Pedestrian Height Estimation and 3D Reconstruction Using Pixel-resolution Mapping Method Without Special Patterns

Wu, Bingxing; Ay, Suat U.; Abdel-Rahim, Ahmed

doi:10.1007/s11633-019-1170-2

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Compared with the CPU-intensive implementations, much less time is required using the proposed parallel implicit fitting technique. The proposed method can be used to reconstruct scene from scanned "3D information" similar to the "P-RM method" proposed in [31].…”

Section: Discussionmentioning

confidence: 99%

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Skeleton Marching-based Parallel Vascular Geometry Reconstruction Using Implicit Functions

Cheng

et al. 2019

Int. J. Autom. Comput.

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Fast high-precision patient-specific vascular tissue and geometric structure reconstruction is an essential task for vascular tissue engineering and computer-aided minimally invasive vascular disease diagnosis and surgery. In this paper, we present an effective vascular geometry reconstruction technique by representing a highly complicated geometric structure of a vascular system as an implicit function. By implicit geometric modelling, we are able to reduce the complexity and level of difficulty of this geometric reconstruction task and turn it into a parallel process of reconstructing a set of simple short tubular-like vascular sections, thanks to the easy-blending nature of implicit geometries on combining implicitly modelled geometric forms. The basic idea behind our technique is to consider this extremely difficult task as a process of team exploration of an unknown environment like a cave. Based on this idea, we developed a parallel vascular modelling technique, called Skeleton Marching, for fast vascular geometric reconstruction. With the proposed technique, we first extract the vascular skeleton system from a given volumetric medical image. A set of sub-regions of a volumetric image containing a vascular segment is then identified by marching along the extracted skeleton tree. A localised segmentation method is then applied to each of these sub-image blocks to extract a point cloud from the surface of the short simple blood vessel segment contained in the image block. These small point clouds are then fitted with a set of implicit surfaces in a parallel manner. A high-precision geometric vascular tree is then reconstructed by blending together these simple tubular-shaped implicit surfaces using the shape-preserving blending operations. Experimental results show the time required for reconstructing a vascular system can be greatly reduced by the proposed parallel technique.

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Section: Discussionmentioning

confidence: 99%

“…It has received much attention with the fast development of general-purpose computing on graphics processing units (GPGPU). The massive parallel processors and memories on a GPGPU are able to process complicated tasks in a parallel manner and significantly improve the computational efficiency [30] .…”

Section: Parallel Computingmentioning

confidence: 99%

Skeleton Marching-based Parallel Vascular Geometry Reconstruction Using Implicit Functions

Cheng

et al. 2019

Int. J. Autom. Comput.

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Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

Zhao

Cao

Kang

et al. 2021

Int. J. Autom. Comput.

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With the rapid increase of the amount of vehicles in urban areas, the pollution of vehicle emissions is becoming more and more serious. Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making. Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning. Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions, however, they are not effective and efficient enough in the combination and utilization of different inputs. To address this issue, we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator (MOVES) model. Specifically, we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms. These matrices can reflect the attributes related to the traffic status of road networks such as volume, speed and acceleration. Then, our multi-channel spatiotemporal network is used to efficiently combine three key attributes (volume, speed and acceleration) through the feature sharing mechanism and generate a precise prediction of them in the future period. Finally, we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states, road networks and the statistical information of urban vehicles. We evaluate our model on the Xi’an taxi GPS trajectories dataset. Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.

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Pedestrian Height Estimation and 3D Reconstruction Using Pixel-resolution Mapping Method Without Special Patterns

Cited by 2 publications

References 23 publications

Skeleton Marching-based Parallel Vascular Geometry Reconstruction Using Implicit Functions

Skeleton Marching-based Parallel Vascular Geometry Reconstruction Using Implicit Functions

Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

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