Real-time Depth Estimation Method Using Hybrid Camera System

Based on multi-feature fusion, this paper introduces a novel depth estimation method to suppress defocus and motion blurs, as well as focal plane ambiguity. Firstly, the node features formed by occlusion were fused to optimize image segmentation, and obtain the position relations between image objects. Next, the Gaussian gradient ratio between the defocused input image and the quadratic Gaussian blur was calculated to derive the edge sparse blur. After that, the fast guided filter was adopted to diffuse the sparse blur globally, and estimate the relative depth of the scene. Experimental results demonstrate that our method excellently resolves the ambiguity of depth estimation, and accurately overcomes the noise problem in real-time.

Download Full-text

Real-time Depth Estimation and View Interpolation using Quasar

Electronic Imaging ◽

10.2352/issn.2470-1173.2016.19.coimg-179 ◽

2016 ◽

Vol 2016 (19) ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Bart Goossens ◽

Simon Donné ◽

Jan Aelterman ◽

Jonas De Vylder ◽

Dirk Van Haerenborgh ◽

...

Keyword(s):

Real Time ◽

Depth Estimation ◽

View Interpolation

Download Full-text

JOINT ESTIMATION OF THE TRANSMISSIBILITY AND SEVERITY OF EBOLA VIRUS DISEASE IN REAL TIME

Journal of Biological System ◽

10.1142/s0218339017400022 ◽

2017 ◽

Vol 25 (04) ◽

pp. 587-603 ◽

Cited By ~ 2

Author(s):

YUSUKE ASAI ◽

HIROSHI NISHIURA

Keyword(s):

Real Time ◽

Ebola Virus ◽

Virus Disease ◽

Critical Role ◽

Estimation Method ◽

Case Fatality ◽

Ascertainment Bias ◽

Joint Estimation ◽

Ebola Virus Disease ◽

Strong Impact

The effective reproduction number [Formula: see text], the average number of secondary cases that are generated by a single primary case at calendar time [Formula: see text], plays a critical role in interpreting the temporal transmission dynamics of an infectious disease epidemic, while the case fatality risk (CFR) is an indispensable measure of the severity of disease. In many instances, [Formula: see text] is estimated using the reported number of cases (i.e., the incidence data), but such report often does not arrive on time, and moreover, the rate of diagnosis could change as a function of time, especially if we handle diseases that involve substantial number of asymptomatic and mild infections and large outbreaks that go beyond the local capacity of reporting. In addition, CFR is well known to be prone to ascertainment bias, often erroneously overestimated. In this paper, we propose a joint estimation method of [Formula: see text] and CFR of Ebola virus disease (EVD), analyzing the early epidemic data of EVD from March to October 2014 and addressing the ascertainment bias in real time. To assess the reliability of the proposed method, coverage probabilities were computed. When ascertainment effort plays a role in interpreting the epidemiological dynamics, it is useful to analyze not only reported (confirmed or suspected) cases, but also the temporal distribution of deceased individuals to avoid any strong impact of time dependent changes in diagnosis and reporting.

Download Full-text

Real-time Array Shape Estimation Method of Horizontal Suspended Linear Array Based on Non-acoustic Auxiliary Sensors

IEEE Access ◽

10.1109/access.2021.3061446 ◽

2021 ◽

pp. 1-1

Author(s):

Zonglun Che ◽

Jun Wang ◽

Jing Zhu ◽

Bingbing Zhang ◽

Yang Zhang ◽

...

Keyword(s):

Real Time ◽

Linear Array ◽

Estimation Method ◽

Shape Estimation ◽

Array Shape Estimation

Download Full-text

Improvement of Reliability Determination Performance of Real Time Kinematic Solutions Using Height Trajectory

Sensors ◽

10.3390/s21020657 ◽

2021 ◽

Vol 21 (2) ◽

pp. 657

Author(s):

Aoki Takanose ◽

Yoshiki Atsumi ◽

Kanamu Takikawa ◽

Junichi Meguro

Keyword(s):

Real Time ◽

Urban Areas ◽

Estimation Method ◽

Tall Buildings ◽

Autonomous Driving ◽

Reliability Estimation ◽

Height Variation ◽

Position Information ◽

Real Time Kinematic ◽

Evaluation Test

Autonomous driving support systems and self-driving cars require the determination of reliable vehicle positions with high accuracy. The real time kinematic (RTK) algorithm with global navigation satellite system (GNSS) is generally employed to obtain highly accurate position information. Because RTK can estimate the fix solution, which is a centimeter-level positioning solution, it is also used as an indicator of the position reliability. However, in urban areas, the degradation of the GNSS signal environment poses a challenge. Multipath noise caused by surrounding tall buildings degrades the positioning accuracy. This leads to large errors in the fix solution, which is used as a measure of reliability. We propose a novel position reliability estimation method by considering two factors; one is that GNSS errors are more likely to occur in the height than in the plane direction; the other is that the height variation of the actual vehicle travel path is small compared to the amount of movement in the horizontal directions. Based on these considerations, we proposed a method to detect a reliable fix solution by estimating the height variation during driving. To verify the effectiveness of the proposed method, an evaluation test was conducted in an urban area of Tokyo. According to the evaluation test, a reliability judgment rate of 99% was achieved in an urban environment, and a plane accuracy of less than 0.3 m in RMS was achieved. The results indicate that the accuracy of the proposed method is higher than that of the conventional fix solution, demonstratingits effectiveness.

Download Full-text

MonoER - A Edge Refined Self-Supervised Monocular Depth Estimation Method

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9326510 ◽

2020 ◽

Author(s):

Tianyu Xiang ◽

Lingzhe Zhao ◽

Hao Zhang ◽

Zhuping Wang

Keyword(s):

Estimation Method ◽

Depth Estimation ◽

Monocular Depth

Download Full-text

Real-Time Single Image Depth Perception in the Wild with Handheld Devices

Sensors ◽

10.3390/s21010015 ◽

2020 ◽

Vol 21 (1) ◽

pp. 15

Author(s):

Filippo Aleotti ◽

Giulio Zaccaroni ◽

Luca Bartolomei ◽

Matteo Poggi ◽

Fabio Tosi ◽

...

Keyword(s):

Real Time ◽

Depth Perception ◽

Depth Estimation ◽

Autonomous Driving ◽

Estimation Methods ◽

Handheld Devices ◽

Single Image ◽

Handheld Device ◽

Time Performance ◽

In The Wild

Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.

Download Full-text