Texture Synthesis Repair of RealSense D435i Depth Images with Object-Oriented RGB Image Segmentation.

A depth camera is a kind of sensor that can directly collect distance information between an object and the camera. The RealSense D435i is a low-cost depth camera that is currently in widespread use. When collecting data, an RGB image and a depth image are acquired simultaneously. The quality of the RGB image is good, whereas the depth image typically has many holes. In a lot of applications using depth images, these holes can lead to serious problems. In this study, a repair method of depth images was proposed. The depth image is repaired using the texture synthesis algorithm with the RGB image, which is segmented through a multi-scale object-oriented method. The object difference parameter is added to the process of selecting the best sample block. In contrast with previous methods, the experimental results show that the proposed method avoids the error filling of holes, the edge of the filled holes is consistent with the edge of RGB images, and the repair accuracy is better. The root mean square error, peak signal-to-noise ratio, and structural similarity index measure from the repaired depth images and ground-truth image were better than those obtained by two other methods. We believe that the repair of the depth image can improve the effects of depth image applications.

Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model.

Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R2 = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R2 = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R2 = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R2 = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R2 = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R2 = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R2 = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method.

Probability-Based Indoor Positioning Algorithm Using iBeacons.

High-precision indoor positioning is important for modern society. This paper proposes a way to achieve high positioning accuracy and obtain a trajectory close to the actual path in a common application scenario by smartphone without the use of a complicated algorithm. In the actual positioning process, a stable signal source can reduce the signal interference caused by environments. Bluetooth low energy has its own advantages in indoor positioning because it can be seen as a more stable signal source. In this study, we used smartphones to record the changing Bluetooth signals and used a basic nearest neighbor, weight centroid, and probability-based method, which we called an advanced weighted centroid method, to obtain position coordinates and the motion trajectory during the experiment. We used a weight centroid method based on least squares to solve the overdetermined problem. This can also be used to calculate the initial position of the advanced weight centroid. The advanced weighted centroid method introduced a Gaussian distribution to model the distribution of the signal. Translating a deterministic problem into a fuzzy probability problem aligns more with positioning facts and can achieve better results. Experimental results showed that the root-mean-square error (RMSE) of the dynamic positioning result obtained through the probabilistic method was within 1 m and had a more consistent trajectory. Moreover, the impact of the number of iBeacons on the positioning accuracy has been discussed, and a reference for iBeacon placement has been provided. In addition, an experiment was also conducted on the effect of signal transmission frequency on accuracy.

Mapping Urban Environmental Noise Using Smartphones.

Noise mapping is an effective method of visualizing and accessing noise pollution. In this paper, a noise-mapping method based on smartphones to effectively and easily measure environmental noise is proposed. By using this method, a noise map of an entire area can be created using limited measurement data. To achieve the measurement with certain precision, a set of methods was designed to calibrate the smartphones. Measuring noise with mobile phones is different from the traditional static observations. The users may be moving at any time. Therefore, a method of attaching an additional microphone with a windscreen is proposed to reduce the wind effect. However, covering an entire area is impossible. Therefore, an interpolation method is needed to achieve full coverage of the area. To reduce the influence of spatial heterogeneity and improve the precision of noise mapping, a region-based noise-mapping method is proposed in this paper, which is based on the distribution of noise in different region types tagged by volunteers, to interpolate and combine them to create a noise map. To validate the effect of the method, a comparison of the interpolation results was made to analyse our method and the ordinary Kriging method. The result shows that our method is more accurate in reflecting the local distribution of noise and has better interpolation precision. We believe that the proposed noise-mapping method is a feasible and low-cost noise-mapping solution.

Using multiple barometers to detect the floor location of smart phones with built-in barometric sensors for indoor positioning.

Following the popularity of smart phones and the development of mobile Internet, the demands for accurate indoor positioning have grown rapidly in recent years. Previous indoor positioning methods focused on plane locations on a floor and did not provide accurate floor positioning. In this paper, we propose a method that uses multiple barometers as references for the floor positioning of smart phones with built-in barometric sensors. Some related studies used barometric formula to investigate the altitude of mobile devices and compared the altitude with the height of the floors in a building to obtain the floor number. These studies assume that the accurate height of each floor is known, which is not always the case. They also did not consider the difference in the barometric-pressure pattern at different floors, which may lead to errors in the altitude computation. Our method does not require knowledge of the accurate heights of buildings and stories. It is robust and less sensitive to factors such as temperature and humidity and considers the difference in the barometric-pressure change trends at different floors. We performed a series of experiments to validate the effectiveness of this method. The results are encouraging.

Using smart phone sensors to detect transportation modes.

The proliferation of mobile smart devices has led to a rapid increase of location-based services, many of which are amassing large datasets of user trajectory information. Unfortunately, current trajectory information is not yet sufficiently rich to support classification of user transportation modes. In this paper, we propose a method that employs both the Global Positioning System and accelerometer data from smart devices to classify user outdoor transportation modes. The classified modes include walking, bicycling, and motorized transport, in addition to the motionless (stationary) state, for which we provide new depth analysis. In our classification, stationary mode has two sub-modes: stay (remaining in the same place for a prolonged time period; e.g., in a parked vehicle) and wait (remaining at a location for a short period; e.g., waiting at a red traffic light). These two sub-modes present different semantics for data mining applications. We use support vector machines with parameters that are optimized for pattern recognition. In addition, we employ ant colony optimization to reduce the dimension of features and analyze their relative importance. The resulting classification system achieves an accuracy rate of 96.31% when applied to a dataset obtained from 18 mobile users.