As a kind of low-dimensional visual structural feature, edge helps to highlight the basic information of the image, playing the key role in the pre-processing of subsequent advanced visual tasks. Edge detection models with VGG16 as the basic framework can achieve excellent performance through transfer learning, but such models suffer from problems such as large number of parameters and high computational costs. To address the challenge of the coexistence of accuracy and lightweight, an edge detection network inspired by the characteristics of biological visual receptive fields (BVRF-Net) was proposed in the paper. In the Global Pathway, the ON/OFF type convolution kernel was constructed by simulating the ganglionic ON/OFF centroid type receptive field, initializing the convolution kernel by pixel difference value. Meanwhile, simulating the sparse suppression property of complex neurons, the sparse surround suppression convolution kernel with center periphery adjustment was constructed to suppress the texture noise. In the Local Pathway, the asymmetric orientation selectivity convolution kernel was constructed by simulating the orientation selectivity and asymmetric surround suppression characteristics of primary visual cortex neurons, which helps to quickly optimize the orientation features and enhance the contrast features by giving the convolution kernel a priori knowledge, achieving the precise extraction of local detail features. Taking BSDS500 dataset, NYUD-v2 dataset and Multicue dataset as experimental objects, BVRF-Net can achieve competitive results with only 0.358M parameters required. The excellent performance proves the effectiveness of incorporating bio-vision characteristics to construct neural network models, promoting the development of biomimetic computational vision.

Computer-aided medical image segmentation helps to assist physicians in locating lesion area for the subsequent diagnosis and treatment. Due to the irregular shape of the target and the uneven sample size between the target and the background area, automatic segmentation of medical images is a challenging task. Many CNN-Based, Transformer-Based models deepen the number of network layers or introduce complex modules in order to improve the segmentation accuracy. Limited by the computational resources, these types of large models are not suitable for the actual clinical environment. Inspired by the rapidity, accuracy, and low consumption characteristics of bio-visual processing, the Ultra-Lightweight Network Inspired by Bio-Visual Interaction (BVI-Net) is constructed in this paper. The Global Pathway is constructed by simulating the dorsal stream, in order to extract global features rapidly, and the Local Pathway is constructed by simulating the ventral stream, in order to process local features finely. At the same time, the skip connection module integrating Graph Convolutional Network (GCN) attention mechanism is constructed to simulate the synchronous integration ability of the visual pathway for multi-level features. The International Skin Imaging Collaboration (ISIC) dataset, the Liver Tumor Segmentation (LiTS) dataset, and the Brain Tumor Segmentation Challenge (BraTS) dataset are used for experiments. The BVI-Net proposed in this paper requires only 0.026M parameters to achieve the excellent performance in three representative medical image segmentation datasets, which has certain advantages over state-of-the-art (SOTA) methods. The biological vision mechanism and the artificial intelligence algorithm are integrated in this paper, which provides new ideas for the construction of biological vision-guided deep learning models and promotes the development of biomimetic computational vision.

Abstract Based on the coding mechanism and interactive features of visual information in the visual pathway, a new method of image contour detection is proposed. Firstly, simulating the visual adaptation characteristics of retinal ganglion cells, an adaptation & sensitization regulation model (ASR) based on the adaptation-sensitization characteristics is proposed, which introduces a sinusoidal function curve modulated by amplitude, frequency and initial phase to dynamically adjusted color channel response information and enhance the response of color edges. Secondly, the color antagonism characteristic is introduced to process the color edge responses, and the obtained primary contour responses is fed forward to the dorsal pathway across regions. Then, the coding characteristics of the “angle” information in the V2 region are simulated, and a double receptive fields model (DRFM) is constructed to compensate for the missing detailed contours in the generation of primary contour responses. Finally, a new double stream information fusion model (DSIF) is proposed, which simulates the dorsal overall contour information flow by the across-region response weighted fusion mechanism, and introduces the multi-directional fretting to simulate the fine-tuning characteristics of ventral detail features simultaneously, extracting the significant contours by weighted fusion of dorsal and ventral information streams. In this paper, the natural images in BSDS500 and NYUD datasets are used as experimental data, and the average optimal F-score of the proposed method is 0.72 and 0.69, respectively. The results show that the proposed method has better results in texture suppression and significant contour extraction than the comparison method.