Low-level brightness-contrast illusions and non-classical receptive field of mammalian retina

被引:5
|
作者
Ghosh, K [1 ]
Sarkar, S [1 ]
Bhaumik, K [1 ]
机构
[1] Saha Inst Nucl Phys, Microelect Div, Kolkata 700064, W Bengal, India
关键词
brightness-contrast illusions; modified DoG model; ganglion cell; non-classical receptive field;
D O I
10.1109/ICISIP.2005.1529511
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Among the visual illusions there is a class, it which is known as low-level brightness contrast illusions. These illusions are processed probably, at the retinal ganglion cell without necessitating any intervention from higher order cortical processing. The concept of classical receptive field of the ganglion cell, derived from earh physiological studies, prompted the idea that a "Difference of Goussian " or DoG model ntight reproduce the output of the ganglion cell. In spite of it's many successes, the DoG model fails to explain some of these low level illusions. On the basis of recently available physiological data, we have modified the DoG model and have shown the efficacy of the modified model in understanding the low level illusions, a phenomenon that many have potential application in designing new robust visual captiaing or dislplay systems.
引用
收藏
页码:529 / 534
页数:6
相关论文
共 44 条
  • [21] EFFECTIVE DETECTION OF SEISMIC EVENTS BY NON-CLASSICAL RECEPTIVE FIELD VISUAL COGNITIVE MODELLING
    Zhao, Jing
    Lei, Haojie
    Li, Yang
    Ren, Jinchang
    Sun, Genyun
    Zhao, Huiminn
    Shen, Hongyan
    Wang, Daxing
    JOURNAL OF SEISMIC EXPLORATION, 2023, 32 (04): : 385 - 406
  • [22] Contour detection based on the contextual modulation of non-classical receptive field facilitation and suppression
    Xiao, Jie
    Guo, Zhaoli
    Cai, Chao
    MIPPR 2013: AUTOMATIC TARGET RECOGNITION AND NAVIGATION, 2013, 8918
  • [23] Multi-scale Image Analysis Based on Non-Classical Receptive Field Mechanism
    Wei, Hui
    Zuo, Qingsong
    Lang, Bo
    NEURAL INFORMATION PROCESSING, PT III, 2011, 7064 : 601 - 610
  • [24] Contour detection model with multi-scale integration based on non-classical receptive field
    Wei, Hui
    Lang, Bo
    Zuo, Qingsong
    NEUROCOMPUTING, 2013, 103 : 247 - 262
  • [25] Non-classical receptive field properties reflecting functional aspects of optimal spike based inference
    Timm Lochmann
    Sophie Denève
    BMC Neuroscience, 10 (Suppl 1)
  • [26] Blind S3D image quality prediction using classical and non-classical receptive field models
    Liu, Lixiong
    Zhang, Jiufa
    Saad, Michele A.
    Huang, Hua
    Bovik, Alan Conrad
    SIGNAL PROCESSING-IMAGE COMMUNICATION, 2020, 87
  • [27] Correction: Corrigendum: Non-classical receptive field mediates switch in a sensory neuron's frequency tuning
    Maurice J. Chacron
    Brent Doiron
    Leonard Maler
    André Longtin
    Joseph Bastian
    Nature, 2003, 423 : 1018 - 1018
  • [28] Contour detection based on a non-classical receptive field model with butterfly-shaped inhibition subregions
    Zeng, Chi
    Li, Yongjie
    Yang, Kaifu
    Li, Chaoyi
    NEUROCOMPUTING, 2011, 74 (10) : 1527 - 1534
  • [29] Convective storms and non-classical low-level jets during high ozone level episodes in the Amazon region: An ARM/GOAMAZON case study
    Dias-Junior, Cleo Q.
    Dias, Nelson Luis
    Fuentes, Jose D.
    Chamecki, Marcelo
    ATMOSPHERIC ENVIRONMENT, 2017, 155 : 199 - 209
  • [30] Non-classical receptive-field inhibition and its relation to orientation-contrast pop-out and line and contour salience: A computational approach
    Petkov, N
    Westenberg, MA
    PERCEPTION, 2004, 33 : 68 - 68