Abstract:

In the rapid recognition of visually significant targets in images， the presence of a large amount of noise in the image can interfere with the detection of significant targets due to factors such as changes in lighting conditions and background complexity， resulting in poor recognition robustness. To this end， this article introduces machine vision technology and uses Fourier transform filtering technology to enhance the original image， improve its robustness to factors such as lighting changes and background complexity， enhance its resistance to noise， and improve the robustness of target recognition. According to the Fourier transform filtering technique in machine vision， the original image is unfolded and processed to generate a gradient map， completing the enhancement of the original image. By using a linear model of multiple adjacent pixels to calculate the trend of slope difference， the optimal threshold is determined based on the measured values of slope difference distribution. The morphological iterative erosion method is introduced to effectively distinguish the target area from the noise area， achieving high-definition segmentation of the image. Adopting a multi-scale analysis strategy to divide the image into multiple superpixel regions of varying numbers， calculating the color mean of pixels within each superpixel， and achieving abstract representation of the image. Based on the characteristics of salient features， the mean saliency of superpixels at various scales is calculated and fused to obtain the visual salient object recognition results of the image. The results show that the CM of the proposed algorithm can reach 0.597 7， UM can reach 0.913 2， and the target recognition recall rate can reach 99% under different types of noise. The proposed algorithm has good consistency， indicating that the proposed method can effectively improve the robustness of recognition results.

Key words: machine vision, visual salient targets in images, quick identification, Fourier transform filtering, gradient map