The mechanism of orientation detection based on color-orientation jointly selective cells

This paper discusses the visual mechanism of global orientation detection and the realization of a mechanism-based artificial visual system for two-dimensional orientation detection tasks. For interpretation and practicability, we introduce the visual mechanism into the design of a detection system. We first propose an orientation detection mechanism according to the color-orientation jointly selectivity cortical neuron character. We assume that part of the orientation detection tasks is completed by the color-orientation jointly selective cells that are only responsible for orientation detection locally. Each cell can only be activated by stimuli with a specific orientation angle and the preferred color. We realize these cells by the McCulloch-Pitts neuron model and extend them to a two-dimensional version. In each local receptive field, there are four separate color-orientation jointly selective cells responsible for orientation detection, and their optimal responsive color corresponds to the central location’s color. Every local region connects such a set of cells. Subsequently, by these sets of these cells, we can collect all local information and obtain the global orientation according to the local activations. The type of local orientation angle recognized the most corresponds to the global orientation. Finally, a mechanism-based artificial visual system (AVS) is implemented. Several simulations and comparative experiments are provided to verify the effectiveness and generalization of the proposed orientation detection scheme and the superiority of the AVS to popular classification networks in orientation detection tasks. In addition, the feature extraction ability of AVS is shown to accelerate the learning and noise immunity of neural networks. •Referred to the cortical neuron features, a novel orientation detection mechanism based on color-orientation jointly selective cells was proposed.•The mechanism-based artificial visual system for orientation detection is practical, reasonable, and efficient.•The artificial visual system shows significant advantages against popular classification networks on orientation detection tasks.•The orientation detection mechanism can be used in learning networks to preprocess data for promoting networks’ noise immunity and robustness.•The hardware implementation of the artificial visual system for orientation detection is efficient and straightforward.