Seeing Colours: Addressing Colour Vision Deficiency with Vision Augmentations using Computational Glasses

Colour vision deficiency is a common visual impairment that cannot be compensated for using optical lenses in traditional glasses, and currently remains untreatable. In our work, we report on research on Computational Glasses for compensating colour vision deficiency. While existing research only showed corrected images within the periphery or as an indirect aid, Computational Glasses build on modified standard optical see-through head-mounted displays and directly modulate the user’s vision, consequently adapting their perception of colours. In this work, we present an exhaustive literature review of colour vision deficiency compensation and subsequent findings; several prototypes with varying advantages—from well-controlled bench prototypes to less controlled but higher application portable prototypes; and a series of studies evaluating our approach starting with proving its efficacy, comparing to the state-of-the-art, and extending beyond static lab prototypes looking at real world applicability. Finally, we evaluated directions for future compensation methods for computational glasses.

Optimization of Nano-Topography Distribution by Compensation Grinding

Optical surfaces are required to have high form accuracy and smoothness. The form accuracy must be below 50 nm. Form accuracy is currently on the order of several tens of nanometers or less; however, further improvement is required. To improve form accuracy, compensation grinding is performed based on form measurement results. However, when the form error is small, a small periodical waviness occurs on the ground surface, which is known as nano-topography. This waviness cannot be compensated for using conventional compensation methods because the nano-topography distributions are not reproducible. A previous study showed that grinding conditions affect the spatial frequency of nano-topography. Therefore, in this study, optimum grinding conditions are estimated from the view point of nano-topography distributions, and the grinding conditions are compensated to optimize these distributions.

Strategy to Decrease the Angle Measurement Error Introduced by the Use of Circular Grating in Dynamic Torque Calibration

A circular grating angle encoder is a key component in the dynamic torque calibration system. To improve the accuracy of an angle measurement, in this paper, the source of the angle measurement error of the circular grating is analyzed; an eccentricity error model and an inclination error model are proposed, respectively; further, these two models are combined to establish a total error model. Through the simulation study with the models, the conditions, in which the eccentricity error or inclination error can be ignored, are discussed. The calibration and compensation methods of the angle measurement error are given, and a progressive error compensation function which integrates the first harmonic fitting and the second harmonic fitting is obtained. An experiment is performed to verify the proposed calibration and compensation methods. The peak-to-peak value of the compensated angle measurement error of the single reading head can be reduced by about 93.76%, which approximates to the error of the mean value of the double reading heads. The experimental results show that the error calibration and compensation method based on the proposed error model can effectively compensate the angle measurement error of the circular grating with a single reading head, and obtain a high-precision measurement angle.

Hindfoot motion analysis by subtalar compensation and ankle osteoarthritis stage using a multi-segment foot model

Background The biomechanics of the hindfoot in ankle osteoarthritis (OA) are not yet fully understood. Here we aimed to identify hindfoot motion in a gait analysis using a multi-segment foot model (MFM) according to ankle OA stage or hindfoot alignment by the presence of subtalar compensation. Methods We retrospectively reviewed the medical records, simple radiographs, and gait MFM data of 54 ankles admitted to our hospital for the treatment of advanced ankle OA. Spatiotemporal gait parameters and three-dimensional motions of the hindfoot segment were analyzed according to sex, age, body mass index, Takakura classification, and presence of subtalar compensation. Results No spatiotemporal gait parameters differed significantly according to the presence of subtalar compensation or ankle OA stage. Only normalized step width differed significantly (P = .028). Average hindfoot motion (decompensation versus compensation) did not differ significantly between the sagittal and transverse planes. Graphing of the coronal movement of the hindfoot revealed collapsed curves in both groups that differed significantly. Compared with Takakura stages 3a, 3b, and 4, cases of more advanced stage 3b had a smaller sagittal range of motion than those of stage 3a (P = .028). Coronal movement of the hindfoot in cases of Takakura stage 3a/3b/4 showed a relatively flat pattern. Conclusions The spatiotemporal parameters were not affected by the alignment state of the heel resulting from subtalar compensation. The sagittal range of hindfoot motion decreased in patients with advanced ankle OA. Once disrupted, the coronal movement of the subtalar joint in ankle OA did not change regardless of ankle OA stage or hindfoot compensation state.

Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes

For the success of video deblurring, it is essential to utilize information from neighboring frames. Most state-of-the-art video deblurring methods adopt motion compensation between video frames to aggregate information from multiple frames that can help deblur a target frame. However, the motion compensation methods adopted by previous deblurring methods are not blur-invariant, and consequently, their accuracy is limited for blurry frames with different blur amounts. To alleviate this problem, we propose two novel approaches to deblur videos by effectively aggregating information from multiple video frames. First, we present blur-invariant motion estimation learning to improve motion estimation accuracy between blurry frames. Second, for motion compensation, instead of aligning frames by warping with estimated motions, we use a pixel volume that contains candidate sharp pixels to resolve motion estimation errors. We combine these two processes to propose an effective recurrent video deblurring network that fully exploits deblurred previous frames. Experiments show that our method achieves the state-of-the-art performance both quantitatively and qualitatively compared to recent methods that use deep learning.

Effective velocities artifacts caused by floating datum and compensation methods

Seismic data processing from a floating datum is accompanied by difficulties in estimating effective velocities. These difficulties are associated with the roughness of the datum surface, which, if ignored, leads to artifacts in the estimated effective velocities. The study presents the results of a quantitative analysis of the distortion of effective velocities with model data, as well as the technique to minimize the distorting effect of the elevation on effective velocities. The essence of the method is bringing the sources and receivers within one CDP to a local constant level. This approach has been tested on modeled and real data. It showed a significant reduction in the effect of floating level roughness on kinematic parameters. At the same time, there is no need to modify the processing flow whatsoever.

Preferences in Farmland Eco-Compensation Methods: A Case Study of Wuhan, China

Successful farmland eco-compensation projects need to reflect the heterogeneous preferences both from suppliers and beneficiaries. This paper tries to answer this question by investigating both citizen and farmer preferences for different farmland eco-compensation methods in Wuhan, China, and explore some of the socio-demographic characteristics that contribute to their preferences. Based on the data of 288 citizens and 331 farmers, the multinomial logit model was employed to analyze their preferences for the four farmland eco-compensation methods (monetary compensation, in-kind compensation, technology compensation and policy compensation), respectively. The results show that: (1) Monetary compensation is the most welcomed farmland eco-compensation method among both citizens and farmers. (2) Despite farmers and citizens both putting a high value on monetary compensation methods, citizens are more likely to provide compensation methods that can help farmers improve their living standards in a sustainable method (in-kind compensation, technology compensation and policy compensation). Farmers are less likely to choose the in-kind compensation method. (3) The preference for farmland eco-compensation systems of farmers and citizens are influenced by different socio-demographic characteristics. The results can help the government to design more aimed farmland eco-compensation methods for farmers with different socio-demographic characteristics.