Summary of A Novel Representation Of Periodic Pattern and Its Application to Untrained Anomaly Detection, by Peng Ye et al.
A Novel Representation of Periodic Pattern and Its Application to Untrained Anomaly Detection
by Peng Ye, Chengyu Tao, Juan Du
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to quality inspection of industrial products with periodic textures or surfaces, such as carbon fiber textiles and display panels. The traditional image-based methods require identifying periodic patterns in normal images and detecting anomalies with inconsistent appearances. However, this task remains challenging in the presence of unknown anomalies and measurement noise. To address this challenge, the authors propose a self-representation of the periodic image defined on continuous parameters, which is embedded into a joint optimization framework called periodic-sparse decomposition. This approach simultaneously models sparse anomalies and Gaussian noise. Additionally, the paper proposes a novel pixel-level anomaly scoring strategy to enhance performance in real-world scenarios that may not strictly satisfy the periodic assumption. The proposed methodology is demonstrated to be effective for periodic pattern learning and anomaly detection through both simulated and real-world case studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better inspect industrial products with special textures, like carbon fiber or screens. Right now, we use images to check these products, but it’s hard when there are weird spots or noise in the image. The authors came up with a new way to represent these periodic patterns using continuous parameters. This lets them learn about the patterns and also figure out what’s unusual. They even have a special scoring system for detecting anomalies. The results show that their method works well for both fake and real-world examples. |
Keywords
* Artificial intelligence * Anomaly detection * Optimization
