General Research on Image Segmentation Algorithms

Автор: Qingqiang Yang, Wenxiong Kang

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 1 vol.1, 2009 года.

Бесплатный доступ

As one of the fundamental approaches of digital image processing, image segmentation is the premise of feature extraction and pattern recognition. This paper enumerates and reviews main image segmentation algorithms, then presents basic evaluation methods for them, and finally discusses the prospect of image segmentation. Some valuable characteristics of image segmentation come out based on a large number of comparative experiments.

Comparative research, Image segmentation, edge detection, thresholding techniques, the evaluation of image segmentation

Короткий адрес: https://sciup.org/15011946

IDR: 15011946

Список литературы General Research on Image Segmentation Algorithms

  • Tan Zhiming. Research on Graph Theory Based Image Segmentation and Its Embedded Application[D]. Shanghai: Dissertation of Shanghai Jiao Tong University, 2007, 14-24.
  • Chen Tianhua. Digital Image Processing. Beijing: Tsinghua University Press, 2007.
  • Gong Shenrong, Liu Chunping, Wang Qiang. Digital Image Processing, and Analysis. Beijing: Tsinghua University Press, 2006.
  • Rafael C, Gonzalez, Richard E Woods. Digital Image Processing(Second Edition) . Beijing: Publishing House of Electronics Industry, 2007.
  • Wang Xingcheng. Advanced Image Processing Technique. Beijing: Chinese Scientific & Technological Press, 2000.
  • Liu Ping. A Survey on Threshold Selection of Image Segmentation. Journal of Image and Graphics,2004.
  • Otsu N. Discriminant and Least Square Threshold Selection. In: Proc 4IJCPR, 1978, 592-596.
  • Kittler J, Illingworth J. On Threshold Selection Using Clustering Criteria. IEEE Trans, 1985, SMC-15: 652-655.
  • Wang Kejun, Ding Yuhang, Zhuang Dayan, Wang Dazhen. Threshold Segmentation for Hand Vein Image. Control Theory and Application, 2005, 24(8):19-22
  • Liu He. Digital Image Processing and Application. Beijing: China Electric Power Press, 2006.
  • Chen Guo. The Fisher Criterion Function Method of Image Thresholding. Chinese Journal of Scientific Instrument, 2003, 24(6):564-567.
  • Liang Dong, Li Xinhua. A Method of Automatic Thresholding Based on Artificial Intelligence.Microelectronics & Computer,1999, (1):2-5.
  • Zhu Xuan, Li Lan, Peng Jinye. Application of Fractal Dimension to the Binary Sketch of Grey Image. Journal of Chinese Computer Systems, 2001, 22(8): 961-963.
  • Xie Fengying, Jiang Zhiguo, Zhou Fugen. Immune Cell Image Segmentation Based on Mathematical Morphology. Journal of Image and Graphics. 2002,7(11):1119-1122.
  • Wang Qiaoping. One Image Segmentation Technique Based on Wavelet Analysis in the Context of Texture. Data Collection and Processing, 1998,13(1):12-16.
  • Alan Bovik. Handbook of Image and Video Processing (Second Edition). Beijing: Publishing House of Electronics Industry, 2006.
  • Lee S U, Chung S Y, Park R H. A Comparative Study of Global Thresholding Techniques for Segmentation. Computer Vision, Graphics and Image Processing, 1990(52): 171-190.
  • Rosenfeld A, Torre P De La. Histogram Concavity Analysis as An Aid in Threshold Selection. IEEE Trans, 1983, SMC-13(2): 231-235.
  • Wang Runsheng. Image Comprehension. Changsha:National Defense Science & Technology University Press, 1995.
  • Pun T. A New Method for Gray-level Picture Thresholding Using the Entropy of Histogram. Signal Processing, 1980(2): 223-237.
  • Kapur J N, Sahoo P K, Wong A K C. A New Method for Graylevel Picture Thresholding Using the Entropy Theory. Computer Vision, Graphics and Image Processing, 1985(29): 273-285.
  • S K Pal, R A King, A A Hashim. Automatic Gray Level Thresholding Through Index of Fuzziness and Entropy. Pattern Recognition Letters, 1983(1): 141-146.
  • W Tsai. Moment-preserving Thresholding: A New Approach. Computer Vision, Graphics and Image Processing, 1985(29): 377-393.
  • Olivo J C. Automatic Threshold Selection Using the Wavelet Transform. CVGIP-GMIP, 1994, 5(1): 3-14.
  • Bouman C A, Shapiro M. A Multiscale Random Field Model for Bayesian Image Segmentation. IEEE Transactions on Image Processing , 1994(2):162-177.
  • Huang Q. Quantitative Methods of Evaluating Image Segmentation [J] . Image Processing, 1995(3):23-26.
  • Zhang Yujin J, Gerbrands J J. Transition Region Determination Based Thresholding[J] . Pattern Recognition Letters, 1991(12):13-23.
  • Zhang Yujin. Image Engineering in China and Some Current Research Focuses. Journal of Computer Aided Design & Computer Graphics, 2002, 14(6):489-500.
  • Cattleman Kenneth R. Digital Image Processing. Prentice Hall, 1998.
  • Claudio Rosito Jung. Multiscale Image Segmentation Using Wavelets and Watersheds. IEEE, 2003, Computer Graphics and Image Processing, SIBGRAPI XVI Brazilian Symposium:278-284.
  • Chen Wufan. Wavelet Analysis and Its Application on Image Processing. Beijing:Science Press, 2002.
  • Huang Daren, Bi Lin, Sun Xin. Multi-band Wavelets Analysis. Hangzhou: Zhejiang University Press, 2001.
  • Sharon E. Hierarchy and Adaptivity in Segmenting Visual Scenes. Nature, 2006, 442(17):810-813.
  • Shi J, Malik, J. Normalized Cuts and Image Segmentation. IEEE TransMachine Intell, 2000(22):888-905.
  • Gao Xinbo. Fuzzy Cluster Analysis and Application [M]. Sian: Xidian University Press, 2004.
  • Jiang Lei, Yang Wenhui. A Modified Fuzzy C-Means Algorithm for Segmentation of Magnetic Resonance Images[C] . Techniques and Applications, 2003, 10-12.
  • Brink A D. Thresholding of Digital Images Using of Two-Dimensional Entropies[J] . Pattern Recognition, 1992, 25(8):803-808.
  • Zhang Aihua. The Research of Image Segmentation Based on Fuzzy Clustering [D]. Wuhan: A Dissertation of Huazhong University of Science and Technology, 2004, 1-14.
  • Elder J H, Zucher S W. Local Scale Control for Edge Detection and Blur Estimation. IEEE Trans on Pattern Analysis and Machine Intelligence, 1998, 20(7):699-715.
  • Kittler J, Illingworth J. Minimum Error Thresholding. Pattern Recognition, 1986, 19(1): 41-47.
  • Zhou Liping, Gao Xinbo. Image Segmentation via Fast Fuzzy CMeans Clustering [J]. Computer Engineering and Application, 2004, 40(8):68-70.
  • Eschrich T, Ke Jingwei. Fast Fuzzy Clustering of Infrared Images[C]. Proc of the 9th IFSA World Congress and 20th NAFIPS International Conference, 2001:1-6.
  • Brink A D. Thresholding of Digital Images Using of Two-Dimensional Entropies[J] . Pattern Recognition, 1992, 25(8):803-808.
  • Chen H D,Chen J R,LI Jiguang. Threshold Selection Based on Fuzzy C-Partition Entropy Approach[J] . Pattern Recognition, 1998, 31(7):857-870.
  • Pal B, Pal S K. A Review on Image Segmentation Techniques[J]. Pattern Recognition, 1993, 26(9):1277-1294.
  • Carvalho B M, Gau C J, Herman G T, et al. Algorithms for Fuzzy Segmentation[J] . Pattern Analysis & Applications, 1999, 2(1):73-81.
  • Selvathi D, Arulmuragn A, Selvi T, et al. MRI Image Segmentation Using Unsupervised Clustering Techniques[C] . Proc of the 6th International Conference on Computational Intelligence and Multimedia Applications(ICCIMA’05), 2005:105-110.
  • Sharon E., Brandt A. Basri, R. Fast Multiscale Image Segmentation. Proc of IEEE Conference on Computer Vision, 2000(1):70-77.
  • Galun M., Sharon E., Basri R. & Brandt. Texture Segmentation by Multiscale Aggregation of Filter Responses and Shape Elements. Computer Vision Proceedings of Ninth IEEE International Conference, 2003 (1):469-476.
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