Calibration and Noise Identification of a Rolling Shutter Camera and a Low-Cost Inertial Measurement Unit.

A low-cost inertial measurement unit (IMU) and a rolling shutter camera form a conventional device configuration for localization of a mobile platform due to their complementary properties and low costs. This paper proposes a new calibration method that jointly estimates calibration and noise parameters of the low-cost IMU and the rolling shutter camera for effective sensor fusion in which accurate sensor calibration is very critical. Based on the graybox system identification, the proposed method estimates unknown noise density so that we can minimize calibration error and its covariance by using the unscented Kalman filter. Then, we refine the estimated calibration parameters with the estimated noise density in batch manner. Experimental results on synthetic and real data demonstrate the accuracy and stability of the proposed method and show that the proposed method provides consistent results even with unknown noise density of the IMU. Furthermore, a real experiment using a commercial smartphone validates the performance of the proposed calibration method in off-the-shelf devices.

Evaluating the Modified Patient Health Questionnaire-2 and Insomnia Severity Index-2 for Daily Digital Screening of Depression and Insomnia: Validation Study.

BACKGROUND: The Patient Health Questionnaire-2 (PHQ-2) and Insomnia Severity Index-2 (ISI-2) are screening assessments that reflect the past 2-week experience of depression and insomnia, respectively. Retrospective assessment has been associated with reduced accuracy owing to recall bias. OBJECTIVE: This study aimed to increase the reliability of responses by validating the use of the PHQ-2 and ISI-2 for daily screening. METHODS: A total of 167 outpatients from the psychiatric department at the Yongin Severance Hospital participated in this study, of which 63 (37.7%) were male and 104 (62.3%) were female with a mean age of 35.1 (SD 12.1) years. Participants used a mobile app ("Mental Protector") for 4 weeks and rated their depressive and insomnia symptoms daily on the modified PHQ-2 and ISI-2 scales. The validation assessments were conducted in 2 blocks, each with a fortnight response from the participants. The modified version of the PHQ-2 was evaluated against the conventional scales of the Patient Health Questionnaire-9 and the Korean version of the Center for Epidemiologic Studies Depression Scale-Revised. RESULTS: According to the sensitivity and specificity analyses, an average score of 3.29 on the modified PHQ-2 was considered valid for screening for depressive symptoms. Similarly, the ISI-2 was evaluated against the conventional scale, Insomnia Severity Index, and a mean score of 3.50 was determined to be a valid threshold for insomnia symptoms when rated daily. CONCLUSIONS: This study is one of the first to propose a daily digital screening measure for depression and insomnia delivered through a mobile app. The modified PHQ-2 and ISI-2 were strong candidates for daily screening of depression and insomnia, respectively.

Recent Development of Computer Vision Technology to Improve Capsule Endoscopy.

Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capture a sparse number of images because of their mechanical limitations. Post-procedural management using computational methods can enhance image quality. Additional information, including depth, can be obtained by using recently developed computer vision techniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer vision technology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions more accurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditional computerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving the diagnostic yield of CE. The close collaboration between information technology and medical professionals is needed.

Interacting Multiview Tracker.

A robust algorithm is proposed for tracking a target object in dynamic conditions including motion blurs, illumination changes, pose variations, and occlusions. To cope with these challenging factors, multiple trackers based on different feature representations are integrated within a probabilistic framework. Each view of the proposed multiview (multi-channel) feature learning algorithm is concerned with one particular feature representation of a target object from which a tracker is developed with different levels of reliability. With the multiple trackers, the proposed algorithm exploits tracker interaction and selection for robust tracking performance. In the tracker interaction, a transition probability matrix is used to estimate dependencies between trackers. Multiple trackers communicate with each other by sharing information of sample distributions. The tracker selection process determines the most reliable tracker with the highest probability. To account for object appearance changes, the transition probability matrix and tracker probability are updated in a recursive Bayesian framework by reflecting the tracker reliability measured by a robust tracker likelihood function that learns to account for both transient and stable appearance changes. Experimental results on benchmark datasets demonstrate that the proposed interacting multiview algorithm performs robustly and favorably against state-of-the-art methods in terms of several quantitative metrics.