A Novel Stitching Method for Dust and Rock Analysis Based on Yutu Rover Panoramic Imagery

Ye, Ben Haobin; Cai, Zhanchuan; Lan, Ting; Cao, Wei

doi:10.1109/jstars.2019.2947281

Cited by 5 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(7) This research calls the loss bice function (Equation ( 19)) with the input of step (4) and step (6) to achieve the conservative sky cross-entropy. This research calls the loss bice function (Equation ( 19)) with the input of step ( 5) and step (6) to achieve the conservative ground cross-entropy. This research further adds a weight parameter "α" to balance the two cross-entropies.…”

Section: Transfer Training Process: Sky and Ground Segmentation Network For The Navigation Visions Of Planetary Rovermentioning

confidence: 99%

“…( 8) Equation ( 22) calls the acc bi function (Equation ( 20)) with the input of step (4) and step (6) to achieve the conservative sky accuracy. Equation ( 22) calls the acc bi function (Equation ( 20)) with the input of step ( 5) and step (6) to achieve the conservative ground accuracy.…”

Section: Transfer Training Process: Sky and Ground Segmentation Network For The Navigation Visions Of Planetary Rovermentioning

confidence: 99%

“…Sky and ground segmentation is a popular topic in computer vision that has a wide range of applications [1][2][3][4][5][6][7][8][9][10][11]. Sky and ground are the two essential components in outdoor and remote scenes [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Song et al [26] used two imbalance SVM classifiers and similarity measurement. Ye et al [6] and Dev et al [27] focused on The whole-sky imager and proposed a superpixel-based SVM method. Beuren et al [28] proposed a whiteness-and blueness-based SVM method.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

Kuang

Rana

Zhao

2021

Sensors

View full text Add to dashboard Cite

Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.

show abstract

Section: Transfer Training Process: Sky and Ground Segmentation Network For The Navigation Visions Of Planetary Rovermentioning

confidence: 99%

Section: Transfer Training Process: Sky and Ground Segmentation Network For The Navigation Visions Of Planetary Rovermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

Kuang

Rana

Zhao

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…However, due to the limitations of the imaging width or mechanism, it is difficult to include full region of interest (ROI) in a remote sensing image scene [2]. Image stitching is a technique of forming a mosaic by combining overlapping areas of multiple images [3]- [6]. In addition, image stitching is very important because it is required for many real world tasks, including remote sensing image processing [7], [8], resource and environmental monitoring [9], [10], and so on.…”

Section: Introductionmentioning

confidence: 99%

UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation

Chen

Luo

et al. 2020

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

View full text Add to dashboard Cite

The goal of unmanned aerial vehicle (UAV) image mosaicking is to create natural-looking mosaics without artifacts due to the parallax of the image and relative camera motion. UAV remote sensing is a low-altitude technology and the UAV imaged scene is not effectively planar, yielding parallax on the images. Moreover, when an object in three dimensions is mapped to an image plane, different surfaces have different projections. These projections vary with the viewpoint in a sequence of UAV images, which causes artifacts near some tall buildings in the stitched images. To solve these problems, we propose a novel stitching method based on multi-region guided local projection deformation, which can significantly reduce ghosting due to these projections vary with the viewpoint and the parallax. In the proposed method, the image is initially meshed and each cell corresponds to a local homography for image matching, which can reduce misalignment artifacts in the results compared with 2D projective transforms or global homography. Then, we divide the overlapping regions of input images into multiple regions by classifying feature points. The partitioned regions which serve well scene constraints, are employed to guide the calculation of local homography. Specifically, instead of calculating local homography by the distance between all the feature points in the image and the vertices of the grid, we propose a strategy where multiple regions have different weights for calculating local homography, which can significantly reduce ghosting near some tall buildings. The benefits of the proposed approach are demonstrated using a variety of challenging cases.

show abstract