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Image Feature Detection Techniques for Decision-Making

Learn how decisions on image features like edges, corners, and junctions are made based on measurements, thresholds, and orientations, enhancing image analysis accuracy. Explore methods for identifying edgels, angles, and connecting edgels using specific algorithms. Understand the significance of threshold parameters and estimation techniques in image processing.

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Image Feature Detection Techniques for Decision-Making

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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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