Object Segmentation Using Block Based Patterns

被引:0
|
作者
Hasan, M. Mahmudul [1 ]
Ali, M. Ameer [1 ]
Kabir, M. Humayun [2 ]
Sorwar, G. [3 ]
机构
[1] East West Univ, Dept CSE, Dhaka, Bangladesh
[2] Bangladesh Univ Engn & Technol, Dept CSE, Dhaka, Bangladesh
[3] Southern Cross Univ, Sch Commerce & Management, Lismore, NSW 2480, Australia
关键词
Image segmentation; split-and-merge; region stability; pattern matching; micro-blocks; video coding; ALGORITHM;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Segmenting homogeneous regions or objects in an image are very much demanding but challenging. Pattern based object segmentation using split and merge (PSM) was proposed to overcome the problems of basic split and merge (SM) algorithm, which is unable to segment properly all types of objects in an image due to huge variations among the objects in size, shape, intensity and orientation. Though the PSM algorithm has better performance than some other image segmentation algorithms, it is completely unable to segment the connected regions in an image and also has higher rate of shape distortion. Addressing these issues, a new algorithm namely object segmentation using block based patterns (OSP) is proposed in this paper considering multi stage merging technique. Experimental results show that the OSP algorithm is not only capable of segmenting connected regions in an image but also yield quite low shape distortion of the regions.
引用
收藏
页码:1375 / +
页数:2
相关论文
共 50 条
  • [41] Gaze-Based Object Segmentation
    Shi, Ran
    Ngan, Ngi King
    Li, Hongliang
    [J]. IEEE SIGNAL PROCESSING LETTERS, 2017, 24 (10) : 1493 - 1497
  • [42] Video Object Segmentation Based on Disparity
    Xingming, Ouyang
    Wei, Wei
    [J]. ADVANCES IN WEB AND NETWORK TECHNOLOGIES, AND INFORMATION MANAGEMENT, 2009, 5731 : 36 - 44
  • [43] Neural network based object recognition using color block matching
    Boehnke, Kay
    Otesteanu, Marius
    Roebrock, Philipp
    Winkler, Wolfgang
    Neddermeyer, Wemer
    [J]. PROCEEDINGS OF THE FOURTH IASTED INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, PATTERN RECOGNITION, AND APPLICATIONS, 2007, : 122 - +
  • [44] Object based Image Splicing Localization using Block Artificial Grids
    Sekhar, P. N. R. L. Chandra
    Shankar, T. N.
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (11) : 511 - 517
  • [45] Object Detection Using Adaptive Block-based Background Model
    Tsai, Wen-Kai
    Chen, Jian-Hui
    Sheu, Ming-Hwa
    Sun, Chi-Chia
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS-TAIWAN (ICCE-TW), 2016, : 45 - 46
  • [46] Object Segmentation Based on Disparity Estimation
    Zhang, Qian
    Liu, Suxing
    An, Ping
    Zhang, Zhaoyang
    [J]. WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09), 2009, : 1053 - 1056
  • [47] Automatic Object Segmentation Based on GrabCut
    Jiang, Feng
    Pang, Yan
    Lee, ThienNgo N.
    Liu, Chao
    [J]. ADVANCES IN COMPUTER VISION, CVC, VOL 1, 2020, 943 : 350 - 360
  • [48] Minimal surfaces based object segmentation
    Caselles, V
    Kimmel, R
    Sapiro, G
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1997, 19 (04) : 394 - 398
  • [49] Camouflaged Object Segmentation Based on COSNet
    Jiang X.
    Cai W.
    Zhang Z.
    Jiang B.
    Yang Z.
    Wang X.
    [J]. Binggong Xuebao/Acta Armamentarii, 2023, 44 (05): : 1456 - 1468
  • [50] Demodulation Using Block and Data Flow Based Object Oriented Approach
    Sever, Murat
    Tavli, Bulent
    [J]. 2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2020,