Vision-based aerial image mosaicking algorithm with object detection

Aerial image sequence mosaicking is one of the chal-lenging research fields in computer vision.To obtain large-scale orthophoto maps with object detection information,we propose a vision-based image mosaicking algorithm without any extra location data.According to object detection results,we define a complexity factor to describe the importance of each input ima-ge and dynamically optimize the feature extraction process.The feature points extraction and matching processes are mainly guided by the speeded-up robust features(SURF)and the grid motion statistic(GMS)algorithm respectively.A robust refer-ence frame selection method is proposed to eliminate the trans-formation distortion by searching for the center area based on overlaps.Besides,the sparse Levenberg-Marquardt(LM)al-gorithm and the heavy occluded frames removal method are ap-plied to reduce accumulated errors and further improve the mo-saicking performance.The proposed algorithm is performed by using multithreading and graphics processing unit(GPU)accel-eration on several aerial image datasets.Extensive experiment results demonstrate that our algorithm outperforms most of the existing aerial image mosaicking methods in visual quality while guaranteeing a high calculation speed.

Gradient-based method for the identification of multi-nodes in sugarcane

In view of the obvious changes in color between the upper and lower leaf scar in sugarcane nodes,a method of simultaneous multi-nodes identification on a single sugarcane stem was proposed based on the analysis of gradient characteristics of sugarcane images.In combination with image processing and machine vision recognition technology,two cameras were used to acquire different parts of sugarcane images,and the two images were integrated into a complete image of sugarcane by image mosaicking.The Sobel operator is used to calculate the gradient of the sugarcane image in a horizontal direction,and the gradient image is obtained.The sugarcane gradient image was scanned by a rectangular template with a width of 14 pixels and a step length of 12 pixels.The features of average gradient and variance gradient were used to identify sugarcane nodes for the first time.The experimental results showed that the recognition accuracy was 96.8952%,and there were fewer false detected sugarcane segments.The detection efficiency could be improved by detecting multi-nodes on a single sugarcane stem at the same time.

Damage quantitative assessment of spacecraft in a large-size inspection

To ensure the safety and reliability of spacecraft during multiple space missions,it is necessary to conduct in-situ nondestructive detection of the spacecraft to judge the damage caused by the hypervelocity impact of micrometeoroids and orbital debris(MMOD).In this paper,we propose an innovative quantitative assessment method based on damage reconstructed image mosaic technology.First,a Gaussian mixture model clustering algorithm is applied to extract images that highlight damage characteristics.Then,a mosaicking scheme based on the ORB feature extraction algorithm and an improved M-estimator SAmple Consensus(MSAC)algorithm with an adaptive threshold selection method is proposed which can create large-scale mosaicked images for damage detection.Eventually,to create the mosaicked images,the damage characteristic regions are segmented and extracted.The location of the damage area is determined and the degree of damage is judged by calculating the centroid position and the perimeter quantitative parameters.The efficiency and applicability of the proposed method are verified by the experimental results.