feature detection
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Feature Detection. Feature Detection. Image Features –Decisions!. Features such as edges, corners, junction, eyes, … are obtained by making some decision from the image measurements. - PowerPoint PPT PresentationTRANSCRIPT
Computer Vision September 2002 L1.1© 2002 by Davi Geiger
Feature Detection
Feature Detection
Computer Vision September 2002 L1.2© 2002 by Davi Geiger
Image Features –Decisions!
Features such as edges, corners, junction, eyes, … are obtained by making some decision from the image measurements.
Decisions are the result of some comparison followed by a choice. Examples (i) if a measurement is above a threshold we accept, not otherwise; (ii) if a measurement is the largest compared to others, we select it.
Computer Vision September 2002 L1.3© 2002 by Davi Geiger
Decisions: Edgels (Edge-pixels and Orientation)
)|),,,(ˆ||),2/,,(ˆ|(maxarg),(max syxIDsyxIDyxs
TsyxIDsyxID )|),,,(ˆ||),2
,,(ˆ|( such that Edge threshold: Decision!
Edge orientation:Decision!
A step edge at The value is (equally) large for both and as shown (in red) for scale s=3 pixels. It is also large for values not shownHowever, the quantity is significantly larger for
|),2/,,(~
| syxID
|),,,(~
||),2/,,(~
| syxIDsyxID
),,,(ˆ syxID ),
2,,(ˆ syxID
3,
4,
6,0
Computer Vision September 2002 L1.4© 2002 by Davi Geiger
),(3max yx
The gray level indicates the angle: the darkest one is 0 degrees. The larger is the angle the lighter is displayed, up to
|),2/,,(ˆ|
||),,,(ˆ||),2/,,(ˆ||),(
max
maxmaxmax
syxID
syxIDsyxIDyx
s
sss
Strength of the Edgel
Decisions: Edgels (cont.)
HTsyxH ),,(ˆ
Eliminate some spurious locations. Decision!
Computer Vision September 2002 L1.5© 2002 by Davi Geiger
Decisions: Local Angle Change
),()sin,cos(),( maxmaxmax yxyyxxyx sss Angle change
y
x
),(max yxs
2)sin,cos(max
yyxxs
2),(max
yxs
A contour segment
),( yxs
212222 )sin,cos( yx
is the contour curvature multiplied by the arc length , where
),(max yxs .),(max yxs where
Computer Vision September 2002 L1.6© 2002 by Davi Geiger
TsyxID |),,,(~
|such that
HTsyxH ),,(~
Decisions: Junctions, Corners
Junction threshold: Decision!
Eliminate spurious locations. Decisions!
Examining the values of where allow us to characterize the junctions. For example, when only two value of pass the test and or suggest a corner. Corners are many times called L-junctions. If three angles are detected, it may be a T-junction or an Y-junction. T-junctions exhibit one region with angle near and usually arise in images due to surface occlusions in a scene. Four angles suggest a X-junction, and usually arise in images due to surface transparency in the scene.
Note that this detector also detects many edgels.
65),(),( 122 yxyx
Remove (Undo) detection if
65)2( 2
TsyxID |),,,(~
|
Computer Vision September 2002 L1.7© 2002 by Davi Geiger
Decisions: Connecting Edgels, Pseudocode
Algorithm to link edgels. Start with a seed location (xc,yc)
Contour-Follower(xc , yc)
if (Edgel(xc , yc ) NIL )
Link-neighbors+(xc , yc ,max)
Link-neighbors-(xc , yc ,max)
end
Link-neighbors±(xc , yc ,max)
xn± = xc ± xmax cosmax ;
yn± = yc ± ymax sinmax ;
if (Edgel(xn±, yn±) NIL )
Link((xc , yc), (xn±, yn±))
Link-neighbors±( xn± , yn± ,max(xn± , yn±) )
end
max
(xc , yc)
Computer Vision September 2002 L1.8© 2002 by Davi Geiger
Decisions: Connecting Edgels, Pseudocode
Link-neighbors±(xc , yc ,max-c)
xn± = xc ± xmax cosmax ;
yn± = yc ± ymax sinmax ;
if ((Edgel(xn±, yn±) NIL )&(Coherence(xc , yc xn±, yn±)))
Link((xc , yc), (xn±, yn±))
Link-neighbors±( xn± , yn± ,max-n(xn± , yn±) )
endCoherence(xc , yc xn, yn )
if ( and ) return True else Nilend
1maxmax |),2
,,(~
),2
,,(~
| TsyxIsyxI cccnnn
1maxmax |),2
3,,(
~),
2
3,,(
~| TsyxIsyxI cccnnn
Computer Vision September 2002 L1.9© 2002 by Davi Geiger
Threshold Parameters: Estimation
We have considered at least four parameters: 1,,, TTTT H
How to estimate them? One technique is Histogram partition:
Plot the Histogram and find the parameter that “best partition it”:
|),2/,,(ˆ| syxID
ountingHistogram c:
T