/pdf/a-review-of-keypoints-detection-and-feature-description-in-2nuc79pr7t.pdf

A Review of Keypoints’ Detection and Feature Description in Image Registration

Seafloor image is important for marine scientific research and ocean engineering. With the wide use of side scan sonar (SSS), multiple measured SSS images need to be mosaicked to form a large-scale and high-resolution seafloor image. This article proposes an automatic mosaic method. First, the overlapping areas between adjacent strips are determined automatically by the track lines and swath width. In the overlapping areas, the image matching is conducted using the speeded-up robust features algorithm with the constraint of geographic coordinates of detected feature points. Then, the overlapping area is segmented using the k-means cluster method. Within each segmented region, the coordinate transformation model is established to make the positions of common features in the overlapping areas unique. With regards to multiple SSS images mosaic, the method to mosaic in sequence is also proposed according to the permutation parameter of each strip. Finally, the SSS images in the overlapping areas are fused and a large-scale seafloor image is formed. This proposed method was applied in one water area of Shenzhen, demonstrating good performance in terms of coordinate consistency of the same features in the mosaicked image. The mean coordinate deviations of the same feature points in overlapping areas were nearly zero and the standard deviation of them also decreased. This proposed method is easily transferable to other study areas and provides an objective, repeatable means for multiple SSS images mosaic.

/pdf/automatic-overlapping-area-determination-and-segmentation-9sle3e1l52.pdf

Automatic Overlapping Area Determination and Segmentation for Multiple Side Scan Sonar Images Mosaic

Limited by aircraft flight altitude and camera parameters, it is necessary to obtain wide-angle panoramas quickly by stitching aerial images, which is helpful in rapid disaster investigation, recovery after earthquakes, and aerial reconnaissance. However, most existing stitching algorithms do not simultaneously meet practical real-time, robustness, and accuracy requirements, especially in the case of a long-distance multistrip flight. In this paper, we propose a novel image-only real-time UAV image mosaic framework for long-distance multistrip flights that does not require any auxiliary information, such as GPS or GCPs. The framework has a complete structure, mainly consisting of the three tasks of automatic initialization, current frame tracking, and real-time mosaic generation. The stitching plane is determined in the initialization process, the homography transformation of the current image is estimated in the tracking task, and the image is mapped to the stitching plane to generate and update the panorama in the real-time mosaic process. The core idea is that, in the tracking task, we introduce and develop a keyframe insertion strategy to generate a keyframe list and, on this basis, design a homography matrix estimation based on a local optimization strategy to reduce the accumulated error when continuously stitching image sequences collected online by UAVs and to realize real-time, effective UAV image mosaic construction. In addition, this framework has good scalability, which is not limited to a specific algorithm. To evaluate the effectiveness of the proposed framework, we carry out a large number of experiments on the AirSim simulation platform and present an exhaustive evaluation in some sequences from a popular dataset. Qualitative and quantitative experimental results in simulation and real environments demonstrate that our algorithm can obtain an effective and robust mosaic image in real-time. Through strategy comparison experiments, it is proven that the keyframe insertion strategy and the local optimization strategy both improve the stitching performance. Compared with five state-of-art image stitching approaches, the mosaic effect of the proposed method is comparable or better. In terms of algorithm speed, its performance is superior to them. Additionally, experiments of illumination change and feature replacement in the framework verify the good adaptability and scalability of the algorithm.

Image-Only Real-Time Incremental UAV Image Mosaic for Multi-Strip Flight

High-accuracy three-dimensional measurement based on multi-directional cooperative target with weighted SfM algorithm

Due to the limitation of economic geography condition, the existing health resources distribution is uneven, the emergence of remote medical disciplines perfectly solved this difficult problem, using computer and network communication network on audio video information transmissions, querying, display, storage, and backup and realizing the network of outpatient service, remote consultation, health advice, and other functions. Telemedicine enables the limited available medical resources to be shared and fully utilized and also enables many economically underdeveloped provinces to enjoy a higher level of medical sharing services. Public health emergency management system on the overall design of the low latency according to system function will be based on the Internet of things. The remote public health emergency management system is divided into three subsystems low latency modules, basic subsystems of platform, application platform, and specific application subsystems, and designs the structure of the various modules. The implementation process is given. In the realization of the system, this paper describes in detail how to realize the functions of the public medical low delay emergency management system, and, in the end, the realization process of the system is reasonably summarized. The application of Internet of things technology in regional emergency rescue can realize the identification and real-time positioning of material personnel, the collection and transmission of the wounded's physiological information, real-time information transmission, and interaction based on mobile handheld devices, as well as the integration of emergency rescue resources, information integration, and command decision-making, so as to assist rescue operations and improve rescue efficiency.

/pdf/optimization-of-remote-public-medical-emergency-management-4yijriderk.pdf

Optimization of Remote Public Medical Emergency Management System with Low Delay Based on Internet of Things.

The remote sensing technology of unmanned aerial vehicle (UAV) is a low altitude remote sensing technology. The technology has been widely used in military, agricultural, medical, geographical mapping, and other fields by virtue of the advantages of fast acquisition, high resolution, low cost, and good security. But limited by the flying height of UAV and the focal length of the digital camera, the single image obtained by the UAV is difficult to form the overall cognition of the ground farmland area. In order to further expand the field of view, it is necessary to mosaic multiple single images acquired by UAV into a complete panoramic image of the farmland. In this paper, aiming at the problem of UAV low-altitude remote sensing image splicing, an image mosaic technique based on Speed Up Robust Feature (SURF) is introduced to achieve rapid image splicing. One hundred fifty ground farmland remote sensing images collected by UAV are used as experimental splicing objects, and the image splicing is completed by the global stitching strategy optimized by Levenberg-Marquardt (L-M). Experiments show that the strategy can effectively reduce the influence of cumulative errors and achieve automatic panoramic mosaic of the survey area.

/pdf/remote-sensing-image-mosaic-technology-based-on-surf-x58i9h0f08.pdf

Remote sensing image mosaic technology based on SURF algorithm in agriculture

To allow doctors to monitor the physical parameters of the patient’s body in real time and to understand the changes in the patient’s condition in time, the medical remote monitoring system based on the Internet of Things was studied. From the perspective of practical application of hospital wards, a medical health monitoring system was designed with the help of CC2430 microcontroller, human information sensor, and microelectronic and modern wireless communication technology. In addition, a sensor node circuit and a coordinator node circuit for collecting medical signals were designed. Meanwhile, the software of wireless sensor network was designed. Finally, the online debugging of each system module was combined with hardware and software. The experimental result proved that the network node was reliable and the data transmission was accurate. It is concluded that the medical monitoring system basically meets the design requirements of this paper.

/pdf/medical-long-distance-monitoring-system-based-on-internet-of-1eh84no007.pdf

Medical long-distance monitoring system based on internet of things

Water distribution networks (WDNs) are prone to infrastructure failures that lead to water loss. This paper presents a practical and efficient leakage detection and localization method based on the supervisory control and data acquisition (SCADA) system combined with state estimation. In WDNs, normal node demand patterns, seasonal variations, and leakage can cause pressure sensor values to fluctuate; accordingly, the pressure sensor values in a SCADA system can be regarded as exhibiting a mixture of normal and abnormal fluctuations. To obtain the fluctuation signal arising from leakage, the node demands must first be calibrated. For leakage detection, the proposed method involves comparing the errors between historical monitored values and simulated values to assess whether a change in these errors is due to normal system changes or the impact of leakage. At the leakage location, an emitter coefficient is used to simulate unknown leakage flow. Then, similarity indicators of the leakage locations are constructed using the Euclidian distance, Pearson correlation coefficient, and Jaccard similarity. The leakage location can be determined by comparing these similarity values. The performance of this method was tested on the L-Town WDN provided in the BattLeDIM competition. Our model’s ability to quickly and accurately detect and locate leakages was validated on the 2018 data set of leakage events. The model then was applied to predict the times and locations of leakages in 2019, and eight leakages were detected, six of which were true positives. 

Junfeng Yu

Papers

Remote sensing image mosaic technology based on SURF algorithm in agriculture

Medical long-distance monitoring system based on internet of things

A Real-Time Method to Detect the Leakage Location in Urban Water Distribution Networks

Variational learning of deep fuzzy theoretic nonparametric model