A Contour Detection Method Based on Interactive Perception Mechanism of Dual Visual Pathways

WU Wei; HAN Xianxiu; FAN Yingle

doi:10.11999/JEIT210818

Volume 44 Issue 7

Jul. 2022

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WU Wei, HAN Xianxiu, FAN Yingle. A Contour Detection Method Based on Interactive Perception Mechanism of Dual Visual Pathways[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2512-2521. doi: 10.11999/JEIT210818

Citation:

WU Wei, HAN Xianxiu, FAN Yingle. A Contour Detection Method Based on Interactive Perception Mechanism of Dual Visual Pathways[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2512-2521. doi: 10.11999/JEIT210818

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A Contour Detection Method Based on Interactive Perception Mechanism of Dual Visual Pathways

doi: 10.11999/JEIT210818 cstr: 32379.14.JEIT210818

Laboratory of Pattern Recognition and Image Processing, Hangzhou DianZi University, Hangzhou 310018, China

Funds: The National Natural Science Foundation of China(61501154)

Received Date: 2021-08-11
Rev Recd Date: 2022-05-10

Available Online: 2022-05-20

Publish Date: 2022-07-25

Abstract

Abstract

According to the mechanism of visual information interactive perception between dual Visual Pathways(VP) in the biological vision system, a new method of contour detection is proposed. Considering the visual stimulus in the hypocritical pathway flowing through multi-level and different-scale receptive fields, a multi-scale contour fusion contour perception model is proposed. Based on the contrast adaptation mechanism and directional sensitivity of the visual pathway on the cortex, salient visual features are extracted. The interactive perception mechanism of the dual vision pathway is simulated, a pulse coding model is constructed guided by the information flow interaction in the V1 cortex, to extract the saliency contour. An inhibition model of feature modulation non-classical receptive field is proposed in the Superior Colliculus(SC) shallow layer, to achieve texture inhibition. Finally, the contour response results in the dual-view path is modified and fused to obtain the final contour response. For the test of the RUG40 image library, the optimal average P index of the whole dataset and each graph is 0.51 and 0.57 respectively. For the test of the BSDS500 image library, the Optimal Scale (ODS) of the Dataset is 0.68. The results show that the method in this paper can effectively highlight the outline of the subject and suppress the textured background, which provides a new idea for the subsequent image understanding and analysis based on the visual mechanism.
- Contour detection,
- Interactive perception,
- Contrast adaptive,
- Visual Pathway(VP)

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References(23)

References

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