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<title cf:type="text"><![CDATA[Editorial department of the Journal of National University of Defense Technology -->智能图像处理]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Sequential image geometric correction of area array camera using equivalent bias angle sparse measurement]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202305019]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[The distortion in the imaging of space-based optical cameras for actual in-orbit earth observations needs to be suppressed by geometric correction. At present, the small size and high frame rate sequential images obtained by mainstream area array cameras for ground observation are difficult to meet the requirements of traditional geometric correction methods for calculating the number of control points and spatial distribution of frame-by-frame solution, and the computational complexity is huge. To address this issue, a geometric correction method by using equivalent bias angle sparse measurement for sequential observation image of area-array sensor was proposed. The problem of parameters solution for each frame converted to the problem of equivalent bias angle recovery under the time-domain sparse measurement. The requirement for the number and spatial distribution of control points in a single frame image were reduced by using the time-frequency information of equivalent bias angle signal. Meanwhile, the real image data from the area-array camera of Gaofen-4 satellite was used to verify the validity and low computation of the proposed method.]]></description>
<pubDate>2023/9/26 0:00:00</pubDate>
<category><![CDATA[智能图像处理]]></category>
<author><![CDATA[AN Chengjin, LI Zhen, CHEN Jun, FAN Jianpeng, MA Chen]]></author>
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<atom:name>AN Chengjin, LI Zhen, CHEN Jun, FAN Jianpeng, MA Chen</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Multilevel real-time visualization technology for large-scale geographic vector linestring data]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202305020]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Aiming at the difficulty of mainstream methods to support the multilevel real-time visualization of large-scale geographic vector linestring data, a multilevel real-time visualization technique for large-scale geographic vector linestring data was proposed. An adaptive visualization model for multilevel tile rendering was established, and a PQR (pixel-quad-R) tree spatial index and an adaptive visualization algorithm based on PQR-tree were designed to support the data organization and visualization of the model, respectively. Experiments on billion-scale datasets show that the technique can calculate visualization results at any zoom level within 0.57 s. Meanwhile, its visualization time is significantly less than mainstream methods. When the data scale increases sharply, the technology still has good visualization performance at each zoom level, and the lowest visualization rate exceeds 100 tiles/s, which is much better than mainstream methods. The technique can support multilevel real-time visualization of large-scale geographic vector linestring data in the single machine, and has a good application prospect in the field of exploration and analysis of spatial big data.]]></description>
<pubDate>2023/9/26 0:00:00</pubDate>
<category><![CDATA[智能图像处理]]></category>
<author><![CDATA[LIU Zebang, CHEN Luo, MA Mengyu, YANG Anran, ZHONG Zhinong, JING Ning]]></author>
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<atom:name>LIU Zebang, CHEN Luo, MA Mengyu, YANG Anran, ZHONG Zhinong, JING Ning</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Intelligent detection method of ROP chain using two-dimensional feature of byte pattern]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202305021]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[ROP(return oriented programming) attack is an important method for network attackers to break through the protection of operating system and realize vulnerability attacks, and ROP chain is the main component of ROP attack. In order to detect the ROP chain in network traffic, an intelligent detection method that can automatically extract the characteristics of ROP chain and has good generalization performance was proposed. The sequential extraction method was adopted to divide the measured network traffic into multiple sequences, one-dimensional traffic data was converted into two-dimensional feature vectors by using sliding window and numerical quantization, and the detection of ROP chain was realized based on the convolution neural network model. Different from the existing static detection methods, the proposed method did not rely on the context information of the program memory address, was simple to implement, easy to deploy, and had excellent detection performance. The experimental results show that the highest accuracy rate of the model is 99.4%, the false negative rate is 0.6%, the false positive rate is 0.4%, the time cost is within 0.1 s, and the false negative rate for the real ROP attack traffic is 0.2%.]]></description>
<pubDate>2023/9/26 0:00:00</pubDate>
<category><![CDATA[智能图像处理]]></category>
<author><![CDATA[WANG Jian, HUANG Kaijie, ZHANG Mengjie, LIU Xingtong, YANG Gang]]></author>
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<atom:name>WANG Jian, HUANG Kaijie, ZHANG Mengjie, LIU Xingtong, YANG Gang</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Compression method for three-dimensional point cloud deep model]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202305022]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[With the widespread application of computer three-dimensional vision, point cloud processing algorithms based on deep learning have attracted a lot of research in recent years, however, the time and storage consuming characteristics have greatly restricted its deployment and application on the mobile terminal devices. Based on the general idea of improving the loss function, a new point cloud deep model compression framework was proposed, and the knowledge distillation method was introduced into the binary quantization model. At the same time, considering the speciality of the point cloud aggregation operation, an auxiliary loss item was introduced. The improved loss function includes three parts:prediction loss, distillation loss and auxiliary loss. The experimental results show that, compared with the existing algorithms, the proposed algorithm can obtain higher accuracy, meanwhile, the application to current mainstream point cloud deep network models can also achieve good scalability.]]></description>
<pubDate>2023/9/26 0:00:00</pubDate>
<category><![CDATA[智能图像处理]]></category>
<author><![CDATA[ZHAO Zhi, XU Ke, MA Yanxin, WAN Jianwei]]></author>
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<atom:name>ZHAO Zhi, XU Ke, MA Yanxin, WAN Jianwei</atom:name>
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