NONLINEAR DIFFUSION FOR ORIENTATION ESTIMATION

This letter presents an image orientation estimation method which is based on a combination of two techniques： quadrature filtering and nonlinear diffusion. The quadrature filters are used to get the orientation tensors for edges, then the orientation tensors are smoothed through nonlinear diffusion. Experimental resuits and analysis show the robustness of the proposed method.

Better Fibre Orientation Estimation with Single-Shell Diffusion MRI Using Spherical U-Net

Diffusion MRI is an important technology for detecting human brain nerve pathways,aiding in neuroscience and clinical diagnosis.However,the Multi-ShellMulti-TissueConstrainedSphericalDeconvolution(M-CSD)method,which is a significant technique for reconstructing thefibre orientation distribution func-tion(fODF),requires multishell data with a considerable number of gradient direc-tions to achieve high accuracy.As multishell data are not easy to acquire,the Single-Shell Single-Tissue CSD(S-CSD)suffers from the Partial Volume Effect(PVE).It would be more convenient if we could use single-shell data to reconstruct better fODFs.We propose a novel method that utilizes the spatial structure and anisotropy of dMRI data through a spherical convolution network.We reduce the need for high-quality data by utilizing b=1000 s/mm2 with 60 gradient directions or even less.Our results show that our method outperforms the traditional S-CSD when compared to the M-CSD results as our gold standard.

Distributed bearing-based formation control of unmanned aerial vehicle swarm via global orientation estimation

Most existing formation control approaches for Unmanned Aerial Vehicle(UAV)swarm assume that global position and global coordinate frame are directly available for each agent.To extend the application domain,this paper proposes a distributed bearing-based formation control scheme,without any reliance on global position or global coordinate frame.The interactions among UAVs are described by a directed topology with two-leader structure.To address the issue of unavailable global coordinate frame,we first present a distributed orientation estimation law for each UAV to determine its orientation under the coordinate frame of the first leader.Based on the orientation estimation,we then design a bearing-based formation control law to globally asymptotically track target moving formations.Finally,simulation results are provided to validate the proposed method,which show that the translation,scale and orientation of the formation can be flexibly controlled via two leaders.

VIDEO BASED ESTIMATION OF PEDESTRIAN WALKING DIRECTION FOR PEDESTRIAN PROTECTION SYSTEM

Pedestrian protection has played an important role for driver assistance systems.Our aim is to develop a video based driver assistance system for the detection of the potentially dangerous situation between the vehicle and pedestrian,in order to warn the driver.In this paper,we address the problem of detecting pedestrian in real-world scenes and estimation of the walking direction with a single camera from a moving vehicle.Considering all the available cues for predicting the possibility of collision is very important.The direction in which the pedestrian is facing is one of the most important cues predicting where the pedestrian may move in the future.So we first address the problem of sin-gle-frame pedestrian orientation estimation in real-world scenes.Then again,we estimate the pedes-trian walking direction using multi-frame based on the result of single-frame orientation estimation.We propose a three-step method:pedestrian detection for single-frame step,orientation estimation for single-frame step and walking direction estimation for multi-frame step.To evaluate the proposed method in its robustness and accuracy,the experiments have been performed between numbers of images which is highly challenging uncontrolled conditions in real world.It shows a significant per-formance improvement in octant orientation estimation of about 64% accuracy in the orientation es-timation step and achieved surprisingly good accuracy in estimating the walking direction against 212 targeted objects.

Single frame super-resolution reconstruction based on sparse representation

In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of- the-art counterparts in terms of both numerical indicators and visual quality.

Indoor Positioning System Using Axis Alignment and Complementary IMUs for Robot Localization

An indoor positioning method for robots is presented to improve the precision of displacement measurement using only low-cost inertial measurement units(IMUs).Firstly,a high-fidelity displacement estimation for linear motion is proposed.A new robot motion model is designed as well as an axis alignment that only uses a single axis of the accelerometer.The integral error of velocity is eliminated by a new subsection calculation method.Two complementary IMUs are combined by assigning them different weights to obtain high accuracy displacement results.Secondly,an orientation estimation based on a fusion filter for the steering motion is proposed.Experiments show that the proposed method significantly improves the accuracy of linear motion measurement and is effective for the indoor positioning of a robot.