|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Overview of Digital Image Restoration显示文摘Image restoration is an image processing technology with great practical value in the field of computer vision.It is a computer technology that estimates the image information of the damaged area according to the residual image information of the damaged image and carries out automatic repair.This article firstly classify and summarize image restoration algorithms,and describe recent advances in the research respectively from three aspects including image restoration based on partial differential equation,based on the texture of image restoration and based on deep learning,then make the brief analysis of digital image restoration of subjective and objective evaluation method,and briefly summarize application of digital image restoration technique in the future and prospects,provide direction for the research on image after repair. | Wei Chen Tingzhu Sun Fangming Bi Tongfeng Sun Chaogang Tang Biruk Assefa | 2019 | Journal of New Media2019，1，1： | 0 |
| 2 | Ecological Optimization Design Methods for the Green Space System in Densely Built-up Areas显示文摘This paper is aimed at studying the environmental degradation of densely built-up areas in the process of urbanization in China. In consideration of the severe environmental conditions of the densely built-up areas, such as the lack of green space and open space, ecological disturbance in some areas, poor landscape quality, this paper focused on the ecological space optimization in the process of urban renewal. Firstly, theories related to this field were analyzed, and a comprehensive ecological efficiency evaluation system was established based on disciplines such as urban ecology, landscape ecology, urban sociology, behavioral psychology, biology, urban planning and design. Secondly, this system was used to judge the ecological efficiency of typical blocks on GIS platform and to find out the key spatial nodes that need to be updated. Thirdly, in different cases, space optimization projects with different theories were designed, and the spatial model of influence was used to comprehensively evaluate their ecological efficiency. Finally, the parameters under different conditions were corrected to get a systematic system for evaluating the green space system in densely built-up areas. Due to the lack of understanding of the ecological function of green space in the past, the environmental condition of densely built-up areas is not good. Therefore, the most important task of urban organic renewal is ecological restoration. In this paper, the exploration is based on the reservation for built-up areas to avoid repeated reconstruction and interference. Authors of this paper tried to find out a way to rebuild green space system that performed more complex functions with limited spatial resources. The application of 'micro-transformation' of green space system in densely built-up areas turns out to improve the quality of landscape while reducing the construction cost. | BI Linglan ZHANG Fuwen ZHANG Fangming | 2018 | Journal of Landscape Research2018，10，4： | 0 |
| 3 | Fire Detection Method Based on Improved Fruit Fly Optimization-Based SVM显示文摘Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is proposed.The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire interferences.Secondly,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved.Then,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire flame.Finally,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were obtained.The video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios. | Fangming Bi Xuanyi Fu Wei Chen Weidong Fang Xuzhi Miao Biruk Assefa | 2020 | Computers, Materials & Continua2020，，1： | 0 |
| 4 | Review on Video Object Tracking Based on Deep Learning显示文摘Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so on.Therefore, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space. | Fangming Bi Xin Ma Wei Chen Weidong Fang Huayi Chen Jingru Li Biruk Assefa | 2019 | Journal of New Media2019，1，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员