Lecture 6 Feature Detection 2

8/14/2019 Lecture 6 Feature Detection 2

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Feature extraction

Scheme and tools in computer vision

Recognition of features is, in general, a complex procedure requiring a

variety of steps that successively transform the iconic data to

recognition information.

Handling unconstrained environments is still very challenging

problem.


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Visual Appearance-base Feature Extraction (Vision)

conditioning

labeling/grouping

extracting

matching model

environment

image

simplified image

properties

groups of pixel

cognition/action

thresholding

connectedcomponent

labeling

edgedetection

Hough

transfor-

mation

filtering

disparity

correlation

Image Processing Scheme

Computer Vision

Tools in Computer Vision


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Feature Extraction (Vision): Tools

Conditioning

Suppresses noise

Background normalization by suppressing uninteresting systematic or patterned

variations Done by:

gray-scale modification (e.g. thresholding)

(low pass) filtering

Labeling Determination of the spatial arrangement of the events, i.e. searching for a

structure

Grouping

Identification of the events by collecting together pixel participating in the samekind of event

Extracting

Compute a list of properties for each group

Matching (see chapter 5)


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Filtering and Edge Detection

Gaussian Smoothing

Removes high-frequency noise

Convolution of intensity image I with G:

with:

Edges

Locations where the brightness undergoes a sharp change,

Differentiate one or two times the image

Look for places where the magnitude of the derivative is large.

Noise, thus first filtering/smoothing required before edge detection


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Edge Detection

Ultimate goal of edge detection

an idealized line drawing.

Edge contours in the image correspond to important scene contours.


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Optimal Edge Detection: Canny

The processing steps

Convolution of image with the Gaussian function G

Finding maxima in the derivative

Canny combines both in one operation

(a) A Gaussian function. (b) The first derivative of a Gaussian function.


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Optimal Edge Detection: Canny 1D example

(a) Intensity 1-D profile of an ideal step edge.

(b) Intensity profileI(x) of a real edge.

(c) Its derivativeI(x).

(d) The result of the convolutionR(x) = G I, where G is the first

derivative of a Gaussian function.


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Optimal Edge Detection: Canny

1-D edge detector can be defined with the following steps:

1. Convolute the image I with G to obtain R.

2. Find the absolute value ofR.3.Mark those peaks |R| that are above some predefined thresholdT. The

threshold is chosen to eliminate spurious peaks due to noise.

2D Two dimensional Gaussian function


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Optimal Edge Detection: Canny Example

a) Example of Canny edge detection

b) After nonmaxima suppression (reduce the thickness of all edges to a

single pixel)


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Nonmaxima Suppression

Output of an edge detector is usually a

b/w image where the pixels with

gradient magnitude above a predefined

threshold are black and all the others

are white

Nonmaxima suppression generates

contours described with only one pixel

thinness by determining the localmaximum.


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Gradient Edge Detectors

Roberts

Prewitt

Sobel


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Example

a) Raw image

b) Filtered

(Sobel)

c) Thresholding

d) Nonmaxima

suppression

Fig.4.46Fig.4.46


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Comparison of Edge Detection Methods

Average time required to compute the edge figure of a 780 x 560 pixels image.

The times required to compute an edge image are proportional with the accuracy of

the resulting edge images


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Dynamic Thresholding

Changing illumination

Constant threshold level in edge detection is not suitable

Dynamically adapt the threshold level

consider only the n pixels with the highest gradient magnitude for further

calculation steps

The gradient magnitude of the point where n is reached (counted backward from

highest value) is used as the temporary threshold value

(a) Number of pixels with a specific gradient magnitude in the image of Fig. 4.46(b).

(b) Same as (a), but with logarithmic scale


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Hough Transform: Straight Edge Extraction

All pointsp on a straight-line edge must satisfyyp = m1xp + b1 .

Each point (xp, yp) that is part of this line constraints the parameter m1

and b1. The Hough transform finds the line (line-parameters m, b) that get most

votes from the edge pixels in the image.

This is realized by four steps1. Create a 2D array A [m,b] with axes that tessellate the values of m and b.

2.Initialize the array A to zero: A[m b]=0 for all values of m, b.

3. For each edge pixel (xp, yp) in the image, loop over all values of m and b:if yp = m1xp + b1 then A[m,b]+=1

4. Search cells in A with largest value. They correspond to extracted straight-

line edge in the image.


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Grouping, Clustering: Assigning Features to Features

pixels feature object

Connected Component Labeling

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Floor Plane Extraction

Vision based identification of traversable

Assumptions

Obstacles differ in appearance from the ground The ground is flat and its angle to the camera is known

There are no overhanging obstacles

Floor plane extraction in artificial environments Shakey, the first autonomous mobile robot developed from 1977 to 1972 at

SRI, use vision based floor plane extraction

Textureless white floor tiles, with walls painted with a high-contrast stripof black paint and the edges of all simple polygonal obstacles were also

painted black


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Adaptive Floor Plane Extraction

The parameters defining the

expected appearance of the floor

are allowed to vary over time

Use edge detection and color

detection jointly

The processing steps As pre-processing, smooth Ifusing

a Gaussian smoothing operator

Initialize a histogram array H with

n intensity values:for

For every pixel (x,y) in Ifincrement

the histogram:


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Whole-Image Features

OmniCam: a CCDcamera mountedtoward a parabolic

mirror Compact

representations of

the entire localregion

Extract one ormore features

from the imagethat are correlatedwell with the

robots position


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Direct Extraction: image histograms

When combined with theWhen combined with the catadioptriccatadioptric cameracameras field of views field of view

invariance, we can create a system that is invariant to robot roinvariance, we can create a system that is invariant to robot rotationtation

and insensitive to smalland insensitive to small--scale robot translation.scale robot translation.

A color cameraA color cameras output image contains information along multiples output image contains information along multiple

bands: R, G, and B values as well as hue, saturation, and luminabands: R, G, and B values as well as hue, saturation, and luminancence

valuesvalues

The simplest histogramThe simplest histogram--based extraction strategy is to build separatebased extraction strategy is to build separate

1D histograms charactering each band. We use1D histograms charactering each band. We use GGii to refer to an arrayto refer to an arraystoring the values in bandstoring the values in band ii for all pixels infor all pixels in GG..


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Image Histograms

The processing steps

As pre-processing, smooth using a Gaussian smoothing operator

Initialize with n levels:

For every pixel (x,y) in increment the histogram:

+

++

=i ii

ii

ii

ii

khkk

khhhKHd )2log2log(),(

Jeffrey divergence:Jeffrey divergence:

Using measure such as JeffreyUsing measure such as Jeffrey

divergence, mobile robots havedivergence, mobile robots haveused wholeused whole--image histogramimage histogram

features to identify their position infeatures to identify their position in

realreal--time against a database oftime against a database of

previously recorded images ofpreviously recorded images oflocations in their environment.locations in their environment.


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Image Fingerprint Extraction

Highly distinctive combination of simple features v: vertical edgev: vertical edgeA,B,C,..,P: hue binsA,B,C,..,P: hue bins

String from current locationString from current location

Strings from the databaseStrings from the database


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Homework 5 (Due two weeks from now)

Use MATLAB to do feature detection on the image I posted on

pipeline. You are asked to use Canny detection and Sobel detection.

Compare the results of two algorithms. You need submit the following

items

Report including introduction of two detection algorithms, the

procedures and final results.

Matlab file for each algorithm so that I can run them on my PC.

Lecture 6 Feature Detection 2

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