A contour detection method combing dual visual pathway and scale information fusion
Objective The extraction and utilization of contour information,as a low-level visual feature of the target sub-ject,contribute to the efficient execution of advanced visual tasks,such as object detection and image segmentation.When processing complex images,contour detection based on biological vision mechanisms can quickly extract object contour information.However,the perception of primary contour information is currently based on a single scale receptive field template or a simple fusion of multiple scale receptive field templates,which ignores the dynamic characteristics of recep-tive field scales and makes it difficult to accurately extract contours in complex scenes.Considering the serial parallel trans-mission and integration mechanism of visual information in the magnocellular(M)and parvocellular(P)dual vision path-ways,we propose a new contour detection method based on the fusion of dual vision pathway scale information.Method First,we introduce Lab,a color system that is close to human visual physiological characteristics,to extract color differ-ence and brightness information from an image.Compared with conventional RGB color systems,Lab is more in line with the way the human eye perceives visual information.Considering that the scale of the receptive field of ganglion cells varies with the size of local stimuli to adapt to different visual task requirements across various scenes,a smaller scale of the receptive field corresponds to a more refined perception of detailed information.We then simulate the fuzzy and fine percep-tion of the stimuli by ganglion cells using two different scale receptive fields,and we use color difference and brightness contrast information to guide the adaptive fusion of large-and small-scale receptive field responses and highlight the contour details.Second,considering the differences in the perception of orientation information among receptive fields at different scales of the lateral geniculate body,we introduce the standard deviation of the optimal orientation obtained from percep-tion at multiple scales as the encoding weight for the direction difference,thereby achieving a modulation of texture region suppression weight information.We also combine local contrast information to guide the lateral inhibition intensity of non-classical receptive fields based on the difference between the central and peripheral directions.Through the collaborative integration of these two,we successfully enhance the contour regions and suppress the background textures.Finally,to simulate the complementary fusion mechanism of color and brightness information in the primary visual cortex(V1)region,we propose a weight association model integrating contrast information.Based on the fusion weight coefficients obtained from the local color contrast and brightness contrast,we achieve a complementary fusion of information flows in the M and P paths,thereby enriching the contour details.Result We compared our model with three biological-vision-based mecha-nisms(SCSI,SED,and BAR)and one deep-learning-based model(PiDiNet).On the BSDS500 dataset,we used several quantitative evaluation indicators,including optimal dataset scale(ODS),optimal image scale(OIS),average precision(AP)indicators,and precision-recall(PR)curves,and selected five images to compare the detection performance of each method.Experimental results show that our model has a better overall performance than the other models.Compared with SCSI,SED,and BAR,our model obtains 4.45％,2.94％,and 4.45％higher ODS index,2.82％,5.80％,and 8.96％higher OIS index,and 7.25％,4.23％,and 5.71％higher AP index,respectively.While the PiDiNet model based on deep learning has some shortcomings compared with various indicators,this model does not require a pre-training of data,has biological interpretability,and has a small computational power requirement.We further extracted four images from the NYUD dataset to visually compared the false detection rate,missed detection rate,and overall performance of the models.We also conducted a series of ablation experiments to demonstrate the contribution of each module in the model to its over-all performance.Conclusion In this paper,we use the M and P dual-path mechanism and the encoding process of lumi-nance and color information in the front-end visual path to realize contour information processing and extraction.Our pro-posed approach can effectively realize a contour detection of natural images,especially for subtle contour edge detection in images,and provide novel insights for studying visual information mechanism in the higher-level cortex.
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