会议论文集名称：
International Conference on Systems and Informatics
关键词：
Feature point detection;Compressed sensing theory;Target tracking;PID control;Quadcopter
摘要：
Aiming at the problems of poor tracking accuracy, inadequate anti-jamming ability and unavoidable monitoring unreachable angle in the traditional tracking method of four-rotor unmanned aerial vehicle (UAV), based on compressed sensing theory and combined with FAST (Features From Accelerated Segment Test) and SURF (Speeded Up Robust Features) algorithm, this paper proposes a fast matching algorithm for multi-target detection and single-target tracking of UAV. We built a matching onboard hardware system, collected data through the camera, and then used feature point detection and matching algorithms to detect multiple moving objects. Finally, we used compressed sensing theory to quickly locate the tracked objects. Compared with the traditional algorithm, this algorithm needs much less time to achieve tracking in the same scene than the general tracking algorithm, reaching the millisecond level, and the tracking loss rate is only 5% for the object whose area is less than 250*256 pixels in the image, which greatly improves the tracking accuracy and anti-jamming performance.
作者机构：
[Shi, Jian] Post Doctoral Mobile Station of Clinical Medicine, Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China;[Feng, Li] School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510006, China;[Liu, Bo] Department of Internet of Things Engineering, School of Information Science and Technology, Hunan Agricultural University, Changsha, Hunan, 410128, China;[Deng, Yunlong] Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
关键词：
Early recognition;Management model;Mental symptoms;Neural network algorithm
摘要：
Early intervention in time helps to improve the prognosis of schizophrenia, and it is particularly important to improve the identification and diagnosis of psychiatric risk syndrome. In this paper, the author analyse the early recognition and efficacy evaluation of mental disorders based on cultural algorithm neural network. Early assessment and intervention can effectively reduce the risk of occurrence of dangerous behaviour. In summary, through the establishment of integrated prevention and control management model, and the early identification and intervention of risk behaviours of mental patients in the study area, we can make the patients with dangerous behaviour spirit get timely, systematic, scientific and effective treatment management in the community.
摘要：
The rapid development of many open source and commercial image editing software makes the authenticity of the digital images questionable. Copy-move forgery is one of the most widely used tampering techniques to create desirable objects or conceal undesirable objects in a scene. Existing techniques reported in the literature to detect such tampering aim to improve the robustness of these methods against the use of JPEG compression, blurring, noise, or other types of post processing operations. These post processing operations are frequently used with the intention to conceal tampering and reduce tampering clues. A robust method based on the color moments and other five image descriptors is proposed in this paper. The method divides the image into fixed size overlapping block. Clustering operation divides entire search space into smaller pieces with similar color distribution. Blocks from the tampered regions will reside within the same cluster since both copied and moved regions have similar color distributions. Five image descriptors are used to extract block features, which makes the method more robust to post processing operations. An ensemble of deep compositional pattern-producing neural networks are trained with these extracted features. Similarity among feature vectors in clusters indicates possible forged regions. Experimental results show that the proposed method can detect copy-move forgery even if an image was distorted by gamma correction, addictive white Gaussian noise, JPEG compression, or blurring. (C) 2016 Published by Elsevier Ireland Ltd.
