A Method for Detecting Feature-Sparse Regions and Matching Enhancement

Wang, Longhao; Lan, Chaozhen; Wu, Biao; Gao, Tian; Wei, Zijun; Yao, Fushan

doi:10.3390/rs14246214

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…On top of that, normalization often decreases the resolution of images, especially for satellite data. The application of deep features like SuperPoint in combination with SuperGlue may overcome this issue [58]. SuperPoint is a self-supervised machine learning operator that seems to be a convenient and adjustable solution.…”

Section: Conclusion and Suggestionsmentioning

confidence: 99%

Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation

Dippold,

Tsai

2024

Sensors

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The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for reducing interferences in the SfM process. The first stage includes cross-sensor training, model selection and the evaluation of image-to-image RGB to color infrared (CIR) translation with Generative Adversarial Networks (GANs). The second stage includes feature detection with multiple feature detector operators, feature removal with respect to the NDVI-based vegetation classification, masking, matching, pose estimation and triangulation to generate sparse 3D point clouds. The materials utilized in both stages are a publicly available RGB-NIR dataset, and satellite and UAV imagery. The experimental results indicate that the cross-sensor and category-wise validation achieves an accuracy of 0.9466 and 0.9024, with a kappa coefficient of 0.8932 and 0.9110, respectively. The histogram-based evaluation demonstrates that the predicted NIR band is consistent with the original NIR data of the satellite test dataset. Finally, the test on the UAV RGB and artificially generated NIR with a segmentation-driven two-view SfM proves that the proposed framework can effectively translate RGB to CIR for NDVI calculation. Further, the artificially generated NDVI is able to segment and classify vegetation. As a result, the generated point cloud is less noisy, and the 3D model is enhanced.

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Section: Conclusion and Suggestionsmentioning

confidence: 99%

Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation

Dippold,

Tsai

2024

Sensors

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Deep Reverse Attack on SIFT Features With a Coarse-to-Fine GAN Model

Li,

Zhu,

Wang

et al. 2024

IEEE Trans. Circuits Syst. Video Technol.

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A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching

Xue,

Hou,

Xuan

et al. 2024

Animals

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As the sheep industry rapidly moves towards modernization, digitization, and intelligence, there is a need to build breeding farms integrated with big data. By collecting individual information on sheep, precision breeding can be conducted to improve breeding efficiency, reduce costs, and promote healthy breeding practices. In this context, the accurate identification of individual sheep is essential for establishing digitized sheep farms and precision animal husbandry. Currently, scholars utilize deep learning technology to construct recognition models, learning the biological features of sheep faces to achieve accurate identification. However, existing research methods are limited to pattern recognition at the image level, leading to a lack of diversity in recognition methods. Therefore, this study focuses on the small-tailed Han sheep and develops a sheep face recognition method based on three-dimensional reconstruction technology and feature point matching, aiming to enrich the theoretical research of sheep face recognition technology. The specific recognition approach is as follows: full-angle sheep face images of experimental sheep are collected, and corresponding three-dimensional sheep face models are generated using three-dimensional reconstruction technology, further obtaining three-dimensional sheep face images from three different perspectives. Additionally, this study developed a sheep face orientation recognition algorithm called the sheep face orientation recognition algorithm (SFORA). The SFORA incorporates the ECA mechanism to further enhance recognition performance. Ultimately, the SFORA has a model size of only 5.3 MB, with accuracy and F1 score reaching 99.6% and 99.5%, respectively. During the recognition task, the SFORA is first used for sheep face orientation recognition, followed by matching the recognition image with the corresponding three-dimensional sheep face image based on the established SuperGlue feature-matching algorithm, ultimately outputting the recognition result. Experimental results indicate that when the confidence threshold is set to 0.4, SuperGlue achieves the best matching performance, with matching accuracies for the front, left, and right faces reaching 96.0%, 94.2%, and 96.3%, respectively. This study enriches the theoretical research on sheep face recognition technology and provides technical support.

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A Method for Detecting Feature-Sparse Regions and Matching Enhancement

Cited by 3 publications

References 28 publications

Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation

Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation

Deep Reverse Attack on SIFT Features With a Coarse-to-Fine GAN Model

A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching

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