Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Nov 4, 2023 ·

This article presents a new approach to detecting and accurately locating anomalous objects in images. The proposed method is based on a supervised machine learning model, which uses a convolutional neural network (CNN) for anomaly detection and an image registration algorithm for precise localization of anomalies.

The authors propose the use of synthetic data generated by deep generative models such as Generative Adversarial Networks (GANs). This synthetic data is used to train the CNN model, with the aim of achieving improved detection performance. Furthermore, the authors employ an image registration algorithm that is utilized to precisely localize the detected anomalies.

To evaluate their system, the authors conduct experiments on a publicly available dataset consisting of images from satellite imagery and medical imaging datasets. They compare the proposed method to state-of-the-art anomaly detectors and demonstrate that their method achieves better performance in terms of both accuracy and F-score.

In conclusion, this article introduces a new method for detecting and precisely locating anomalies in images which is based on a supervised machine learning model. The utilization of synthetic data generated by GANs ensures improved detection performance. Moreover, the proposed image registration algorithm enables the precise localization of anomalies. Experiments conducted on public datasets show that the proposed method outperforms existing methods.