumination discontinuity

4 Images as functions…

Images as functions…

Edges look like steep cliffs

5 Characterizing edges

Characterizing edges

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

image

Source: L. Lazebnik

6 Image derivatives

Image derivatives

How can we differentiate a digital image F[x,y]? Option 1: reconstruct a continuous image, f, then compute the derivative Option 2: take discrete derivative (finite difference)

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How would you implement this as a linear filter?

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Source: S. Seitz

7 Image gradient

Image gradient

The gradient of an image:

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

The edge strength is given by the gradient magnitude: The gradient direction is given by: how does this relate to the direction of the edge?

Source: Steve Seitz

8 Image gradient

Image gradient

Source: L. Lazebnik

9 Effects of noise

Effects of noise

Where is the edge?

Noisy input image

Source: S. Seitz

10 Solution: smooth first

Solution: smooth first

f

Source: S. Seitz

11 Associative property of convolution

Associative property of convolution

Differentiation is convolution, and convolution is associative: This saves us one operation:

Source: S. Seitz

12 2D edge detection filters

2D edge detection filters

derivative of Gaussian (x)

Gaussian

13 Derivative of Gaussian filter

Derivative of Gaussian filter

y-direction

x-direction

14 The Sobel operator

The Sobel operator

Common approximation of derivative of Gaussian

The standard defn. of the Sobel operator omits the 1/8 term doesn’t make a difference for edge detection the 1/8 term is needed to get the right gradient magnitude

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15 Sobel operator: example

Sobel operator: example

Source: Wikipedia

16 Example

Example

original image (Lena)

17 Finding edges

Finding edges

gradient magnitude

18 Finding edges

Finding edges

thresholding

where is the edge?

19 Questions

Questions

«Имидж лик или личина по мхк»
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