Relevant bibliographies by topics / Fuzzy mean-shift

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Fuzzy mean-shift'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Fuzzy mean-shift"

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Li, Qinghua, Serkan Ural, John Anderson, and Jie Shan. "A Fuzzy Mean-Shift Approach to Lidar Waveform Decomposition." IEEE Transactions on Geoscience and Remote Sensing 54, no. 12 (2016): 7112–21. http://dx.doi.org/10.1109/tgrs.2016.2596105.

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HAN, Cai-xia, and Xiao-yu WANG. "Image smoothing based on Mean-shift algorithm and fuzzy entropy." Journal of Computer Applications 29, no. 1 (2009): 63–64. http://dx.doi.org/10.3724/sp.j.1087.2009.00063.

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Shen, Yue, and Xiao-jun Wu. "Improvement Algorithm of Mean Shift Based on Fuzzy Subordination Relations." Journal of Electronics & Information Technology 30, no. 11 (2011): 2688–92. http://dx.doi.org/10.3724/sp.j.1146.2007.00675.

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Tan, Dayu, Weimin Zhong, Chao Jiang, Xin Peng, and Wangli He. "High-order fuzzy clustering algorithm based on multikernel mean shift." Neurocomputing 385 (April 2020): 63–79. http://dx.doi.org/10.1016/j.neucom.2019.12.030.

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kaur, Mandip. "EFFICIENT IMAGE SEGMENTTION OF BRAIN TUMOR DETECTION USING FUZZY C-MEAN AND MEAN-SHIFT." International Journal of Advanced Research in Computer Science 8, no. 7 (2017): 317–23. http://dx.doi.org/10.26483/ijarcs.v8i7.4221.

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WANG Kai, 王凯, 赵永强 ZHAO Yong-qiang, 程咏梅 CHENG Yong-mei, and 魏坤 Wei Kun. "Hyperspectral Image Segmentation Based on Mean Shift and Fuzzy Integral Fusion." ACTA PHOTONICA SINICA 39, no. 1 (2010): 188–92. http://dx.doi.org/10.3788/gzxb20103901.0188.

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Zhou, Huiyu, Gerald Schaefer, Abdul H. Sadka, and M. Emre Celebi. "Anisotropic Mean Shift Based Fuzzy C-Means Segmentation of Dermoscopy Images." IEEE Journal of Selected Topics in Signal Processing 3, no. 1 (2009): 26–34. http://dx.doi.org/10.1109/jstsp.2008.2010631.

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Wan, Shuting, and Xiong Zhang. "Bearing fault diagnosis based on teager energy entropy and mean-shift fuzzy C-means." Structural Health Monitoring 19, no. 6 (2020): 1976–88. http://dx.doi.org/10.1177/1475921720910710.

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Feature extraction and fault recognition of vibration signals are two important parts of bearing fault diagnosis. In this article, a fault diagnosis method based on teager energy entropy of each wavelet subband and improved fuzzy C-means is proposed. First, bearing vibration signal is decomposed into wavelet packet and normalized teager energy entropy feature matrix is constructed as clustering index. Principal component analysis is applied to the high-dimensional teager energy entropy feature matrix, and the principal components are determined by cumulative contribution rate to construct feat
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Li, Yong, Jing Wen Xu, Jun Fang Zhao, Yu Dan Zhao, and Xin Li. "An Improved Mean Shift Segmentation Method of High-Resolution Remote Sensing Image Based on LBP and Canny Features." Applied Mechanics and Materials 713-715 (January 2015): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1589.

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Mean shift algorithm is a robust approach toward feature space analysis, which has been wildly used for natural scene image and medical image segmentation. Due to fuzzy boundary and low accuracy of Mean shift segmentation method, this paper puts forward to an improved Mean shift segmentation method of high-resolution remote sensing image based on LBP and Canny features. The results show that this improved Mean shift segmentation access can enhance segmentation accuracy compared to the traditional Mean shift.
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Phadke, Gargi, and Rajbabu Velmurugan. "Mean LBP and modified fuzzy C-means weighted hybrid feature for illumination invariant mean-shift tracking." Signal, Image and Video Processing 11, no. 4 (2016): 665–72. http://dx.doi.org/10.1007/s11760-016-1008-0.

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Dissertations / Theses on the topic "Fuzzy mean-shift"

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Chang, Hao-Shiu, and 張豪修. "A Mean-Shift Video Object Tracking Algorithm Based on Fuzzy Clustering." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/31027298757261755864.

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碩士<br>義守大學<br>資訊工程學系碩士班<br>95<br>With the popularization of video products and the rapid progress in visual techniques in recent years, the computer systems with video object tracking technology have been applied in various fields in daily life, such as surveillance systems, intelligent traffic control systems, parking lot management systems, and so on. The computer visual systems not only replace manpower in a large number of traditional jobs but also can avoid the errors induced from human tiredness. Meanwhile, it has the ability to timely report back the accident, therefore the time costs o
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(5929958), Qinghua Li. "Geospatial Processing Full Waveform Lidar Data." Thesis, 2019.

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This thesis focuses on the comprehensive and thorough studies on the geospatial processing of airborne (full) waveform lidar data, including waveform modeling, direct georeferencing, and precise georeferencing with self-calibration.<div><br></div><div>Both parametric and nonparametric approaches of waveform decomposition are studied. The traditional parametric approach assumes that the returned waveforms follow a Gaussian mixture model where each component is a Gaussian. However, many real examples show that the waveform components can be neither Gaussian nor symmetric. To address the problem,
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Τσάμπρας, Λάμπρος. "Κατηγοροποίηση μαγνητικών τομογραφιών με DSPs". Thesis, 2014. http://hdl.handle.net/10889/8282.

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Είναι ενδιαφέρουσα αλλά συνάμα δύσκολη η ανάλυση ιατρικών εικόνων, επειδή υπάρχουν πολύ μικρές διακυμάνσεις και μεγάλος όγκος δεδομένων για επεξεργασία. Είναι αρκετά δύσκολο να αναπτυχθεί ένα αυτοματοποιημένο σύστημα αναγνώρισης, το οποίο θα μπορούσε να επεξεργάζεται μεγάλο όγκο πληροφοριών των ασθενών και να παρέχει μια σωστή εκτίμηση. Στην ιατρική, η συμβατική διαγνωστική μέθοδος για εικόνες MR γονάτου για αναγνώριση ανωμαλιών, είναι από την επίβλεψη έμπειρων ιατρών. Η τεχνική της ασαφούς λογικής είναι πιο ακριβής, αλλά αυτό εξαρτάται πλήρως από τη γνώση των εμπειρογνωμόνων, η οποία μπορεί ν
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Book chapters on the topic "Fuzzy mean-shift"

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Münch, David, Eckart Michaelsen, and Michael Arens. "Supporting Fuzzy Metric Temporal Logic Based Situation Recognition by Mean Shift Clustering." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33347-7_21.

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Dadgostar, Farhad, Abdolhossein Sarrafzadeh, and Scott P. Overmyer. "Face Tracking Using Mean-Shift Algorithm: A Fuzzy Approach for Boundary Detection." In Affective Computing and Intelligent Interaction. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11573548_8.

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Abdel Maksoud, Eman A., Mohammed Mahfouz Elmogy, and Rashid Mokhtar Al-Awadi. "Segmentation of Brain Tumor from MRI Images Based on Hybrid Clustering Techniques." In Handbook of Research on Machine Learning Innovations and Trends. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch006.

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The popularity of clustering in segmentation encouraged us to develop a new medical image segmentation system based on two-hybrid clustering techniques. Our medical system provides an accurate detection of brain tumor with minimal time. The hybrid techniques make full use of merits of these clustering techniques and overcome the shortcomings of them. The first is based on K-means and fuzzy C-means (KIFCM). The second is based on K-means and particle swarm optimization (KIPSO). KIFCM helps Fuzzy C-means to overcome the slow convergence speed. KIPSO provides global optimization with less time. It helps K-means to escape from local optima by using particle swarm optimization (PSO). In addition, it helps PSO to reduce the computation time by using K-means. Comparisons were made between the proposed techniques and K-means, Fuzzy C-means, expectation maximization, mean shift, and PSO using three benchmark brain datasets. The results clarify the effectiveness of our second proposed technique (KIPSO).
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Conference papers on the topic "Fuzzy mean-shift"

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Ju, Ming-Yi, Chen-Sen Ouyang, and Hao-Shiu Chang. "Mean shift tracking using fuzzy color histogram." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580780.

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Zhiqiang Wen, Zhigao Zeng, and Wenqiu Zhu. "Kernel optimization strategy based on mean shift." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382180.

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Wang, Zhaohui, Chunping Liu, and Shengrong Gong. "Tracking multi-objects using combination feature and Mean Shift." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6233772.

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Liao, Yun, Hua Zhou, Zhihong Liang, Yin Zhang, JunHui Liu, and Lei Su. "Tracking target based on particle filtering and Mean Shift." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019516.

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Kang, Hoon, Seung Hwan Lee, and Jayong Lee. "Image segmentation based on fuzzy flood fill mean shift algorihm." In NAFIPS 2010 - 2010 Annual Meeting of the North American Fuzzy Information Processing Society. IEEE, 2010. http://dx.doi.org/10.1109/nafips.2010.5548413.

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Zhou, Yong-mei, Sheng-yi Jiang, and Mei-lin Yin. "A Region-Based Image Segmentation Method with Mean-Shift Clustering Algorithm." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.363.

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Zhou, Huiyu, Gerald Schaefer, Abdul Sadka, and M. Emre Celebi. "Anisotropic mean shift based fuzzy c-means segmentation of skin lesions." In the 5th international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1456223.1456313.

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Huiyu Zhou, Gerald Schaefer, and Chunmei Shi. "A mean shift based fuzzy c-means algorithm for image segmentation." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649857.

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Singh, Baljinder, and Pankaj Aggarwal. "Detection of brain tumor using modified mean-shift based fuzzy c-mean segmentation from MRI Images." In 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2017. http://dx.doi.org/10.1109/iemcon.2017.8117123.

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Lv, Lixia, and Chenghan Yan. "The mean shift tracking algorithm with adaptive bandwidth based on affine transformation." In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2015. http://dx.doi.org/10.1109/fskd.2015.7382184.

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