Keypoint-Guided Ophidian Transformation for String-Shaped Data Augmentation

Image classification tasks in scientific research often struggle with limited data, particularly when few images are available per class, necessitating effective data augmentation strategies. For snakes and other string-shaped objects, traditional augmentation methods such as flipping, adding noise, or altering colour prove inadequate in enhancing classification accuracy. 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 first straightens the snake image, then warps it according to a reference curve defined by key points, effectively inverting the straightening process. By utilizing advanced curve interpolation, pixel mapping, and post-processing techniques, KGOT ensures smooth, biologically plausible deformations while preserving important individual-specific features. Our experiments demonstrate that KGOT-augmented data increase accuracy compared to the traditional augmentation method using geographic transformation techniques. Although some limitations persist in scenarios where snake bodies overlap, the success of KGOT augmentation provides valuable information on factors that improve classification performance. This approach holds promise not only for snake individual identification but also for a broader range of applications involving string-shaped object classification tasks.