gradients and edges

Computer Vision - A Modern ApproachSet: Linear Filters

Slides by D.A. Forsyth

Gradients and edges

• Points of sharp change in an image are interesting:– change in reflectance– change in object– change in illumination– noise

• Sometimes called edge points

• General strategy– determine image gradient

– now mark points where gradient magnitude is particularly large wrt neighbours (ideally, curves of such points).

There are three major issues: 1) The gradient magnitude at different scales is different; which should we choose? 2) The gradient magnitude is large along thick trail; how do we identify the significant points? 3) How do we link the relevant points up into curves?

Smoothing and Differentiation

• Issue: noise– smooth before differentiation– two convolutions to smooth, then differentiate?– actually, no - we can use a derivative of Gaussian filter

• because differentiation is convolution, and convolution is associative

The scale of the smoothing filter affects derivative estimates, and alsothe semantics of the edges recovered.

1 pixel 3 pixels 7 pixels

The Laplacian of Gaussian

• Another way to detect an extremal first derivative is to look for a zero second derivative

• Appropriate 2D analogy is rotation invariant– the Laplacian

• Bad idea to apply a Laplacian without smoothing– smooth with Gaussian, apply

Laplacian– this is the same as filtering

with a Laplacian of Gaussian filter

• Now mark the zero points where there is a sufficiently large derivative, and enough contrast

sigma=2

sigma=4

contrast=1 contrast=4LOG zero crossings

We still have unfortunate behaviourat corners

We wish to mark points along the curve where the magnitude is biggest.We can do this by looking for a maximum along a slice normal to the curve(non-maximum suppression). These points should form a curve. There arethen two algorithmic issues: at which point is the maximum, and where is thenext one?

Non-maximumsuppression

At q, we have a maximum if the value is larger than those at both p and at r. Interpolate to get these values.

Predictingthe nextedge point

Assume the marked point is an edge point. Then we construct the tangent to the edge curve (which is normal to the gradient at that point) and use this to predict the next points (here either r or s).

Remaining issues

• Check that maximum value of gradient value is sufficiently large– drop-outs? use hysteresis

• use a high threshold to start edge curves and a low threshold to continue them.

Notice

• Something nasty is happening at corners• Scale affects contrast• Edges aren’t bounding contours

fine scalehigh threshold

coarse scale,high threshold

coarsescalelowthreshold

Orientation representations

• The gradient magnitude is affected by illumination changes– but it’s direction isn’t

• We can describe image patches by the swing of the gradient orientation

• Important types:– constant window

• small gradient mags– edge window

• few large gradient mags in one direction

– flow window• many large gradient mags

in one direction– corner window

• large gradient mags that swing

Representing Windows

• Types– constant

• small eigenvalues– Edge

• one medium, one small– Flow

• one large, one small– corner

• two large eigenvalues

H I I Twindow

Harris Corner detector• Le regioni uniformi hanno tutti I gradienti uniformi• I bordi lineari hanno tutti i gradienti allineati• gli angoli sono I punti di congiunzione di due bordi e

quindi avra` I gradienti attorno allineati lungo due direttrici fondamentali

Harris Corner detector

• Tutti e due gli auovalori di H sono > 0• Rilevatore di angoli Harris: gli angoli sono massimi locali

della funzione R=det(H)-kTr(H)2

Edge Linking

Hough Transform

Metodi graph-based

Normalized Cut

AassoAcut

AassoAcutANcut

)()()(

gradients and edges

large derivative

significant points

relevant points

smallflowone large

image gradientnow mark

edge curves

large wrt neighbours

edge pointsgeneral

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