Consecutive Image Acquisition without Anomalies

Abstract

An image is a visual representation that can be used to obtain information. A camera on a moving vector (e.g., on a rover, drone, quad, etc.) may acquire images along a controlled trajectory. The maximum visual information is captured during a fixed acquisition time when consecutive images do not overlap and have no space (or gap) between them. The images acquisition is said to be anomalous when two consecutive images overlap (overlap anomaly) or have a gap between them (gap anomaly). In this article, we report a new algorithm, named OVERGAP, that remove these two types of anomalies when consecutive images are obtained from an on-board camera on a moving vector. Anomaly detection and correction use here both the Dynamic Time Warping distance and Wasserstein distance. The proposed algorithm produces consecutive, anomaly-free images with the desired size that can conveniently be used in a machine learning process (mainly Deep Learning) to create a prediction model for a feature of interest.

Keywords

• anomaly detection
• anomaly correction
• optical measurement
• dynamic time warping distance
• Wasserstein distance