Keypoint-Guided Ophidian Transformation for String-Shaped Data Augmentation

This paper introduces KGOT (Keypoint-Guided Ophidian Transformation), a novel image augmentation technique designed to generate realistic, non-linear transformations of string-shaped objects. KGOT is particularly effective for snake images and other similar string-shaped objects where traditional augmentation methods prove inadequate.

The full paper is currently under review and not yet available for public access. Once accepted and published, more details about the methodology, experiments, and results will be provided here.

Key points:

• Addresses the challenge of limited datasets in image classification tasks
• Introduces a new technique for non-linear transformations of string-shaped objects
• Demonstrates improved classification accuracy compared to traditional augmentation methods
• Potential applications in snake species identification and other string-shaped object classification tasks

For more information about this research, please check out in our github repository here.