SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Fabric defect detection using modified local binary patterns
EURASIP Journal on Advances in Signal Processing
ACM Computing Surveys (CSUR)
HMM-Based Defect Localization in Wire Ropes --- A New Approach to Unusual Subsequence Recognition
Proceedings of the 31st DAGM Symposium on Pattern Recognition
An analysis-by-synthesis approach to rope condition monitoring
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
One-class classification with Gaussian processes
Pattern Recognition
| Hi-index | 0.00 |
We present a new approach for anomaly detection in the context of visual surface inspection. In contrast to existing, purely appearance-based approaches, we explicitly integrate information about the object geometry. The method is tested using the example of wire rope inspection as this is a very challenging problem. A perfectly regular 3d model of the rope is aligned with a sequence of 2d rope images to establish a direct connection between object geometry and observed rope appearance. The surface appearance can be physically explained by the rendering equation.Without a need for knowledge about the illumination setting or the reflectance properties of the material we are able to sample the rendering equation. This results in a probabilistic appearance model. The density serves as description for normal surface variations and allows a robust localization of rope surface defects. We evaluate our approach on real-world data from real ropeways. The accuracy of our approach is comparable to that of a human expert and outperforms all other existing approaches. It has an accuracy of 95% and a low false-alarm-rate of 1.5%, whereupon no single defect is missed.