Gao Jie

Bio: Gao Jie is an academic researcher. The author has contributed to research in topics: Algorithm design & The Internet. The author has an hindex of 1, co-authored 1 publications receiving 39 citations.

An Improved Adaptive Threshold Canny Edge Detection Algorithm

Abstract: It has proposed an adaptive threshold edge detection algorithm in this paper, which applies the bilateral filtering that has the advantages of edge-preserving and noise-removing firstly. Then it uses OTSU, which is based on gradient magnitude to maximize the separability of the resultant classes, to determine the low and high thresholds of the canny operator. Finally, the edge detection and connection are performed. The experimental results show that this algorithm is practical and reliable.

Dynamic Monitoring Method of Abnormal Intrusion of Distribution Terminals for Power Internet of Things

Abstract: : At this stage, the security protection system of power IOT distribution terminals lacks effective anomaly intrusion detection methods, and the distribution terminals of power IOT have the characteristics of large number and wide range, which plays an important role in maintaining the normal operation of power IOT. Once the distribution terminal encounters illegal intrusion, it can lead to the damage of the important infrastructure of the distribution terminal of the power Internet of things, and pose a great security threat to the stable operation of the power Internet of things. This paper analyzes the content of building the network flow order of distribution terminals for the power Internet of things. From the aspects of basic attribute selection, data processing, building the network flow order, attribute classification, abnormal feature extraction, and realizing abnormal monitoring, it deeply discusses the abnormal intrusion dynamic monitoring method of the network flow order for the power Internet of things, in order to accurately monitor the abnormal intrusion dynamic data and enhance the abnormal intrusion dynamic monitoring effect, Improve the level of abnormal intrusion dynamic monitoring technology, and effectively ensure the safe and stable operation of power distribution terminals of the Internet of things.

Image Registration and Fusion of Visible and Infrared Integrated Camera for Medium-Altitude Unmanned Aerial Vehicle Remote Sensing

Abstract: This study proposes a novel method for image registration and fusion via commonly used visible light and infrared integrated cameras mounted on medium-altitude unmanned aerial vehicles (UAVs).The innovation of image registration lies in three aspects. First, it reveals how complex perspective transformation can be converted to simple scale transformation and translation transformation between two sensor images under long-distance and parallel imaging conditions. Second, with the introduction of metadata, a scale calculation algorithm is designed according to spatial geometry, and a coarse translation estimation algorithm is presented based on coordinate transformation. Third, the problem of non-strictly aligned edges in precise translation estimation is solved via edge–distance field transformation. A searching algorithm based on particle swarm optimization is introduced to improve efficiency. Additionally, a new image fusion algorithm is designed based on a pulse coupled neural network and nonsubsampled contourlet transform to meet the special requirements of preserving color information, adding infrared brightness information, improving spatial resolution, and highlighting target areas for unmanned aerial vehicle (UAV) applications. A medium-altitude UAV is employed to collect datasets. The result is promising, especially in applications that involve other medium-altitude or high-altitude UAVs with similar system structures.

VU: Edge Computing-Enabled Video Usefulness Detection and its Application in Large-Scale Video Surveillance Systems

Abstract: In the era of smart and connected communities, video surveillance systems, which typically involve tens to thousands of cameras, have increasingly become prominent components for public safety. In current practice, when a failure occurs in a video surveillance system, the operation and maintenance teams usually spend a substantial amount of time locating and identifying the failure; hence, the fast online response cannot be guaranteed in a large-scale video surveillance system. Meanwhile, the video data that contains potential failures consumes bandwidth that could be used for useful video data. The useless video will waste the scarce bandwidth in the network and storage usage in the cloud. The emergence of edge computing is highly promising in video preprocessing with an edge camera. A video surveillance system is a killer application for edge computing. In this article, we propose an edge computing-enabled video usefulness (i.e., VU) model for large-scale video surveillance systems. We also explore its application, e.g., early failure detection and bandwidth improvement. According to the usefulness of the video data, the VU model can locate a failure and send it to end-users on the fly. In this article, our goals are threefold: 1) proposing a comprehensive VU model. To the best of our knowledge, this is the first work to explore the feasibility of the VU model and to determine VU values in a real application; 2) reducing the mean time to detection (i.e., MTTD) efficiently via edge computing-enabled fast online failure detection approaches; and 3) relieving the network bandwidth for large-scale video surveillance systems. Our experimental results demonstrate the approaches in VU model accurately detect failures that were collected from a video surveillance system with approximately 4000 cameras. The MTTD is substantially shortened by the fast online detection approaches. The video data with the worst VU values is mostly discarded to lessen overload of the network.

An Adaptive Threshold Based on Multiple Resolution Levels for Canny Edge Detection

Abstract: Machine vision requires detectors to obtain the characteristics and the nature of the object in the image The Canny edge detection method is the most recognisable technique that combines a low-pass Gaussian filter to reduce noise and oppression instead of the maximum threshold and hysteresis for localisation advantages One of the problems encountered in the Canny approach is in the selection of a threshold value Using a single fixed threshold value for the maximum gradient is not the optimal choice Therefore, the Canny approach uses two threshold values, a high threshold and a low threshold to reduce the number of false positive pixels, representing the contours in the image significantly However, using two fixed threshold values is also not the best option because of the high variation in the image Although adaptive thresholds have been introduced, they are only used for specific types of images In this paper, we introduce a method that computes the threshold values from the foreground and background image pixels from global and local image analysis According to this method, an image is divided into several blocks using multiple resolution levels After that, a modification sampling approach is used on global and local regions to get the optimal thresholds by selecting the highest between the class variance values Experiments have been done on four different types of dataset images which are Berkeley, DRIVE, Persian and CASIA V2 datasets The results show that the proposed method outperforms the Canny method and other adaptive methods

Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods

Abstract: Hydrophobicity is an important parameter to characterize electrical properties of insulated materials. Therefore, it is an urgent task to develop on-line instruments to identify the hydrophobicity of insulated material's surface conveniently, quickly and accurately. For this purpose, a novel evaluation system with image processing and decision tree is proposed which is based on embedded platform. For obtaining satisfactory results, we first propose a mixed image segmentation method to overcome the complex conditions outside, concerning non-controlled illumination, nonstandard surfaces and unfixed shooting angle. Then we adopt four new characteristic parameters to describe the image of each sample. Finally, a classification method based on MultiBoost decision tree is conducted which synthesizes the merits of both AdaBoost and Wagging algorithm. Results indicate the procedures can be applied in the DSP (Digital Signal Processor) platform perfectly and better results can be obtained than those did in our previous study or that of some other research.

Implementation of Canny Edge Detection Algorithm on FPGA and displaying Image through VGA Interface

Abstract: Edge detection is one of the most important stages in image processing. The Canny edge detection algorithm is most widely used edge detection algorithm because of it advantages. In this paper we present canny edge detection algorithm implemented on Spartan 3E FPGA and developed VGA interfacing for displaying images on the screen. In this paper we have taken 128×128 Image and displayed same on the monitor through FPGA.