Saliency-Induced Moving Object Detection for Robust RGB-D Vision Navigation Under Complex Dynamic Environments

Sun, Chao; Wu, Xing; Sun, Jia; Sun, Changyin; Xu, Mingzhu; Ge, Quanbo

doi:10.1109/tits.2023.3275279

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…The experiments are conducted using Visual Studio 2019 and OpenCV 4.6.0. To evaluate the performance of the proposed depth image optimization algorithm, employ the NLM algorithm, the Multiple Edge Converge Inpainting Algorithm (MECI) [10], and the AB-NLM algorithm as comparative benchmarks.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Considerable research efforts have been devoted by researchers both domestically and internationally to effectively fill the holes in-depth images and obtain high-quality depth images. CHO et al [10] estimated the boundaries using color images and converged from the edges to the target boundary, effectively addressing holes between edges and multiple edge pixels to achieve seamless inpainting of object boundaries near the camera through an iterative process of alternating filling directions. Xiang et al [11] extended Criminisi's inpainting approach [12] by incorporating a block matching algorithm for identifying irregularly shaped voids and a hybrid matching algorithm for selecting optimal repair patches with minimal differences in hole regions, leading to successful restoration, particularly for larger irregular holes.…”

Section: Introductionmentioning

confidence: 99%

“…These methodologies collectively leverage diverse strategies in hole repair techniques to effectively address challenges in in-depth image repair. However, the methods proposed in [10,11] are somewhat complex. The ultimate repair outcome in [13] is influenced by the effectiveness of image segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Non-Local Means Hole Repair Algorithm Based on Adaptive Block

Zhao,

Li,

Pan

2023

Applied Sciences

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RGB-D cameras provide depth and color information and are widely used in 3D reconstruction and computer vision. In the majority of existing RGB-D cameras, a considerable portion of depth values is often lost due to severe occlusion or limited camera coverage, thereby adversely impacting the precise localization and three-dimensional reconstruction of objects. In this paper, to address the issue of poor-quality in-depth images captured by RGB-D cameras, a depth image hole repair algorithm based on non-local means is proposed first, leveraging the structural similarities between grayscale and depth images. Second, while considering the cumbersome parameter tuning associated with the non-local means hole repair method for determining the size of structural blocks for depth image hole repair, an intelligent block factor is introduced, which automatically determines the optimal search and repair block sizes for various hole sizes, resulting in the development of an adaptive block-based non-local means algorithm for repairing depth image holes. Furthermore, the proposed algorithm’s performance are evaluated using both the Middlebury stereo matching dataset and a self-constructed RGB-D dataset, with performance assessment being carried out by comparing the algorithm against other methods using five metrics: RMSE, SSIM, PSNR, DE, and ALME. Finally, experimental results unequivocally demonstrate the innovative resolution of the parameter tuning complexity inherent in-depth image hole repair, effectively filling the holes, suppressing noise within depth images, enhancing image quality, and achieving elevated precision and accuracy, as affirmed by the attained results.

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Section: Experimental Results and Analysismentioning

confidence: 99%