2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献

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Sliding Mode Control for Fin Stabilizer System of Marine Vessels with PID Sliding Surface 基于PID滑动面的船舶减摇鳍滑模控制

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965507

Q. Nguyen, Tie-shan Li, Liang'en Yuan, Qihe Shan, Yuchi Cao

引用次数: 0

Remote Sensing Image Classification Method Based on Preferential Adaptive Interval-Value Fuzzy C-Means 基于优先自适应区间值模糊c均值的遥感图像分类方法

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965521

Guozheng Feng, Jindong Xu, Baode Fan, Tianyu Zhao, Meng Zhu, Xiao Sun, Jin Zhou, Shiyuan Han

{"title":"Remote Sensing Image Classification Method Based on Preferential Adaptive Interval-Value Fuzzy C-Means","authors":"Guozheng Feng, Jindong Xu, Baode Fan, Tianyu Zhao, Meng Zhu, Xiao Sun, Jin Zhou, Shiyuan Han","doi":"10.1109/SPAC46244.2018.8965521","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965521","url":null,"abstract":"The heterogeneity of objects within the class and the ambiguity of objects between the classes in remote sensing images cause the uncertainty in ground objects classification. Fuzzy set theory could express the fuzziness effectively, while interval-value data model can reflect the uncertainty of the data. Therefore, combining the interval-value data model and fuzzy c-means algorithm, a preferential adaptive interval-value fuzzy c-means (PA-IVFCM) algorithm is proposed in this paper. The overall interval width of the category is adjusted by normalizing mean square error in the class, the interval modeling of the data is selected by using the preferential factor dynamically, thereby increasing the intra-class compactness and the boundary separability. The experimental results show that PA-IVFCM method can be effectively applied in the SPOT5 remote sensing data classification, and the overall classification accuracy and Kappa coefficients are greatly improved compared with the existing popular fuzzy classification methods.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127366704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pixels Matching in No Obvious Feature Area in Binocular Vision Based on Peripheral Feature Points 基于周边特征点的双目视觉无明显特征区域像素匹配

2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965563

Renlong Chen, Mingjun Liu, Xueyan Gong, Jinping Li

{"title":"Pixels Matching in No Obvious Feature Area in Binocular Vision Based on Peripheral Feature Points","authors":"Renlong Chen, Mingjun Liu, Xueyan Gong, Jinping Li","doi":"10.1109/SPAC46244.2018.8965563","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965563","url":null,"abstract":"In binocular vision, the pixel matching of no obvious feature refers to the matching of pixels in the area where the gray value does not change significantly or in the area where there is no significant gradient change in the gray level. The basic idea is that the position of the matching pixels in the area with no obvious features can be determined by the peripheral feature points. First, the camera is calibrated by the chessboard calibration method, and the internal and external parameters of the camera are obtained. The distortion of the left and right images is corrected by the calibration results. Then, the SIFT algorithm is used to extract the feature points, the Euclidean distance threshold is set to determine the better matching points, and the random sample consensus (RANSAC) algorithm is used to eliminate the wrong matching points. Finally, four target matching points with the same distance and direction information are obtained, and the final target matching points are obtained by averaging the four target matching points. Experiments show that any pair of pixels in the unmarked area can be matched accurately.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125774191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

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