Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework

Understand

In this paper, we propose an efficient approach for industrial defect detection that is modeled based on anomaly detection using point pattern data.

• Most recent works use \textit{global features} for feature extraction to summarize image content.
• However, global features are not robust against lighting and viewpoint changes and do not describe the image's geometrical information to be fully utilized in the manufacturing industry.
• To the best of our knowledge, we are the first to propose using transfer learning of local/point pattern features to overcome these limitations and capture geometrical information of the image regions.