Panoramic Image Generation Using Centerline- Constrained Mesh Parameterization for Arbitrarily Shaped Tunnel Lining

Deng, Fei; Yang, Junxing

doi:10.1109/access.2020.2964121

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…Image stitching was used to obtained larger images from smaller captured images, whereas the quality of stitching is not evaluated. Finally, Deng et al [56] used images from the Beijing metro tunnel and Xiaoyadaokou tunnel to stitch the images of tunnel surfaces.…”

Section: Discussionmentioning

confidence: 99%

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Real-Time Video Stitching for Mine Surveillance Using a Hybrid Image Registration Method

Bai

Chen

et al. 2020

Electronics

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Video stitching technology provides an effective solution for a wide viewing angle monitoring mode for industrial applications. At present, the observation angle of a single camera is limited, and the monitoring network composed of multiple cameras will have many overlapping images captured. Monitoring surveillance cameras can cause the problems of viewing fatigue and low video utilization rate of involved personnel. In addition, current video stitching technology has poor adaptability and real-time performance. We propose an effective hybrid image feature detection method for fast video stitching of mine surveillance video using the effective information of the surveillance video captured from multiple cameras in the actual conditions in the industrial coal mine. The method integrates the Moravec corner point detection and the scale-invariant feature transform (SIFT) feature extractor. After feature extraction, the nearest neighbor method and the random sampling consistency (RANSAC) algorithm are used to register the video frames. The proposed method reduces the image stitching time and solves the problem of feature re-extraction due to the change of observation angle, thus optimizing the entire video stitching process. The experimental results on the real-world underground mine videos show that the optimized stitching method can stitch videos at a speed of 21 fps, effectively meeting the real-time requirement, while the stitching effect has a good stability and applicability in real-world conditions.

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Section: Discussionmentioning

confidence: 99%

“…We used the videos from four datasets: Tytyri mine, Finland; Sonderhausen mine, Germany [55]; Târgu Ocna Salt Mine, Romania; and Xiaoyadaokou tunnel, China [56]. The examples of images from the dataset are presented in Figures 4 and 5.…”

Section: Datasetmentioning

confidence: 99%

Real-Time Video Stitching for Mine Surveillance Using a Hybrid Image Registration Method

Bai

Chen

et al. 2020

Electronics

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“…The main processes include image preprocessing, image registration, feature point matching, and image fusion. Deng et al [30] proposed an algorithm for unwrapping 3D tunnel lining meshes into straight two-dimensional (2D) meshes to generate a 2D seamless panoramic image of the tunnel lining. Kim et al [31] proposed the use of cost curve curvaturebased optimization of feature point accuracy to overcome the shortcomings of traditional methods based only on similarity metrics.…”

Section: Relate Workmentioning

confidence: 99%

A subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image

Xia,

Mao,

Wang

et al. 2024

Eng. Res. Express

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The paper presents an intelligent tunnel 3D laser image acquisition system. The system can quickly acquire the 3D point cloud and tunnel surface image of the whole tunnel section. The paper proposes the subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image. Firstly, the method extracts the homonymous points from the mapped image generated from the 3D point cloud and the camera image based on the maximum information coefficient method. Secondly, the 3D point cloud positions corresponding to the homonymous points are determined. The optimization of camera parameters is achieved based on the gradient descent method. Then, the tunnel point cloud is expanded to generate a gray scale image based on spherical projection. Based on the optimized parameters, the point cloud is projected to the camera image to determine the depth value of each camera image pixel point. Finally, the pixel values of the camera image are projected based on the spherical projection to obtain the stitching result images. This method stitches individual images together to form a more complete tunnel surface image. The larger tunnel surface image helps in identifying tunnel defects.

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“…One of the key advantages of the immersive contents experience is the wide field of view (FoV) perception, create with the help of panoramic images where a single wide-angle stitched image is produced from multiple smaller viewpoints images captured via various cameras [ 6 , 7 ]. The image stitching pipeline involves two main steps, such as geometric alignment and photometric correction.…”

Section: Introductionmentioning

confidence: 99%

DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Ullah

Irfan

Han

et al. 2020

Sensors

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Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.

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Panoramic Image Generation Using Centerline- Constrained Mesh Parameterization for Arbitrarily Shaped Tunnel Lining

Cited by 7 publications

References 41 publications

Real-Time Video Stitching for Mine Surveillance Using a Hybrid Image Registration Method

Real-Time Video Stitching for Mine Surveillance Using a Hybrid Image Registration Method

A subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image

DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

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