A Goal-Directed Visual Perception System Using Object-Based Top-Down Attention

被引:14
作者
Yu, Yuanlong [1 ]
Mann, George K. I. [1 ]
Gosine, Raymond G. [1 ]
机构
[1] Mem Univ Newfoundland, Fac Engn & Appl Sci, St John, NF A1B 3X5, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Cognitive visual perception; goal-directed; integrated competition hypothesis; object-based visual attention; top-down visual attention; PROBABILISTIC NEURAL-NETWORKS; ACTIVE-VISION; COMPUTER VISION; GAZE CONTROL; MODEL; ROBOTS; REPRESENTATION; ARCHITECTURE; MECHANISMS;
D O I
10.1109/TAMD.2011.2163513
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The selective attention mechanism is employed by humans and primates to realize a truly intelligent perception system, which has the cognitive capability of learning and thinking about how to perceive the environment autonomously. The attention mechanism involves the top-down and bottom-up ways that correspond to the goal-directed and automatic perceptual behaviors, respectively. Rather than considering the automatic perception, this paper presents an artificial system of the goal-directed visual perception by using the object-based top-down visual attention mechanism. This cognitive system can guide the perception to an object of interest according to the current task, context and learned knowledge. It consists of three successive stages: preattentive processing, top-down attentional selection and post-attentive perception. The preattentive processing stage divides the input scene into homogeneous proto-objects, one of which is then selected by the top-down attention and finally sent to the post-attentive perception stage for high-level analysis. Experimental results of target detection in the cluttered environments are shown to validate this system.
引用
收藏
页码:87 / 103
页数:17
相关论文
共 50 条
  • [41] Perceptual learning to reduce sensory eye dominance beyond the focus of top-down visual attention
    Xu, Jingping P.
    He, Zijiang J.
    Ooi, Teng Leng
    VISION RESEARCH, 2012, 61 : 39 - 47
  • [42] Weld seam profile extraction using top-down visual attention and fault detection and diagnosis via EWMA for the stable robotic welding process
    He, Yinshui
    Yu, Zhuohua
    Li, Jian
    Ma, Guohong
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 104 (9-12) : 3883 - 3897
  • [43] Dynamic, object-based remapping of visual features in trans-saccadic perception
    Melcher, David
    JOURNAL OF VISION, 2008, 8 (14):
  • [44] Unseeing the White Bear: Negative Search Criteria Guide Visual Attention Through Top-Down Suppression
    Forstinger, Marlene
    Gruener, Markus
    Ansorge, Ulrich
    JOURNAL OF EXPERIMENTAL PSYCHOLOGY-HUMAN PERCEPTION AND PERFORMANCE, 2022, 48 (06) : 613 - 638
  • [45] Functional size of human visual area V1: A neural correlate of top-down attention
    Verghese, Ashika
    Kolbe, Scott C.
    Anderson, Andrew J.
    Egan, Gary F.
    Vidyasagar, Trichur R.
    NEUROIMAGE, 2014, 93 : 47 - 52
  • [46] Model of Top-Down/Bottom-UP Visual Attention for Location of Salient Objects in Specific Domains
    Benicasa, Alcides X.
    Zhao, Liang
    Romero, Roseli A. F.
    2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2012,
  • [47] Expectation and attention increase the integration of top-down and bottom-up signals in perception through different pathways
    Gordon, Noam
    Tsuchiya, Naotsugu
    Koenig-Robert, Roger
    Hohwy, Jakob
    PLOS BIOLOGY, 2019, 17 (04)
  • [48] The normalization model predicts responses in the human visual cortex during object-based attention
    Doostani, Narges
    Hossein-Zadeh, Gholam-Ali
    Vaziri-Pashkam, Maryam
    ELIFE, 2023, 12
  • [49] Robot Behavior Selection Using Salient Landmarks and Object-based Attention
    Liu, Dong
    Cong, Ming
    Du, Yu
    Gao, Sen
    2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO), 2013, : 1101 - 1106
  • [50] Exploiting Visual Saliency Algorithms for Object-Based Attention: A New Color and Scale-Based Approach
    Ardizzone, Edoardo
    Bruno, Alessandro
    Gugliuzza, Francesco
    IMAGE ANALYSIS AND PROCESSING (ICIAP 2017), PT II, 2017, 10485 : 191 - 201