1. © 2002 by Davi GeigerComputer Vision September 2002 L1.1 Feature Detection

2. © 2002 by Davi GeigerComputer Vision September 2002 L1.2 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. © 2002 by Davi GeigerComputer Vision September 2002 L1.3 Decisions: Edgels (Edge-pixels and Orientation) Edge threshold: Decision! Edge orientation: Decision! A step edge at  The value is (equally) large for both  and  as shown (in red) for scale s=3 pixels. It is also large for values  not shown  However, the quantity is significantly larger for 

4. © 2002 by Davi GeigerComputer Vision September 2002 L1.4 The gray level indicates the angle: the darkest one is 0 degrees. The larger is the angle the lighter is displayed, up to  Strength of the Edgel Decisions: Edgels (cont.) Eliminate some spurious locations. Decision!

5. © 2002 by Davi GeigerComputer Vision September 2002 L1.5 Decisions: Local Angle Change Angle change y x A contour segment is the contour curvature multiplied by the arc length, where

6. © 2002 by Davi GeigerComputer Vision September 2002 L1.6 Decisions: Junctions, Corners Junction threshold: Decision! Eliminate spurious locations. Decisions! Examining the values of  where allow us to characterize the junctions. For example, when only two value of  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  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. Remove (Undo) detection if

7. © 2002 by Davi GeigerComputer Vision September 2002 L1.7 Decisions: Connecting Edgels, Pseudocode Algorithm to link edgels. Start with a seed location (x c, y c ) Contour-Follower( x c, y c ) if (Edgel (x c, y c )  NIL ) Link-neighbors+( x c, y c,  max ) Link-neighbors-( x c, y c,  max ) end Link-neighbors±( x c, y c,  max ) x n ± = x c ±  x  max cos  max ; y n ± = y c ±  y  max sin  max ; if (Edgel (x n ±, y n ± )  NIL ) Link ((x c, y c ), (x n ±, y n ± )) Link-neighbors± ( x n ±, y n ±,  max (x n ±, y n ± ) ) end  max ( x c, y c )

8. © 2002 by Davi GeigerComputer Vision September 2002 L1.8 Decisions: Connecting Edgels, Pseudocode Link-neighbors±( x c, y c,  max-c ) x n ± = x c ±  x  max cos  max ; y n ± = y c ±  y  max sin  max ; if ((Edgel (x n ±, y n ± )  NIL )&(Coherence(x c, y c x n ±, y n ± ))) Link ((x c, y c ), (x n ±, y n ± )) Link-neighbors± ( x n ±, y n ±,  max-n (x n ±, y n ± ) ) end Coherence( x c, y c x n, y n ) if ( and ) return True else Nil end

9. © 2002 by Davi GeigerComputer Vision September 2002 L1.9 Threshold Parameters: Estimation We have considered at least four parameters: How to estimate them? One technique is Histogram partition: Plot the Histogram and find the parameter that “best partition it”: