Continuous Point Cloud Stitch based on Image Feature Matching Constraint and Score

Hu, Fangchao; Li, Yinguo; Huang, Wei; Tian, Zhen

doi:10.1109/ivs.2018.8500429

Cited by 3 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hu et al [17] proposed a feature-matching score-based approach for continuous point cloud stitching. e main of the proposed method is to find out the actual time and energy consumption rate of the stitching process.…”

Section: Related Workmentioning

confidence: 99%

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Peng¹,

Yu²

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

Image stitching in 3D film production synchronizes multiple independent frame images as a single high-resolution output. The stitching process is common for displaying a large coverage or shot in a single frame containing distinct images. For improving the realistic accuracy of 3D film images, this manuscript introduces an Itinerant Pixel-Matching-based Stitching Process (IPMSP). The proposed stitching process relies on cross-sectional pixels identified in merging two or more images. Based on the linear cumulative distribution in augmenting images, the homogeneity feature is identified. If the homogeneity is high then the stitching for linear pixels occurs increasing the frame resolution and size. The cumulative distribution is determined using the recurrent neural network in verifying homogeneity and contrast. For the contrast pixels identified, the cross-sectional matching is performed for substituting similar pixels in the missing sections. The process is repeated until the stitching region is the same based on the homogeneity feature and increased dimensions. Therefore, this process is capable of improving accuracy, precision, substitution, and reducing errors and complexity.

show abstract

Section: Related Workmentioning

confidence: 99%

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Peng¹,

Yu²

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

show abstract

“…According to the chain rule of divergence [24,25], the above formula can be further simplified as follows:…”

Section: Point Set Registration Based On Improved Kl Divergencementioning

confidence: 99%

Point Set Registration Based on Improved KL Divergence

Lee

2021

Scientific Programming

View full text Add to dashboard Cite

A point set registration algorithm based on improved Kullback–Leibler (KL) divergence is proposed. Each point in the point set is represented as a Gaussian distribution. The Gaussian distribution contains the position information of the candidate point and surrounding ones. In this way, the entire point set can be modeled as a Gaussian mixture model (GMM). The registration problem of two point sets is further converted as a minimization problem of the improved KL divergence between two GMMs, and the genetic algorithm is used to optimize the solution. Experimental results show that the proposed algorithm has strong robustness to noise, outliers, and missing points, which achieves better registration accuracy than some state-of-the-art methods.

show abstract

“…After the feature points of each image are obtained, the feature points of the nearest neighbors are used as matching points to match the features of each image [31,32]. Using the feature vector of the key descriptor to be 128-dimensional, the nearest neighbor feature point of each feature point is searched by the distance function and various search algorithms.…”

Section: B Feature Points Matchingmentioning

confidence: 99%

Research on a Single-Tree Point Cloud Segmentation Method Based on UAV Tilt Photography and Deep Learning Algorithm

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Developing a robust point cloud segmentation algorithm for individual trees from an amount of point cloud data has great significance for tracking tree changes. This method can measure the size, growth and mortality of individual trees to track and understand forest carbon storage and variation. Traditional measurement methods are not only slow but tardy. In order to obtain forest information better and faster, this article focuses on two aspects: The first is using UAVs to obtain multi-view remote sensing images of the forest, then using the SFM algorithm to construct the forest sparse point cloud and PMVS algorithm to construct the dense point cloud. The second is that a targeted point cloud deep learning method is proposed to extract the point cloud of a single tree. The research results show that the accuracy of single-tree point cloud segmentation of deep learning methods is more than 90%, and the accuracy is far better than traditional planar image segmentation and point cloud segmentation. The combination of point cloud data acquisition with UAV remote sensing and point cloud deep learning algorithms can meet the needs of forestry surveys. It is undeniable that this method, as a forestry survey tool, has a large space for promotion and possible future development.

show abstract

Continuous Point Cloud Stitch based on Image Feature Matching Constraint and Score

Cited by 3 publications

References 15 publications

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Point Set Registration Based on Improved KL Divergence

Research on a Single-Tree Point Cloud Segmentation Method Based on UAV Tilt Photography and Deep Learning Algorithm

Contact Info

Product

Resources

About