Segmenting moving objects with a recurrent Stochastic neural network

被引：0
|
作者：
Zhao, J [1 ]
机构：
[1] Ningbo Univ, CST Res, Ningbo 315211, Peoples R China
来源：
NEURAL INFORMATION PROCESSING | 2004年 / 3316卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Moving object segmentation with a neural network is a challenging task. In this paper, we have proposed an effective optimization scheme for dynamic image segmentation and computer vision using a large scale recurrent stochastic binary neural network. The model is formally defined and can be described as a Markov Random Field. Related theoretical results and Bayesian interpretation are presented. Experimental results on real world video images illustrate the effectiveness of the approach.
引用
收藏
页码：666 / 672
页数：7
相关论文
共 50 条
  • [1] An improved neural network for segmenting objects' boundaries in real images
    Leow, WK
    Lua, SC
    1997 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, 1997, : 1663 - 1668
  • [2] Stochastic graph recurrent neural network
    Yan, Tijin
    Zhang, Hongwei
    Li, Zirui
    Xia, Yuanqing
    NEUROCOMPUTING, 2022, 500 : 1003 - 1015
  • [3] Optimizing the Neural Network Detector of Moving Objects
    D. V. Svitov
    S. A. Alyamkin
    Optoelectronics, Instrumentation and Data Processing, 2021, 57 : 18 - 26
  • [4] Optimizing the Neural Network Detector of Moving Objects
    Svitov, D. V.
    Alyamkin, S. A.
    OPTOELECTRONICS INSTRUMENTATION AND DATA PROCESSING, 2021, 57 (01) : 18 - 26
  • [5] Stochastic Recurrent Neural Network for Speech Recognition
    Chien, Jen-Tzung
    Shen, Chen
    18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION, 2017, : 1313 - 1317
  • [6] Interactive deep learning method for segmenting moving objects
    Wang, Yi
    Luo, Zhiming
    Jodoin, Pierre-Marc
    PATTERN RECOGNITION LETTERS, 2017, 96 : 66 - 75
  • [7] Segmenting moving objects from a freely moving camera with an effective segmentation cue
    Wu, Mingjun
    Peng, Xianrong
    Zhang, Qiheng
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2011, 22 (02)
  • [8] Visual tracking of moving objects using a neural network controller
    Schram, G
    vanderLinden, FX
    Krose, BJA
    Groen, FCA
    ROBOTICS AND AUTONOMOUS SYSTEMS, 1996, 18 (03) : 293 - 299
  • [9] Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network
    Chen, Rui
    Chen, Mingjian
    Li, Wanli
    Guo, Naikun
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2020, 9 (02)
  • [10] Evaluation of the reliability of the transport network based on the stochastic flow of the moving objects
    Wu, Weiwei
    Ning, Xuanxi
    Proceedings of the Fifth International Conference on Information and Management Sciences, 2006, 5 : 88 - 91
12345