A fuzzy region growing approach for segmentation of color images

被引:124
|
作者
Moghaddamzadeh, A
Bourbakis, N
机构
[1] SUNY BINGHAMTON,DEPT EE,AAAI LAB,BINGHAMTON,NY 13902
[2] UNIV CRETE,DEPT ECE,KHANIA 73100,GREECE
关键词
segmentation; color segmentation; fuzzy image processing; fuzzy segmentation; region growing;
D O I
10.1016/S0031-3203(96)00084-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Segmentation is one of the most important preprocessing steps towards pattern recognition and image understanding and a significant step towards image compression and coding. With detecting edges, most of the large segments can be found and separated from others by edge pixels. It is, however, the pixels on edge locations or those in high detailed areas whose association to adjacent segments must be found A pixel can be a part of the closest segment or in association with the neighboring pixels from a new smaller segment. In this paper, two segmentation algorithms are presented. One is used for fine segmentation towards compression and coding of images and the other for coarse segmentation towards other applications like object recognition and image understanding. Edge detection and region growing approaches are combined to find large and crisp segments for coarse segmentation. Segments can grow or expand based on two fuzzy criteria. The fuzzy region growing and expanding approaches presented here use histogram tables for fine segmentation. The procedures introduced here can be used in any order or combination to yield the best result for any particular application or image type. (C) 1997 Pattern Recognition Society.
引用
收藏
页码:867 / 881
页数:15
相关论文
共 50 条
  • [41] Edge Preserving Region Growing for Aerial Color Image Segmentation
    Subudhi, Badri Narayan
    Patwa, Ishan
    Ghosh, Ashish
    Cho, Sung-Bae
    INTELLIGENT COMPUTING, COMMUNICATION AND DEVICES, 2015, 309 : 481 - 488
  • [42] Color Image Segmentation Based on Blocks Clustering and Region Growing
    Sima, Haifeng
    Liu, Lixiong
    Guo, Ping
    NEURAL INFORMATION PROCESSING, PT III, 2011, 7064 : 459 - 466
  • [43] Clustering based region growing algorithm for color image segmentation
    Cramariuc, B
    Gabbouj, M
    Astola, J
    DSP 97: 1997 13TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING PROCEEDINGS, VOLS 1 AND 2: SPECIAL SESSIONS, 1997, : 857 - 860
  • [44] A hierarchical approach to fuzzy segmentation of colour images
    Chamorro-Martínez, J
    Sánchez, D
    Prados-Suárez, B
    Galán-Perales, E
    Vila, MA
    PROCEEDINGS OF THE 12TH IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1 AND 2, 2003, : 966 - 971
  • [45] Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features
    Hu, Xinyu
    Chen, Qi
    Ye, Xuhui
    Zhang, Daode
    Tang, Yuxuan
    Ye, Jun
    SYMMETRY-BASEL, 2021, 13 (12):
  • [46] Fuzzy-based unsupervised segmentation of textured color images
    Dai, XY
    Maeda, J
    2002 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL III, PROCEEDINGS, 2002, : 293 - 296
  • [47] Adjustable fuzzy neural networks for color segmentation of map images
    Zhong, N
    Shieh, JS
    INTERNATIONAL CONFERENCE ON IMAGING SCIENCE, SYSTEMS, AND TECHNOLOGY, PROCEEDINGS, 1999, : 189 - 195
  • [48] Image Segmentation by Contextual Region Growing Based on Fuzzy Classification
    Chaibou, Mahaman Sani
    Kalti, Karim
    Solaiman, Basel
    Mahjoub, Mohamed Ali
    2016 2ND INTERNATIONAL CONFERENCE ON ADVANCED TECHNOLOGIES FOR SIGNAL AND IMAGE PROCESSING (ATSIP), 2016, : 489 - 493
  • [49] Image Segmentation by Fuzzy Edge Detection and Region Growing Technique
    Khwairakpam, Amitab
    Hazarika, Ruhul Amin
    Kandar, Debdatta
    PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON MICROELECTRONICS, COMPUTING AND COMMUNICATION SYSTEMS, MCCS 2018, 2019, 556 : 51 - 64
  • [50] Microscope cell color images segmentation by fuzzy morphological reconstruction
    Bouchet, Agustina
    Pastore, Juan I.
    Brun, Marcel
    Ballarin, Virginia L.
    12TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS, 2017, 10160