Edge Detection Evaluation in Boundary Detection Framework Feng

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

Edge Detection Error • 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?

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 – Human 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 • Edge-map error analysis • Model simulation

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 !