Segmentation from point cloud data is essential in many applications, such as remote sensing, mobile robots, or autonomous cars. However, the point clouds captured by the 3D range sensor are commonly sparse and unstructured, challenging efficient segmentation. A fast solution for point cloud instance segmentation with small computational demands is lacking. To this end, we propose a novel fast Euclidean clustering (FEC) algorithm which applies a point-wise scheme over the cluster-wise scheme used in existing works. The proposed method avoids traversing every point constantly in each nested loop, which is time and memory-consuming. Our approach is conceptually simple, easy to implement (40 lines in C++), and achieves two orders of magnitudes faster against the classical segmentation methods while producing high-quality results.

Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna.

We propose an accurate and fast bundle adjustment (BA) solution that estimates the 6-DoF pose with an independent RS model of the camera and the geometry of the environment based on measurements from a rolling shutter (RS) camera. This tackles the challenges in the existing works, namely, relying on high frame rate video as input, restrictive assumptions on camera motion and poor efficiency. To this end, we first verify the positive influence of the image point normalization to RSBA. Then we present a novel visual residual covariance model to standardize the reprojection error during RSBA, which consequently improves the overall accuracy. Besides, we demonstrate the combination of Normalization and covariance standardization Weighting in RSBA (NW-RSBA) can avoid common planar degeneracy without the need to constrain the filming manner. Finally, we propose an acceleration strategy for NW-RSBA based on the sparsity of its Jacobian matrix and Schur complement. The extensive synthetic and real data experiments verify the effectiveness and efficiency of the proposed solution over the state-of-the-art works.

Background: Due to the emergency pandemic threat, the COVID-19 has attracted widespread attention around the world. Common symptoms of infection were fever, cough, and myalgia fatigue. On January 31, 2020, the World Health Organization (WHO) declares this outbreak a Public Health Emergency of International Concern (PHEIC).Methods: In order to study the spread of the novel coronavirus pneumonia, this paper proposed an improved SEIR model to simulate the spread of the virus, which includes the effect factor of government intervention. The model parameters are determined based on the daily reported statistical data (up to February 8) of confirmed, suspected, cured, and death. According to utilize the spread rate, the probability of infection of the suspected, the probability of the suspected becoming a confirmed one, the cure rate, the mortality rate, and the quarantine ratio, we performed simulations and parameter calibrations at three region levels, i.e., China, Hubei and non-Hubei respectively. In addition, considering that the government initiated effective prevention and control measures after the outbreak, this paper dynamically estimates all the parameters of the proposed model.Results: The simulation reveals that the parameters of non-Hubei region are not significantly different from Hubei’s. Hubei Province has a high transmission rate, low cure rate, high probability of infection, low effective quarantine rate. since January 31, with the continuous strengthening of epidemic prevention and control measures, all parameters of the model have changed significantly. The parameters of Hubei and non-Hubei regions have the same trend. The trend of all parameters is now moving in a direction that is conducive to reducing the number of confirmed, suspected and fatal cases.Conclusions: The number of infections of the virus initially showed a rapid increase in the trend, and the number of infectious case showed a clear downward shape. With the government to take a variety of prevention and control measures and the efforts of the general medical staff, the number of infection curve on February 22 appeared in the top of the arc pattern, indicating that the inflection point began to appear, but the decline in the number of infections slowly.

Large-scale Neural Radiance Fields (NeRF) reconstructions are typically hindered by the requirement for extensive image datasets and substantial computational resources. This paper introduces IOVS4NeRF, a framework that employs an uncertainty-guided incremental optimal view selection strategy adaptable to various NeRF implementations. Specifically, by leveraging a hybrid uncertainty model that combines rendering and positional uncertainties, the proposed method calculates the most informative view from among the candidates, thereby enabling incremental optimization of scene reconstruction. Our detailed experiments demonstrate that IOVS4NeRF achieves high-fidelity NeRF reconstruction with minimal computational resources, making it suitable for large-scale scene applications.