Automatic video mosaicking algorithm via dynamic key-frame

Automatic video mosaicking is a challenging task in computer vision. Current researches consider either panoramic or mapping tasks on short videos. In this paper, an automatic mosaicking algorithm is proposed for both mapping and panoramic tasks based on the adapted key-frame on videos of any length.The speeded up robust features(SURF) and the grid motion statistic(GMS) algorithm are used for feature extraction and matching between consecutive frames, which are used to compute the transformation. In order to reduce the influence of the accumulated error during image stitching, an evaluation metric is put forward for the transformation matrix. Besides, a self-growth method is employed to stitch the global image for long videos. The algorithm is evaluated by using aerial-view and panoramic videos respectively on the graphic processing unit(GPU) device, which can satisfy the real-time requirement. The experimental results demonstrate that the proposed algorithm is able to achieve a better performance than the state-of-art.

Landslide data mosaicking based on an airborne laser point cloud and multi-beam sonar images

Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have predominantly focused on landslides that occur on land.To this end,we aim to investigate ashore and underwater landslide data synchronously.This study proposes an optimized mosaicking method for ashore and underwater landslide data.This method fuses an airborne laser point cloud with multi-beam depth sounder images.Owing to their relatively high efficiency and large coverage area,airborne laser measurement systems are suitable for emergency investigations of landslides.Based on the airborne laser point cloud,the traversal of the point with the lowest elevation value in the point set can be used to perform rapid extraction of the crude channel boundaries.Further meticulous extraction of the channel boundaries is then implemented using the probability mean value optimization method.In addition,synthesis of the integrated ashore and underwater landslide data angle is realized using the spatial guide line between the channel boundaries and the underwater multibeam sonar images.A landslide located on the right bank of the middle reaches of the Yalong River is selected as a case study to demonstrate that the proposed method has higher precision thantraditional methods.The experimental results show that the mosaicking method in this study can meet the basic needs of landslide modeling and provide a basis for qualitative and quantitative analysis and stability prediction of landslides.

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.

A New Method of Mosaicking Context Camera (CTX) Images for the Geomorphological Study of Martian Landscape

Various spacecraft and satellites from the world’s best space agencies are exploring Mars since 1970, constantly with great ability to capture the maximum amount of dataset for a better understanding of the red planet. In this paper, we propose a new method for making a mosaic of Mars Reconnaissance Orbiter (MRO) spacecraft payload Context Camera (CTX) images. In this procedure, we used ERDAS Imagine for image rectification and mosaicking as a tool for image processing, which is a new and unique method of generating a mosaic of thousands of CTX images to visualize the large-scale areas. The output product will be applicable for mapping of Martian geomorphological features, 2D mapping of the linear feature with high resolution, crater counting, and morphometric analysis to a certain extent.

Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds

A detailed study was carried out to find optimal seam-lines for mosaicking of images acquired by an airborne light detection and ranging (LiDAR) system.High ground objects labeled as obstacles can be identified by delineating black holes from filtered point clouds obtained by filtering the raw laser scanning dataset.An innovative A algorithm is proposed that can automatically make the seam-lines keep away from these obstacles in the registered images.This method can intelligently optimize the selection of seam-lines and improve the quality of orthophotos.A simulated grid image was first used to analyze the effect of different heuristic functions on path planning.Three subsets of LiDAR data from Xi'an,Dunhuang,and Changyang in Northwest China were obtained.A quantitative method including pixel intensity,hue,and texture was used.With our proposed method,9.4%,8.7%,and 9.8% improvements were achieved in Dunhuang,Xi'an,and Changyang,respectively.

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.

Repair approach for DMC images based on hierarchical location using edge curve

The color composite digital mapping camera （DMC） images are produced by the post-processing software of Z/I imaging. But the failure of radiometric correction in post-processing leads to residual radiometric differences between CCD images, which then affect the quality of the images in further applications. This paper, via analyzing the characters and causes of such a phenomenon, proposes a repair approach based on hierarchical location using edge curve. The approach employs a hierarchical strategy to locate the transition area and seam-line automatically and then repair the image through the global reconstruction between CCD images and the local reconstruction in the transition area. Experiments indicate that the approach proposed by this paper is feasible and can improve the quality of images effectively.

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.