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Instructor: Mircea Nicolescu Lecture 6 CS 485 / 685 Computer Vision

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Edge Detection Using Derivatives Points that lie on an edge can be found by: (1) Detecting the local maxima or minima of the first derivative. (2) Detecting the zero-crossings of the second derivative. 2 nd derivative 1 st derivative 2

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Image Derivatives How can we differentiate a digital image? −Option 1: reconstruct a continuous image, f(x,y), then compute the derivative. −Option 2: take discrete derivative (i.e., finite differences) Consider this case first! 3

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Edge Detection Using First Derivative ramp edge roof edge (upward) step edge (downward) step edge (centered at x) 1D functions (not centered at x) 4

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Edge Detection Using Second Derivative Approximate finding maxima/minima of first derivative by finding places where: Can’t always find discrete pixels where the second derivative is zero – look for zero-crossing instead. 5

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Replace x+1 with x (centered at x): 1D functions: (centered at x+1) Edge Detection Using Second Derivative 6

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(upward) step edge (downward) step edge ramp edge roof edge Edge Detection Using Second Derivative 8

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Effect of Smoothing on Derivatives Where is the edge? 9

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Effect of Smoothing on Derivatives 10

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Combine Smoothing with Differentiation (saves one operation) 11

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Mathematical interpretation: Numerical differentiation is an ill-posed problem - solution does not exist or it is not unique or it does not depend continuously on initial data Ill-posed problems can be solved using “regularization” - impose additional constraints Smoothing performs image interpolation Combine Smoothing with Differentiation 12

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13 Edge Detection Using the Gradient The first derivative of an image can be computed using the gradient: To save computations, the magnitude of gradient could be approximated by: 2D functions

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14 Edge Detection Using the Gradient Properties of the gradient: −The magnitude of gradient provides information about the strength of the edge −The direction of gradient is always perpendicular to the direction of the edge Main idea: −Compute derivatives in x and y directions −Find gradient magnitude −Threshold gradient magnitude

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Approximate gradient using finite differences: Approximate Gradient 15 hxhx

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Cartesian vs pixel-coordinates: - j corresponds to x direction - i to -y direction Approximate Gradient 16 i j

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sensitive to vertical edges! sensitive to horizontal edges! Approximate Gradient 17

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We can implement f/ x and f/ y using the following masks: (x+1/2,y) (x,y+1/2) * * approximation at (x+1/2,y) approximation at (x,y+1/2) Approximate Gradient 18

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A different approximation of the gradient: f/ x and f/ y can be implemented using the masks: * (x+1/2,y+1/2) approximation at Approximate Gradient Roberts edge detector 19

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20 The Prewitt edge detector −The partial derivatives can be computed by: Note: M x and M y are approximations at (i, j)) −Consider the arrangement of pixels about the pixel (i, j): −The constant c implies the emphasis given to pixels closer to the center of the mask. −Setting c = 1, we get the Prewitt operator: Approximate Gradient

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21 The Sobel edge detector Note: M x and M y are approximations at (i, j)) −Setting c = 2, we get the Sobel operator: Approximate Gradient

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22 Edge Detection Using the Gradient Main steps in edge detection using masks:

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23 Edge Detection Using the Gradient (an example using the Prewitt edge detector) Note: in this example, the divisions by 2 and 3 in the computation of f x and f y are done for normalization purposes only

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24 Edge Detection Using the Gradient Another example:

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25 Edge Detection Using the Gradient Example – cont.:

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26 Edge Detection Using the Gradient Example – cont.:

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Isotropic Property of Gradient Magnitude The gradient magnitude detects edges in all directions. 27

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28 Practical Issues −Differential masks act as high-pass filters – tend to amplify noise. −To reduce the effects of noise – first smooth with a low-pass filter. 1)The noise suppression tradeoff −A larger filter reduces noise, but worsens localization (i.e., it adds uncertainty to the location of the edge) and vice-versa.

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29 Practical Issues 2)Choice of threshold gradient magnitude low thresholdhigh threshold

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30 Practical Issues 3)Edge thinning and linking −required to obtain good contours

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31 Criteria for Optimal Edge Detection −Good detection: the optimal detector must minimize the probability of false positives (detecting spurious edges caused by noise), as well as that of false negatives (missing real edges) −Good localization: the edges detected must be as close as possible to the true edges. −Single response constraint: the detector must return one point only for each true edge point; that is, minimize the number of local maxima around the true edge

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32 The Canny Edge Detector −This is probably the most widely used edge detector in computer vision. −Canny has shown that the first derivative of the Gaussian closely approximates the operator that optimizes the product of signal-to- noise ratio and localization. −Analysis is based on "step-edges" corrupted by "additive Gaussian noise". The Canny edge detector:

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33 The Canny Edge Detector

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34 The Canny Edge Detector The derivative of the Gaussian:

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35 The Canny Edge Detector Canny – smoothing and derivatives:

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36 The Canny Edge Detector Canny – gradient magnitude: imagegradient magnitude

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37 The Canny Edge Detector −To find the edge points, we need to find the local maxima of the gradient magnitude. −Broad ridges must be thinned so that only the magnitudes at the points of greatest local change remain. −All values along the direction of the gradient that are not peak values of a ridge are suppressed. Non-maxima suppression

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38 The Canny Edge Detector Non-maxima suppression – cont. (i,j) Warning: may require checking interpolated pixels p and r

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39 The Canny Edge Detector Canny – Non-maxima suppression: gradient magnitudethinned

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40 The Canny Edge Detector Hysteresis thresholding / Edge linking Standard thresholding: - Can only select “strong” edges - Does not guarantee continuity gradient magnitudelow thresholdhigh threshold

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The Canny Edge Detector Use two thresholds: For “maybe” edges, use neighboring pixels to decide - low threshold t l - high threshold t h (usually, t h = 2t l ) t l ≤ tltl thth thth Hysteresis thresholding / Edge linking – cont. 41

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Idea: use a high threshold to start edge curves and a low threshold to continue them. Use edge direction for linking edges The Canny Edge Detector Hysteresis thresholding / Edge linking – cont. 42

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43 The Canny Edge Detector Hysteresis thresholding / Edge linking – cont. −The algorithm performs edge linking as a by-product of double- thresholding −Note: large gaps are still difficult to bridge (more sophisticated algorithms are required)

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44 The Canny Edge Detector Canny – Hysteresis thresholding / Edge linking thinned

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Edge detection Goal: Identify sudden changes (discontinuities) in an image Intuitively, most semantic and shape information from the image can be encoded.

Edge detection Goal: Identify sudden changes (discontinuities) in an image Intuitively, most semantic and shape information from the image can be encoded.

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