Sparse Depth-Guided Image Enhancement Using Incremental GP with Informative Point Selection.

We propose an online dehazing method with sparse depth priors using an incremental Gaussian Process (iGP). Conventional approaches focus on achieving single image dehazing by using multiple channels. In many robotics platforms, range measurements are directly available, except in a sparse form. This paper exploits direct and possibly sparse depth data in order to achieve efficient and effective dehazing that works for both color and grayscale images. The proposed algorithm is not limited to the channel information and works equally well for both color and gray images. However, efficient depth map estimations (from sparse depth priors) are additionally required. This paper focuses on a highly sparse depth prior for online dehazing. For efficient dehazing, we adopted iGP for incremental depth map estimation and dehazing. Incremental selection of the depth prior was conducted in an information-theoretic way by evaluating mutual information (MI) and other information-based metrics. As per updates, only the most informative depth prior was added, and haze-free images were reconstructed from the atmospheric scattering model with incrementally estimated depth. The proposed method was validated using different scenarios, color images under synthetic fog, real color, and grayscale haze indoors, outdoors, and underwater scenes.

Validation of a Multi-Sensor-Based Kiosk for Short Physical Performance Battery.

OBJECTIVES: We aimed to validate a multi-sensor-based kiosk (automatically measured Short Physical Performance Battery [eSPPB] kiosk) that can perform automated measurement of the SPPB. DESIGN: Prospective, cross-sectional study. SETTING: Rehabilitation clinic of a tertiary-care hospital. PARTICIPANTS: Ambulatory outpatients, aged 65 years or older (N = 40). MEASUREMENTS: The eSPPB kiosk was developed to measure the three components of the SPPB: standing balance, gait speed, and chair stand test with embedded sensors and algorithms. Correlations between the total and component-specific scores of the eSPPB and manually measured SPPB (mSPPB), assessed by a physical therapist, were assessed. Further, correlations between SPPB parameters and geriatric functional measures were also evaluated. RESULTS: This study included 40 participants with a mean age of 74.4 ± 6.5 years, a mean total eSPPB score of 10.1 ± 2.1, and a mean total mSPPB score of 10.2 ± 2.1. The intraclass correlation coefficient between the eSPPB and mSPPB total score was 0.97 (P < .001), and the κ agreement was 0.79 (P < .001). The intraclass coefficients between the components of eSPPB and mSPPB were 0.77 (P < .001), 0.88 (P < .001), and 0.99 (P < .001) for standing balance, gait speed, and chair stand test, respectively. CONCLUSION: The newly developed kiosk might be a viable and efficient method for performing the SPPB in older adults. J Am Geriatr Soc 67:2605-2609, 2019.

Accurate Mobile Urban Mapping via Digital Map-Based SLAM.

This paper presents accurate urban map generation using digital map-based Simultaneous Localization and Mapping (SLAM). Throughout this work, our main objective is generating a 3D and lane map aiming for sub-meter accuracy. In conventional mapping approaches, achieving extremely high accuracy was performed by either (i) exploiting costly airborne sensors or (ii) surveying with a static mapping system in a stationary platform. Mobile scanning systems recently have gathered popularity but are mostly limited by the availability of the Global Positioning System (GPS). We focus on the fact that the availability of GPS and urban structures are both sporadic but complementary. By modeling both GPS and digital map data as measurements and integrating them with other sensor measurements, we leverage SLAM for an accurate mobile mapping system. Our proposed algorithm generates an efficient graph SLAM and achieves a framework running in real-time and targeting sub-meter accuracy with a mobile platform. Integrated with the SLAM framework, we implement a motion-adaptive model for the Inverse Perspective Mapping (IPM). Using motion estimation derived from SLAM, the experimental results show that the proposed approaches provide stable bird's-eye view images, even with significant motion during the drive. Our real-time map generation framework is validated via a long-distance urban test and evaluated at randomly sampled points using Real-Time Kinematic (RTK)-GPS.