An improved neural architecture for gaze movement control in target searching

This paper presents an improved neural architecture for gaze movement control in target searching. Compared with the four-layer neural structure proposed in [14], a new movement coding neuron layer is inserted between the third layer and the fourth layer in previous structure for finer gaze motion estimation and control. The disadvantage of the previous structure is that all the large responding neurons in the third layer were involved in gaze motion synthesis by transmitting weighted responses to the movement control neurons in the fourth layer. However, these large responding neurons may produce different groups of movement estimation. To discriminate and group these neurons' movement estimation in terms of grouped connection weights form them to the movement control neurons in the fourth layer is necessary. Adding a new neuron layer between the third layer and the fourth lay is the measure that we solve this problem. Comparing experiments on target locating showed that the new architecture made the significant improvement.

[1]  B. Schiele,et al.  Fast and Robust Face Finding via Local Context , 2003 .

[2]  Eric O. Postma,et al.  Context-based object detection in still images , 2006, Image Vis. Comput..

[3]  Wen Gao,et al.  Top–Down Gaze Movement Control in Target Search Using Population Cell Coding of Visual Context , 2010, IEEE Transactions on Autonomous Mental Development.

[4]  Antonio Torralba,et al.  Contextual Priming for Object Detection , 2003, International Journal of Computer Vision.

[5]  Wei Zhang,et al.  The Role of Top-down and Bottom-up Processes in Guiding Eye Movements during Visual Search , 2005, NIPS.

[6]  Krista A. Ehinger,et al.  Modelling search for people in 900 scenes: A combined source model of eye guidance , 2009 .

[7]  Thierry Pun,et al.  Integration of bottom-up and top-down cues for visual attention using non-linear relaxation , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[8]  John K. Tsotsos,et al.  Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..

[9]  M. Chun,et al.  Contextual Cueing: Implicit Learning and Memory of Visual Context Guides Spatial Attention , 1998, Cognitive Psychology.

[10]  Wen Gao,et al.  A Visual Perceiving and Eyeball-Motion Controlling Neural Network for Object Searching and Locating , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[11]  Yu Fu,et al.  Learning Internal Representation of Visual Context in a Neural Coding Network , 2010, ICANN.

[12]  M. Chun,et al.  Contextual cueing of visual attention , 2022 .

[13]  J. Henderson,et al.  The effects of semantic consistency on eye movements during complex scene viewing , 1999 .

[14]  L. Itti,et al.  Modeling the influence of task on attention , 2005, Vision Research.

[15]  Lucas Paletta,et al.  Context Based Object Detection from Video , 2003, ICVS.

[16]  B. K. Julsing,et al.  Face Recognition with Local Binary Patterns , 2012 .