Improved HardNet and Stricter Outlier Filtering to Guide Reliable Matching

Xu, Meng; Chen, Shen; Zhang, Jun; Wang, Zhipeng; Ruan, Zhiwei; Poslad, Stefan; Xu, Ping

doi:10.32604/cmc.2023.034053

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Examples of such applications include fire detection, locating ingredients for cooking robots, and planning routes to kitchens and offices. Estimating the camera's 6 degrees of freedom (6-DoF) pose from images captured by the camera can be achieved through end-to-end deep learning [7] or feature matching from structure-based approaches [8]. By integrating advanced deep learning technology with color and depth cameras as input sensors, multi-sensor systems can assist in intelligent living.…”

Section: Introduction 1background and Introductionmentioning

confidence: 99%

Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features

Zhang

Gong

et al. 2023

Sensors

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Accurate and robust camera pose estimation is essential for high-level applications such as augmented reality and autonomous driving. Despite the development of global feature-based camera pose regression methods and local feature-based matching guided pose estimation methods, challenging conditions, such as illumination changes and viewpoint changes, as well as inaccurate keypoint localization, continue to affect the performance of camera pose estimation. In this paper, we propose a novel relative camera pose regression framework that uses global features with rotation consistency and local features with rotation invariance. First, we apply a multi-level deformable network to detect and describe local features, which can learn appearances and gradient information sensitive to rotation variants. Second, we process the detection and description processes using the results from pixel correspondences of the input image pairs. Finally, we propose a novel loss that combines relative regression loss and absolute regression loss, incorporating global features with geometric constraints to optimize the pose estimation model. Our extensive experiments report satisfactory accuracy on the 7Scenes dataset with an average mean translation error of 0.18 m and a rotation error of 7.44° using image pairs as input. Ablation studies were also conducted to verify the effectiveness of the proposed method in the tasks of pose estimation and image matching using the 7Scenes and HPatches datasets.

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Section: Introduction 1background and Introductionmentioning

confidence: 99%

Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features

Zhang

Gong

et al. 2023

Sensors

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Three-dimensional model of normal human dermal tissue using serial tissue sections

Liu,

Zhang,

Huang

2024

Front. Bioeng. Biotechnol.

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Background: This study aims to construct a three-dimensional model of skin dermis utilizing continuous tissue sections, with the primary objective of obtaining anatomical structure data for normal human dermal tissues.Methods: Normal skin tissue specimens were acquired, paraffin-embedded, and subjected to HE staining. Panoramic images of skin sections were captured using a microscope. Tissue section images were aligned using the SIFT and StackReg image alignment methods, with analysis conducted using the OpenCV module. Mimics17 software facilitated the reconstruction of the skin dermal 3D model, enabling the calculation of dermal porosity and the void diameter.Results: Panoramic skin slices exhibited high-resolution differentiation of dermal fibers and cellular structures. Both SIFT and StackReg image alignment methods yielded similar results, although the SIFT method demonstrated greater robustness. Successful reconstruction of the three-dimensional dermal structure was achieved. Quantitative analysis revealed a dermal porosity of 18.96 ± 4.41% and an average pore diameter of 219.29 ± 34.27 μm. Interestingly, the porosity of the dermis exhibited a gradual increase from the papillary layer to the fourth layer, followed by a transient decrease and then a gradual increase. The distribution of the mean pore diameter mirrored the pattern observed in porosity distribution.Conclusion: Utilizing the continuous skin tissue slice reconstruction technique, this study successfully reconstructed a high-precision three-dimensional tissue structure of the skin. The quantitative analysis of dermal tissue porosity and average pore diameter provides a standardized dataset for the development of biomimetic tissue-engineered skin.

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Improved HardNet and Stricter Outlier Filtering to Guide Reliable Matching

Cited by 2 publications

References 9 publications

Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features

Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features

Three-dimensional model of normal human dermal tissue using serial tissue sections

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