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

Lecture 2: Edge detection

Noah Snavely

From Sandlot Science

2 ### 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

Edges are caused by a variety of factors

surface normal discontinuity

depth discontinuity

surface color discontinuity

illumination discontinuity

4 ### Images as functions…

Edges look like steep cliffs

5 ### Characterizing edges

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

image

Source: L. Lazebnik

6 ### 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 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 Source: L. Lazebnik

9 ### Effects of noise

Where is the edge?

Noisy input image

Source: S. Seitz

10 ### Solution: smooth first

f

Source: S. Seitz

11 ### Associative property of convolution

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

Source: S. Seitz

12 ### 2D edge detection filters

derivative of Gaussian (x)

Gaussian

13 ### Derivative of Gaussian filter

y-direction

x-direction

14 ### 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

Source: Wikipedia

16 ### Example

original image (Lena)

17 ### Finding edges

18 ### Finding edges

thresholding

where is the edge?

19 ### Questions

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