A biologically-inspired concept for active image recognition

被引:0
|
作者
Suri, RE [1 ]
机构
[1] Intelligent Opt Syst, Torrance, CA 90505 USA
关键词
D O I
10.1109/KIMAS.2003.1245074
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A novel concept is proposed that uses active information gathering for recognizing objects on images. This novel concept mimics recent neurobiological insights on human control of eye movements (TD algorithm). TD algorithms are predictive reinforcement algorithms for learning of action sequences. In the proposed framework, standard techniques, such as template matching, are used as processing steps. Each processing step compares a template of one part of the object with image locations and computes a value that describes how well the template matches. A TD algorithm is trained on many images to optimize the sequence of processing steps by providing feedback whether the final object recognition was correct or incorrect. After training, the algorithm searches for template matches with the sequence of templates and locations that are most promising for recognition of a certain object. This object recognition strategy resembles active information gathering by saccadic eye movements.
引用
收藏
页码:379 / 384
页数:6
相关论文
共 50 条
  • [1] Biologically-inspired image interpretation and automatic target recognition technologies
    Sheerin, D
    Doll, TJ
    Chiu, CK
    Home, R
    [J]. SIGNAL PROCESSING, SENSOR FUSION, AND TARGET RECOGNITION XII, 2003, 5096 : 210 - 221
  • [2] Accelerators for Biologically-Inspired Attention and Recognition
    Park, Mi Sun
    Zhang, Chuanjun
    DeBole, Michael
    Kestur, Srinidhi
    Narayanan, Vijaykrishnan
    Irwin, Mary Jane
    [J]. 2013 50TH ACM / EDAC / IEEE DESIGN AUTOMATION CONFERENCE (DAC), 2013,
  • [3] Biologically-inspired algorithms for object recognition
    Ternovskiy, I
    Nakazawa, D
    Campbell, S
    Suri, RE
    [J]. INTERNATIONAL CONFERENCE ON INTEGRATION OF KNOWLEDGE INTENSIVE MULTI-AGENT SYSTEMS: KIMAS'03: MODELING, EXPLORATION, AND ENGINEERING, 2003, : 364 - 367
  • [4] Biologically-inspired pattern recognition for odor detection
    Roppel, T
    Wilson, DM
    [J]. PATTERN RECOGNITION LETTERS, 2000, 21 (03) : 213 - 219
  • [5] BioNet: A Biologically-inspired Network for Face Recognition
    Li, Pengyu
    [J]. 2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2023, : 10344 - 10354
  • [6] Generic Object Recognition with Biologically-Inspired Features
    Gao, Changxin
    Sang, Nong
    Gao, Jun
    Zou, Lamei
    Tang, Qiling
    [J]. 2009 FOURTH INTERNATIONAL CONFERENCE ON BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS, PROCEEDINGS, 2009, : 37 - 43
  • [7] A Biologically-inspired Attentional Approach for Face Recognition
    Khellat-Kihel, Souad
    Tistarelli, Massimo
    [J]. 2019 7TH INTERNATIONAL WORKSHOP ON BIOMETRICS AND FORENSICS (IWBF), 2019,
  • [8] An Improved Biologically-Inspired Image Fusion Method
    Wang, Yuqing
    Wang, Yong
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2018, 32 (08)
  • [9] Color-texture image segmentation and recognition through a biologically-inspired architecture
    Antón-Rodríguez M.
    González-Ortega D.
    Díaz-Pernas F.J.
    Martínez-Zarzuela M.
    Díez-Higuera J.F.
    [J]. Pattern Recognition and Image Analysis, 2012, 22 (1) : 54 - 68
  • [10] SPEECH RECOGNITION USING BIOLOGICALLY-INSPIRED NEURAL NETWORKS
    Bohnstingl, Thomas
    Garg, Ayush
    Wozniak, Stanislaw
    Saon, George
    Eleftheriou, Evangelos
    Pantazi, Angeliki
    [J]. 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 6992 - 6996