3D growing deformable B-surface model for object detection

被引:0
|
作者
Chen, XJ [1 ]
Teoh, EK [1 ]
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A new method, called 3D growing deformable B-Surface model, is proposed for object detection which works in 3D space directly. First, the coarse boundary of the subject is extracted. The 3D external force eld pound of the subject is generated based on this coarse boundary using modi ed pound GVF (gradient vector mow). After the initialization of a surface patch, growing B-Surface model starts to deform it to locate the boundary of the object. Next, this surface patch is anchored to the surface of the subject and a new surface patch grows up based it. This process is repeated until a closed surface of the subject is obtained. 3D growing deformable B-Surface model overcomes the dif culty pound that comes from analyzing 3D volume image slice by slice. And the computation load of B-Surface is reduced since the internal force is not necessary in every iteration deformation step. Next, the geometric information on every surface point can be calculated easily. And it has the ability to achieve high compression ratio (ratio of data to parameters) by presenting the whole surface with only a relatively small number of control points. Growing B-Surface model simplifes the initialization step of the surface model.
引用
收藏
页码:357 / 362
页数:6
相关论文
共 50 条
  • [21] Persistence-based Interest Point Detection for 3D Deformable Surface
    Wang, Xupeng
    Sohel, Ferdous
    Bennamoun, Mohammed
    Guo, Yulan
    Lei, Hang
    PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 1, 2017, : 58 - 69
  • [22] Growing deformable surface patches for topology-adaptive object detection in MR images
    Chen, XJ
    Teoh, EK
    MEDICAL IMAGING 2005: IMAGE PROCESSING, PT 1-3, 2005, 5747 : 1491 - 1500
  • [23] A Lightweight Model for 3D Point Cloud Object Detection
    Li, Ziyi
    Li, Yang
    Wang, Yanping
    Xie, Guangda
    Qu, Hongquan
    Lyu, Zhuoyang
    APPLIED SCIENCES-BASEL, 2023, 13 (11):
  • [24] Joint 3D Tracking of a Deformable Object in Interaction with a Hand
    Tsoli, Aggeliki
    Argyros, Antonis A.
    COMPUTER VISION - ECCV 2018, PT XIV, 2018, 11218 : 504 - 520
  • [25] Globally constrained deformable models for 3D object reconstruction
    Montagnat, J
    Delingette, H
    SIGNAL PROCESSING, 1998, 71 (02) : 173 - 186
  • [26] Height-Adaptive Deformable Multi-Modal Fusion for 3D Object Detection
    Li, Jiahao
    Chen, Lingshan
    Li, Zhen
    IEEE ACCESS, 2025, 13 : 52385 - 52396
  • [27] Construction of 3D human distal femoral surface models using a 3D statistical deformable model
    Zhu, Zhonglin
    Li, Guoan
    JOURNAL OF BIOMECHANICS, 2011, 44 (13) : 2362 - 2368
  • [28] Robust shape estimation for 3D deformable object manipulation
    Han, Tao
    Zhao, Xuan
    Sun, Peigen
    Pan, Jia
    COMMUNICATIONS IN INFORMATION AND SYSTEMS, 2018, 18 (02) : 107 - 124
  • [29] Graph-Based Deformable 3D Object Matching
    Drost, Bertram
    Ilic, Slobodan
    PATTERN RECOGNITION, GCPR 2015, 2015, 9358 : 222 - 233
  • [30] DVST: Deformable Voxel Set Transformer for 3D Object Detection from Point Clouds
    Ning, Yaqian
    Cao, Jie
    Bao, Chun
    Hao, Qun
    REMOTE SENSING, 2023, 15 (23)