Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
Foreground target detection algorithm (FTDA) is a fundamental preprocessing step in computer vision and video processing. A\nuniversal background subtraction algorithm for video sequences (ViBe) is a fast, simple, efficient and with optimal sample\nattenuation FTDA based on background modeling. However, the traditional ViBe has three limitations: (1) the noise problem\nunder dynamic background; (2) the ghost problem; and (3) the target adhesion problem. In order to solve the three problems\nabove, ant colony clustering is introduced and Ant_ViBe is proposed in this paper to improve the background modeling\nmechanism of the traditional ViBe, from the aspects of initial sample modeling, pheromone and ant colony update mechanism,\nand foreground segmentation criterion. Experimental results show that the Ant_ViBe greatly improved the noise resistance under\ndynamic background, eased the ghost and targets adhesion problem, and surpassed the typical algorithms and their fusion\nalgorithms in most evaluation indexes....
The Industrial Internet of Things (IIoT) is of strategic importance in the new era of industrial big data, creating a brand-new\nindustrial ecosystem. Considering the unknown parameters in the IIoT-based industrial process control systems, this paper\ncombines the artificial fish swarm algorithm (AFSA) and the particle filtering (PF) algorithm into the AFSA-PF algorithm based\non the self-organizing state space (SOSS) model.TheAFSA-PF algorithm not only can estimates the system state but also can make\nthe sampling distribution of the unknown parameter to move the true parameter distribution. Ultimately, the true values of the\nunknown parameters are identified. In this way, the system model can gradually approximate the actual IIoT-based industrial\nprocess control system....
The paper proposes an algorithm based on the Multi-State Constraint Kalman Filter (MSCKF) algorithm to construct the map for\nrobots special in the poor GPS signal environment. We can calculate the position of the robots with the data collected by inertial\nmeasurement unit and the features extracted by the camera with MSCKF algorithm in a tight couple way. The paper focuses on the\nway of optimizing the position because we adopt it to compute Kalman gain for updating the state of robots. In order to reduce the\nprocessing time, we design a novel fast Gaussâ??Newton MSCKF algorithm to complete the nonlinear optimization. Compared with\nthe performance of conventional MSCKF algorithm, the novel fast-location algorithm can reduce the processing time with the\nkitti datasets....
At present, the image mining is mainly based on its local and key features, which focuses on its texture and statistical grayscale\nfeatures, but it focuses on its edge and shape features rarely. However, the contour is also an important feature for image shape\nrecognition. In this paper, a good target image contour coding algorithm was adopted, and an LCV segmentation model with good\nimage boundary acquisition capability that can reflect the target image contour features was selected for the original image\ncontour segmentation. Thedetailed features analysis of the contour coding algorithm was carried out through the experiments; the\nexperimental results showed that the algorithm was a significant technological breakthrough in image feature extraction\nand recognition....
The Distributed Queuing (DQ) algorithm is predicted as one of the solutions\nto the issues currently found in IoT networks over the use of Aloha based algorithms.\nSince recently, the algorithm has been of interest to many IoT researchers\nas a replacement of those Aloha variants for channel access. However,\nprevious works analyzed and evaluated the DQ algorithm without any\nconsideration of the stability of its queues, assuming it is stable for any given\nnumber of nodes in the network. In this paper, we define the DQ stability\ncondition in a single-channel M2M environment considering a traffic model\nof periodic and urgent frames from each node in the network. Besides, a steadystate\nevaluation of the algorithmâ??s performance metrics is also presented. In\ngeneral, the DQ algorithm, when it is stable, was observed not to efficiently\nuse the contention slots for the collision resolution. In a single-channel environment,\nthe DQ algorithm is found to outperform the Aloha based algorithms\nonly in an idle-to-saturation scenario....
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