计算机科学
编码(社会科学)
视皮层
计算机视觉
人工智能
光流
投射试验
模式识别(心理学)
计算机图形学(图像)
神经科学
图像(数学)
数学
心理学
统计
作者
Zhefei Cai,Rui Yang,Yingle Fan,Wei Wu
出处
期刊:IEEE Transactions on Cognitive and Developmental Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-06-14
卷期号:16 (2): 660-670
被引量:2
标识
DOI:10.1109/tcds.2023.3285909
摘要
Based on the information flow partition projection characteristics of P-type and M-type ganglion cells (referred to as P cells and M cells) and the neural sparse coding mechanism, this article proposed a new method for image contour detection. First, we considered the difference between M cells and P cells in detail sensitivity and the information transmission of different visual signals. The parallel visual pathway was constructed to simulate the prelevel characteristics of the V1 layer to obtain the primary contour response. Then, we introduced the orientation sensitivity and stimulus response difference of the visual receptive field to construct the visual information difference enhancement model. In consideration of the visual attention mechanism, we proposed an adaptive size sparse coding network model that simulates the prelevel characteristics of the V4 layer to intelligently focus the target contour features. At the same time, de-redundancy was performed to obtain the fine feature image. Finally, the hierarchical information feedback fusion was built, and the fine feature image was used to correct the primary contour response to obtain complete contour detection results. Taking the BSDS500 dataset as experimental objects, the results showed that the proposed method exhibits an effective tradeoff between contour extraction and texture suppression.
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