Research on a multi‑dimensional image information fusion algorithm based on NSCT transform

Traditional inspection cameras determine targets and detect defects by capturing images of their light intensity,but in complex environments,the accuracy of inspection may decrease.Information based on polarization of light can characterize various features of a material,such as the roughness,texture,and refractive index,thus improving classification and recognition of targets.This paper uses a method based on noise template threshold matching to denoise and preprocess polarized images.It also reports on design of an image fusion algorithm,based on NSCT transform,to fuse light intensity images and polarized images.The results show that the fused image improves both subjective and objective evaluation indicators,relative to the source image,and can better preserve edge information and help to improve the accuracy of target recognition.This study provides a reference for the comprehensive application of multi-dimensional optical information in power inspection.

Defect detection on button surfaces with the weighted least-squares model

Defect detection assurance on production lines machine-vision-based surface is important in quality This paper presents a fast defect detection method using the weighted least-squares model. We assume that an inspection image can be regarded as a combination of a defect-free template image and a residual image. The defect-free template image is generated from training samples adaptively, and the residual image is the result of the subtraction between each inspection image and corresponding defect-free template image. In the weighted least-squares model, the residual error near the edge is suppressed to reduce the false alarms caused by spatial misalignment. Experiment results on different types of buttons show that the proposed method is robust to illumination vibration and rotation deviation and produces results that are better than those of two other methods.

A similarity-guided segmentation model for garbage detection under road scene

The development of computer vision technology provides a possible path for realizing intelligent control of road sweepers to reduce energy waste in urban street cleaning work.For garbage segmentation of seven categories under road scene,we introduce an efficient deep-learning-based method.Our model follows a lightweight structure with a feature pyramid attention(FPA)module employed in the decoder to enhance feature integration at multi-levels.Besides,a similarity guidance(SG)module is added to the decoder branches,which calculates the cosine distance between learned prototypes and feature maps to guide the segmentation results from a metric learning perspective.Our model has less than 3 M parameters and can run at over 65 FPS in an RTX 2070 GPU.Experimental results demonstrate that our method can yield competitive results in terms of speed and accuracy trade-off,with overall mean intersection-over-union(mIoU)reaching 0.87 and 0.67,respectively,on two garbage data sets we built.Besides,our model can perform acceptable category-balanced segmentation from less than 20 annotated images per category by introducing the SG module.

Robot visual guide with Fourier-Mellin based visual tracking

Robot vision guide is an important research area in industrial automation,and image-based target pose estimation is one of the most challenging problems.We focus on target pose estimation and present a solution based on the binocular stereo vision in this paper.To improve the robustness and speed of pose estimation,we propose a novel visual tracking algorithm based on Fourier-Mellin transform to extract the target region.We evaluate the proposed tracking algorithm on online tracking benchmark-50(OTB-50)and the results show that it outperforms other lightweight trackers,especially when the target is rotated or scaled.The final experiment proves that the improved pose estimation approach can achieve a position accuracy of 1.84 mm and a speed of 7 FPS(frames per second).Besides,this approach is robust to the variances of illumination and can work well in the range of 250-700 lux.

Improved accuracy of superpixel segmentation by region merging method

Superpixel as an important pre-processing technique has been successfully used in many vision applications. In this paper, we proposed a region merging method to improve superpixel segmentation accuracy with low computational cost. We first segmented the image into many accurate small regions, and then progressively agglomerated them until the desired region number was reached. The region merging weight was derived from a novel energy function, which encourages the superpixel with color consistency and similar size. Experimental results on the Berkeley BSDS500 data set showed that our region merging method can significantly improve the accuracy of superpixel segmentation. Moreover, the region merging method only need 50ms to process a 481x321 image on a single Intel i3 CPU at 2.5 GHz.