Edge detection

Computer Vision Set: Edge detection

Slides by D.A. Forsyth, C.F. Olson, S.M. Seitz, L.G. Shapiro

2Computer Vision Set: Edge detection

Image edges

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

• Sometimes called edge points or edge pixels• We want to find the edges generated by scene elements

and not by noise

Edge detection

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

Computer Vision Set: Edge detection

Origin of edges

• Edges are caused by a variety of factors

depth discontinuity

surface color discontinuity

illumination discontinuity

surface normal discontinuity

Edge detection

How can you tell whether a pixel is on an edge?

Edge detection

• Basic idea: look for a neighborhood with lots of change

81 82 26 2482 33 25 2581 82 26 24

Questions:

• What is the best neighborhood size?

• How should change be detected?

Finding edges

• General strategy:– Determine image gradients after smoothing

(gradients are directional derivatives computed using finite differences)

– Mark points where the gradient magnitude is large with respect to neighboring points

– Ideally this yields curves of edge points.

Image gradients

• We use the image gradient to determine whether a pixel is an edge.– Two components: [gx, gy]– Both components use finite differencing to approximate

derivatives– Gradients have magnitude and orientation– Vertical edges respond strongly to the x component– Horizontal edges respond strongly to the y component– Diagonal edges will respond less strongly, but to both components

• Overall magnitude should be the same

Sobel operator

• The Sobel operator is a simple example that is common.

-1 0 1 1 2 1Sx = -2 0 2 Sy = 0 0 0 -1 0 1 -1 -2 -1

On a pixel of the image I• let gx be the response to Sx• let gy be the response to Sy

g = (gx + gy ) is the gradient magnitude. = atan2(gy,gx) is the gradient direction.

2 2 1/2

Then the gradient is I = [gx gy]T

Smoothing and differentiation• Issue: noise

– Need to smooth image before determining image gradients– Should we perform two convolutions (smooth, then differentiate)?– Not necessarily: we can use a derivative of Gaussian filter

• Differentiation is convolution and convolution is associative• D * (G * I) = (D * G) * I – What are D, G, and I?

Gaussian Gaussian derivative in x Plot of Gaussian derivative

Smoothing and differentiation• Shape of Gaussian derivative:

– Light on one side (positive values)– Dark on other side (negative values)– Values fall off from horizontal center line– After initial peaks, values fall off from vertical center line

Gaussian derivative in x Plot of Gaussian derivative

Smoothing and differentiation• Important implementation trick – we don’t need to

convolve by a 2D kernel• A 2D Gaussian function is “separable”

– Gσ(x, y) = Gσ(x) * Gσ(y) • This means we can convolve the image with two 1D

functions (rather than one 2D function)• This results in considerable savings for an n x n image and

k x k kernel:– 1 2D kernel: approximately n2k2 multiplications and additions– 2 1D kernels: approximately 2n2k

• The gradient operator is convolved with the appropriate 1D kernel or applied in succession

As the scale (sigma) increases, finer features are lost, but diffuse edges are gained.

Note that the gradient magnitude encompasses horizontal, vertical, and diagonal edges.

Gradient magnitudes after smoothing

Original Sigma = 1 Sigma = 5

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 points up into curves?

Gradient magnitudes after smoothing

Original Sigma = 1 Sigma = 5

We wish to mark points along the curve where the gradient magnitude is largest.We can do this by looking for a maximum along a slice along the gradient direction. These points should form a curve. There are two algorithmic issues: at which point is the maximum, and where is the next one along the curve?

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.

Non-maxima suppression

• At q, the gradient Gq is a vector perpendicular to the edge direction

• The locations p and r are one pixel in the direction of the gradient and the opposite direction.

• One pixel in the gradient direction is g = [Gx/Gmag, Gy/Gmag]

• Recall that Gmag is the length of the gradient vector [Gx, Gy]

• r = q + g, p = q - g

Non-maxima suppression

• At p and r, the gradient magnitude should be interpolated from the surrounding four pixels.

• If the gradient magnitude at q is larger than the interpolated value at p and r, then q is marked as an edge

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).

Only necessary if following edges.

Remaining issues

• Must check that the gradient magnitude is sufficiently large.

• A common problem is that at some points along the curve the gradient magnitude will drop below the threshold, but not at others.– Use hysteresis: a high threshold to start edge curves

and a lower threshold to continue them.• Performance at corners is poor.

Canny edge detector• The Canny edge detector (1986) is still used most often in

practice. It is essentially what we have discussed:– Smooth and differentiate the image using derivative of Gaussian

filters in x and y– Detect initial candidates by thresholding the gradient magnitude– Apply non-maxima suppression at the candidates– Aggregate edge pixels into contours by following edges

perpendicular to the gradient – When aggregating, allow contour gradient magnitude to fall below

initial threshold, but must remain above lower threshold• Note that this detector (and others) is sensitive to the

parameters used (sigma, thresholds)

Zero-crossing detectors

Edge detection using the zero-crossing of the 2nd derivative is historically important.

Performance at corners is poor, but zero-crossings always form closed contours.

step edgesmoothed

1st derivative

2nd derivativezero crossing

originalimage

fine scale(sigma=1),medium threshold,no hysteresis

Much detail (and noise) that disappears at coarser scales

coarse scale(sigma=4),high threshold,no hysteresis

Curves are often broken, not closed contours

coarse scale(sigma=4),low threshold,no hysteresis

Additional edges found are questionable.

Edge detection

edge detectionslides

shapirocomputer vision

pixels computer vision

gx gytcomputer vision

d image

yields curves of edge

gradient magnitude

smooth image

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