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Steganography Using Reversible Texture Synthesis Based on Seeded Region Growing and LSB 被引量:2
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作者 Qili Zhou Yongbin Qiu +4 位作者 Li Li Jianfeng Lu Wenqiang Yuan Xiaoqing Feng Xiaoyang Mao 《Computers, Materials & Continua》 SCIE EI 2018年第4期151-163,共13页
Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images... Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images,and is hard to guarantee security.To solve these problems,steganography using reversible texture synthesis based on seeded region growing and LSB is proposed.Secret information is embedded in the process of synthesizing texture image from the existing natural texture.Firstly,we refine the visual effect.Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture.We use seeded region growing algorithm to ensure texture’s similar local appearance.Secondly,the size and capacity of image can be decreased by introducing the information segmentation,because the capacity of the secret information is proportional to the size of the synthetic texture.Thirdly,enhanced security is also a contribution in this research,because our method does not need to transmit parameters for secret information extraction.LSB is used to embed these parameters in the synthetic texture. 展开更多
关键词 STEGANOGRAPHY texture synthesis LSB seeded region growing algorithm information segmentation
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SEED REGION SELECTION AND HOMOGENEITY CRITERION FOR DOORPLATE IMAGE SEGMENTATION IN MOBILE ROBOT NAVIGATION
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作者 Yang Guosheng Tan Min 《Journal of Electronics(China)》 2005年第5期505-512,共8页
Focused on the seed region selection and homogeneity criterion in Seeded Region Growing (SRG), an unsupervised seed region selection and a polynomial fitting homogeneity criterion for SRG are proposed in this paper. F... Focused on the seed region selection and homogeneity criterion in Seeded Region Growing (SRG), an unsupervised seed region selection and a polynomial fitting homogeneity criterion for SRG are proposed in this paper. First of all, making use of Peer Group Filtering (PGF) techniques, an unsupervised seed region selection algorithm is presented to construct a seed region. Then based on the constructed seed region a polynomial fitting homogeneity criterion is applied to solve the concrete problem of doorplate segmentation appearing in the robot navigation along a corridor. At last, experiments are performed and the results demonstrate the effectiveness of the proposed algorithm. 展开更多
关键词 Image segmentation Seeded Region Growing (SRG) Peer Group Filtering(PGF) Polynomial approximation
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Novel method for the visual navigation path detection of jujube harvester autopilot based on image processing 被引量:1
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作者 Xiongchu Zhang Bingqi Chen +4 位作者 Jingbin Li Xin Fang Congli Zhang Shubo Peng Yongzheng Li 《International Journal of Agricultural and Biological Engineering》 SCIE 2023年第5期189-197,共9页
To realize automatic harvesting of the jujube,the jujube harvester was designed and manufactured.For achieving the jujube harvester autopilot,a novel algorithm for visual navigation path detection was proposed.The cen... To realize automatic harvesting of the jujube,the jujube harvester was designed and manufactured.For achieving the jujube harvester autopilot,a novel algorithm for visual navigation path detection was proposed.The centerline of tree row lines was taken as the navigation path.The method included four main parts:image preprocessing,image segmentation,tree row lines access,and navigation path access.The methods of threshold segmentation,noise removal,and border smoothing were utilized on the image in Lab color space for the image segmentation.The least square method was employed to fit the tree row lines,and the centerline was obtained as the navigation path.Experimental results indicated that the average false detection rate was 3.98%,and the average detection speed was 41 fps.The algorithm meets the requirements of the jujube harvester autopilot in terms of accuracy and speed.It also can lay the foundation for accomplishing the jujube harvester vision-based autopilot. 展开更多
关键词 visual navigation path jujube orchards image processing Lab color space seed region growing
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Sea fog detection based on unsupervised domain adaptation 被引量:6
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作者 Mengqiu XU Ming WU +3 位作者 Jun GUO Chuang ZHANG Yubo WANG Zhanyu MA 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2022年第4期415-425,共11页
Sea fog detection with remote sensing images is a challenging task. Driven by the different image characteristics between fog and other types of clouds, such as textures and colors, it can be achieved by using image p... Sea fog detection with remote sensing images is a challenging task. Driven by the different image characteristics between fog and other types of clouds, such as textures and colors, it can be achieved by using image processing methods. Currently, most of the available methods are datadriven and relying on manual annotations. However, because few meteorological observations and buoys over the sea can be realized, obtaining visibility information to help the annotations is difficult. Considering the feasibility of obtaining abundant visible information over the land and the similarity between land fog and sea fog, we propose an unsupervised domain adaptation method to bridge the abundant labeled land fog data and the unlabeled sea fog data to realize the sea fog detection. We used a seeded region growing module to obtain pixel-level masks from roughlabels generated by the unsupervised domain adaptation model. Experimental results demonstrate that our proposed method achieves an accuracy of sea fog recognition up to 99.17%, which is nearly 3% higher than those vanilla methods. 展开更多
关键词 Deep learning Sea fog detection Seeded region growing Transfer learning Unsupervised domain adaptation
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An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm in PET-CT imaging 被引量:6
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作者 Juanjuan ZHAO Guohua JI +2 位作者 Xiaohong HAN Yan QIANG Xiaolei LIAO 《Frontiers of Computer Science》 SCIE EI CSCD 2016年第1期189-200,共12页
To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scann... To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages: image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography (PET-CT) data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately. 展开更多
关键词 pulmonary parenchyma segmentation bot-tom region of lung image binarization iterative threshold seeded region growing four-corner rotating and scanning denoising contour refining PET-CT
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Automatic inspection of LED indicators on automobile meters based on a seeded region growing algorithm
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作者 Hong ZHOU Hai-er XU +2 位作者 Pei-qi HE Zhi-bai SONG Chen-ge GENG 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2010年第3期199-205,共7页
Light emitting diode (LED) indicators used on automobile meters are essential for safe driving and few errors can be tolerated. The current manual inspection approach can achieve only 95% accuracy rate in weeding out ... Light emitting diode (LED) indicators used on automobile meters are essential for safe driving and few errors can be tolerated. The current manual inspection approach can achieve only 95% accuracy rate in weeding out errors occurring in the production process. It is imperative to improve the accuracy of the inspection process to better achieve the goal of safe driving. This paper proposes an automatic inspection method for LED indicators for use on automobile meters. Firstly,red-green-blue (RGB) color images of LED indicators are acquired and converted into R,G,and B intensity images. A seeded region growing (SRG) algorithm,which selects seeds automatically based on Otsu's method,is then used to extract the LED indicator regions. Finally,a region matching process based on the seed and three area parameters of each region is applied to inspect the LED in-dicators one by one to locate any errors. Experiments on standard automobile meters showed that the inspection accuracy rate of this method was up to 99.52% and the inspection speed was faster compared with the manual method. Thus,the new method shows good prospects for practical application. 展开更多
关键词 Automatic inspection Light emitting diode (LED) indicators Automobile meter Seeded region growing (SRG)
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