RAISE-Vision: An Automated Contact Angle Measurement Platform for Contact Lens Wettability Characterization
Authors: Erin Ng, Mohammad Nazeri, Julie S Liu, Tithi Choksi, Lyndon Jones, Frank Gu
Organizations: Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada · Institute of Biomedical Engineering (BME), University of Toronto, Toronto, Canada · Department of Chemical Engineering, McMaster University, Hamilton, Canada · Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Canada · School of Optometry and Vision Science, University of Waterloo, Waterloo, Canada · Centre for Ocular Research and Education (CORE), University of Waterloo, Waterloo, Canada · Acceleration Consortium, University of Toronto, Toronto, Canada
Contact angle (CA) is a common parameter measured in the development of novel contact lens materials and surface coating technologies. Existing literature on in vitro CA of contact lenses via the sessile drop method is hindered in repeatability, reliability, and comparability due to inter-laboratory and inter-operator protocol variation. In this work, the Robotic Autonomous Imaging Surface Evaluator for vision applications (RAISE-Vision) is introduced as an automated platform for CA measurement on curved hydrogel surfaces, with specific application to in vitro contact lens wettability analysis throughout air exposure. Customized 3D printed hardware, such as lens mounts, camera holders and drying apparatuses, was designed to facilitate an automated repeated sessile drop workflow within a liquid handler. Collected images were directly fed to a custom open-source image processing script for CA determination on a curved surface that combines the elliptical fit and tangent methods to allow for variations in droplet shape. The CA of senofilcon A contact lenses measured using the RAISE-Vision platform increased from 62.6±5.5∘ to 83.4±2.1∘ over 10 sequential measurements in air and was able to replicate results obtained using an analogous manual protocol (p=0.470). The custom image processing script was also separately demonstrated to generate comparable results to a commercially available CA determination software (p=0.343). Aside from minimizing operator variability for CA measurement, RAISE-Vision provides a platform for standardized in vitro contact lens wettability analysis and understanding of surface wetting behaviour during air exposure.
This paper presents a contact-based 3-D surface measurement method based on a Digital Fringe Projection (DFP) system, belonging to the vision-based tactile sensing family pioneered by the commercially successful GelSight sensor. Such sensors have proven effective for robotic fingertip manipulation and contact sensing. However, because GelSight employs photometric stereo with RGB LEDs, it does not measure absolute depth directly but instead infers it by integrating estimated surface gradients, which can accumulate reconstruction errors; in addition, it becomes increasingly difficult to calibrate as the sensing area grows, and its depth accuracy is challenged on highly reflective or transparent objects. To overcome these drawbacks, we propose a fringe-projection-based contact measurement technique that performs triangulation-based 3-D reconstruction on a coated silicone contact surface, providing dense per-pixel surface geometry and full-field 3-D shape measurement over the contact region. By integrating high-accuracy digital fringe projection into the sensor, our approach simplifies calibration over larger areas and enhances depth precision for complex surfaces. Experimental results, including a direct comparison with a GelSight Mini sensor, a sphere-fitting accuracy evaluation, and an uncertainty analysis, confirm that the proposed method significantly improves the accuracy and stability of structured-light-based 3-D measurements, allowing reliable reconstruction of objects with diverse optical properties.
Ingu Yeo, Hyung-Gun Chi, Jae-Sang Hyun
Department of Mechanical Engineering and the Yonsei Institute for Embodied Intelligence, Yonsei University, Seoul 03722, South Korea
A low-impedance robot finger reports through joint torque how strongly it is loaded, but the same torque can arise from a small force near the fingertip or a large force near the joint. Resolving the force therefore requires knowing where along the finger contact occurred. LUMO makes that location externally observable. Embedded LEDs illuminate a compliant silicone pad, and contact deforms the pad so that light emerging from the finger's side changes in a pattern set by where the load acts. Because the same structure also carries the contact load, we optimize its cross-section, including the pad profile, rigid carrier, and lateral void, for two behaviors at once. Mechanically, the pad conforms under low preload while the carrier increasingly restricts further deformation as load rises. Optically, different contact locations produce separated responses on the finger's side. The search uses rigid--soft contact simulation, ray tracing, and multi-objective Bayesian optimization. Across two silicones, six contact locations, and 10- and 30-mm spherical indenters, the optimized morphologies improve neighboring-location separation relative to variation from re-establishing contact by 15--59%. Estimating contact location from the optical response using the known LED spacing and combining it with joint torque gives 1.44N normal-force MAE over 931 samples. In a two-finger hand, localized side responses appear on several links simultaneously during grasps.
Dong Ho Kang, Youngsu Ko, Luis Sentis
The University of Texas at Austin, Austin, TX, USA
This work focuses on the impact and detection of clear contact lenses in the context of iris recognition. While the detection of cosmetic or patterned contact lenses has been extensively studied under the presentation attack detection (PAD) paradigm, clear prescription contact lenses, that are typically transparent, have received comparatively less attention despite their widespread use. Unlike patterned lenses, clear lenses introduce no salient texture artifact, making them difficult to detect and are often assumed to have no impact on iris recognition. We first examine this assumption using the commercial VeriEye matcher on four benchmark datasets and show that clear lenses marginally degrade genuine match scores and increase verification error. We then propose a two-stage contact-lens detection framework. Stage1 uses an existing PAD model to identify patterned lenses, while Stage2 focuses on the more challenging clear-lens versus no-lens distinction using a ConvNeXt-Base model equipped with Mask-Guided Spatial Attention (MGSA). The proposed MGSA module incorporates a Hough-derived anatomical ROI mask together with learned spatial attention and Squeeze-and-Excitation channel recalibration, allowing the network to focus on subtle limbal cues associated with clear lens wear. Across four datasets, the full pipeline consisting of both patterned and clear contact lens detection achieves between 90.0%--98.8% accuracy. Finally, we introduce a z-score calibration method that adjusts VeriEye match scores when a clear lens is detected in the input images. This calibration reduces EER by 4.1%--28.3% across datasets, demonstrating that reliable clear contact lens detection can directly improve iris verification performance.