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Feature Detection

Feature Detection. Feature Detection. Image Features –Decisions!. Features such as edges, corners, junction, eyes, … are obtained by making some decision from the image measurements.

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Feature Detection

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  1. Feature Detection Feature Detection

  2. 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.

  3. Decisions: Edgels (Edge-pixels and Orientation) Edge threshold: Decision! Edge orientation: Decision! Eliminate some spurious locations. Decision! Strength of the Edgel The gray level indicates the angle: the darkest one is 0 degrees. The larger is the angle the lighter is displayed, up to 5p/6.

  4. Decisions: Local Angle Change Angle change where is the contour curvature multiplied by the arc length , where A contour segment y x

  5. Decisions: Junctions, Corners Junction threshold: Decision! Remove (Undo) detection if Eliminate spurious locations. Decisions! Examining the values of q where allow us to characterize the junctions. For example, when only two value of q 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 p, 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.

  6. qmax (xc , yc) 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 ,qmax) Link-neighbors-(xc , yc ,qmax) end Link-neighbors±(xc , yc ,qmax) xn±= xc ± xqmaxcosqmax ; yn±= yc ±  yqmaxsinqmax ; if (Edgel(xn±,yn±)  NIL ) Link((xc , yc), (xn±,yn±)) Link-neighbors±( xn±,yn±,qmax(xn±,yn±) ) end

  7. Threshold Parameters: Estimation We have considered at least three parameters: How to estimate them? One technique is Histogram partition: Plot the Histogram and find the parameter that “best partition it”:

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