E. Ruiz

Alexandra Malyugina, Guoxi Huang, Eduardo Ruiz et al.

Underwater videos often suffer from degraded quality due to light absorption, scattering, and various noise sources. Among these, marine snow, which is suspended organic particles appearing as bright spots or noise, significantly impacts machine vision tasks, particularly those involving feature matching. Existing methods for removing marine snow are ineffective due to the lack of paired training data. To address this challenge, this paper proposes a novel enhancement framework that introduces a new approach for generating paired datasets from raw underwater videos. The resulting dataset consists of paired images of generated snowy and snow, free underwater videos, enabling supervised training for video enhancement. We describe the dataset creation process, highlight its key characteristics, and demonstrate its effectiveness in enhancing underwater image restoration in the absence of ground truth.

Eduardo Ruiz, Walterio Mayol-Cuevas

In this paper we explore state-of-the-art underactuated, compliant robot gripper designs through looking at their performance on a generic grasping task. Starting from a state of the art open gripper design, we propose design modifications,and importantly, evaluate all designs on a grasping experiment involving a selection of objects resulting in 3600 object-gripper interactions. Interested in non-planned grasping but rather on a design's generic performance, we explore the influence of object shape, pose and orientation relative to the gripper and its finger number and configuration. Using open-loop grasps we achieved up to 75% success rate over our trials. The results indicate and support that under motion constraints and uncertainties and without involving grasp planning, a 2-fingered underactuated compliant hand outperforms higher multi-fingered configurations. To our knowledge this is the first extended objective comparison of various multi-fingered underactuated hand designs under generic grasping conditions.