Edge Detection Evaluation in Boundary Detection Framework Feng Ge Computer Science and Engineering, USC

Edge detection Detect pixels with strong gradient of “gray-level” Error – False negative(Missing ): Not detected Edges – False positive: detected false edges – Orientation error: shift from real position – Dislocation error: shift from real direction How to evaluate these errors? Edge Detection Error

Evaluation Criteria Ground Truth – Human or predefined results? Quantificaition – Measuring and expressing in number means good. Generality – Real images in large number Combined 3 criteria are good evaluation methods!

Overview Subjective vs Objective –H uman vision checking – Quantitative measurement With ground truth vs Without – Standard for evaluation – Some characters,e.g, continuation,coherence. Synthetic vs Real images – Simple structure – Complicated structures

Motive— in boundary detection framework Problem: Boundary detection algorithms work well in synthetic data, while poorly in real images This gap,we believe, is largely introduced by edge detection

Experiment Settings : Image Database Large: 1030 images Generality Unambiguous Manually extracted ground truth

Experiment Settings : Evaluation Flowchart

Experiment Settings : Detectors Edge & Line Detector: Canny & Line Approximation Boundary detector: Ratio-Contour

Experiment Settings : performance measurement

Experiment Original images image->edge->fragments->bounday->evaluation Synthetic images texture images->fragments --->bounday->evaluation ground truth->adding noise Semi-synthetic images original images->background -->bounday->evaluation ground truth->adding noise

Experiment --Synthetic images Result – Much better than original images Problem – Background correlation changed – Irregular background in texture images

Experiment – Semi-synthetic images Model simulation Edge-map error analysis

Result-1 Simulate edge missing Procedure: Sample ground truth, random delete some percentage of fragments

Result-2 Simulate edge detection error: missing & dislocation – Fix dislocation error, vary missing rate (a) – Fix missing error, vary dislocation error (b) (a) (b)

Conclusion Our noise model is close to real edge error, as regarding to the simulated result Edge missing and dislocation are mainly encountered errors in edge detection. Edge dislocation is more crucial in edge error compared with missing error

Discussion-1 Error introduced by line detection

Discussion-2 Model error – Gaussian distribution assumption Based on boundary detection – Globally, not locally – Introduce some error, but statistically, reasonable Image database – Low resolution – Ground truth error

Future work Distinguish errors introduced by line approximation from edge detection Noise model refinement Substitute line with curve in edge-map approximation Data base improvement

Thank You !