Ingo Sieber | Searcharxiv

arXiv2026

MISTac: A Vision-Based Tactile Sensor for Minimally Invasive Surgery

Minimally invasive and robot-assisted surgery offer many advantages over traditional open surgery, but deprive surgeons of tactile feedback and the ability to palpate tissue with their fingers. To address this lack of tactile feedback, we introduce the MISTac, a high resolution vision-based tactile sensor specifically designed for palpation in MIS. The sensor has a replaceable sensor tip with a diameter of 8 mm which allows it to fit through the trocars used in minimally invasive surgery. Its modular 3D-printed case design allows the use of bulky off-the-shelf illumination and imaging hardware that can easily be exchanged and upgraded. The sensor has an optical resolution of 176.68 $\mu m$, a tactile resolution of 250 $\mu m$, and can resolve forces as little as 24.3 mN. An in vivo study with the sensor shows its usability in minimally invasive surgery. We trained a machine learning model with the tactile data collected in the trial on a tissue classification task achieving an aggregate accuracy of ~84% in a leave-one-out cross validation. Tactile sensors have the potential to one day aid surgeons during minimally invasive surgery with tasks such as tissue classification or intra-operative tumor localization; MISTac is a small step towards this vision. We open-source MISTac at https://github.com/lasr-lab/mistac

cs.RO↗

arXiv2025

Classification of Microplastic Particles in Water using Polarized Light Scattering and Machine Learning Methods

The detection and classification of microplastics in water remain a significant challenge due to their diverse properties and the limitations of traditional optical methods. Standard spectroscopic techniques often suffer from the strong infrared absorption of water, while many emerging optical approaches rely on transmission geometries that require sample transparency. This study presents a systematic classification framework utilizing 120 degree backscattering reflection polarimetry and deep learning to identify common polymers (HDPE, LDPE, and PP) directly in water. This backscattering-based approach is specifically designed to analyze opaque, irregularly shaped particles that lack distinguishable surface features under standard illumination. To ensure high-fidelity data, we introduce a feedback review loop to identify and remove outliers, which significantly stabilizes model training and improves generalization. This framework is validated on a dataset of 600 individually imaged microplastic fragments spanning three polymer types. Our results evaluate the distinct contributions of the Angle of Linear Polarization and the Degree of Linear Polarization to the classification process. By implementing a late fusion architecture to combine these signals, we achieve an average test accuracy of 83 percent. Finally, a systematic feature hierarchy analysis reveals that the convolutional neural network relies on internal polarization textures associated with the particle's microstructure, rather than on macro-contours, with classification accuracy declining by over 40 percent when internal structure is removed. This demonstrates that the system extracts polarization-dependent internal structural information that is inaccessible to conventional intensity-only imaging methods.

cs.CV↗