Enhancement of RGB-D Image Alignment Using Fiducial Markers

Madeira, Tiago; Oliveira, Miguel; Dias, Paulo

doi:10.3390/s20051497

Cited by 6 publications

(3 citation statements)

References 59 publications

(60 reference statements)

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“…With the rapid development of artificial intelligence, especially deep learning, in recent years, convolutional neural networks (CNN) have been widely studied and have achieved excellent results in different tasks related to computer vision, such as image enhancement [ 12 ], segmentation [ 13 ], tracking [ 14 ], detection [ 15 ], and recognition [ 16 , 17 , 18 ]. The features learned with the CNN do not heavily rely on manual modeling, so their robustness and accuracy are usually better than for manual methods.…”

Section: Related Workmentioning

confidence: 99%

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Leng

Yang

Kim

et al. 2020

Sensors

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Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.

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Section: Related Workmentioning

confidence: 99%

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Leng

Yang

Kim

et al. 2020

Sensors

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“…3D modeling of construction infrastructure is a typical form of digital documentation in which geometric shape and texture information is reconstructed from the real object [18,19]. The technologies behind 3D reconstruction are rooted in fields including photogrammetry, UAVs, laser scanners, and computer vision.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Data Fusion for 3D Reconstruction of Complex Structures: A Case Study on a Real High Formwork Project

2023

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As the most comprehensive document types for the recording and display of real-world information regarding construction projects, 3D realistic models are capable of recording and displaying simultaneously textures and geometric shapes in the same 3D scene. However, at present, the documentation for much of construction infrastructure faces significant challenges. Based on TLS, GNSS/IMU, mature photogrammetry, a UAV platform, computer vision technologies, and AI algorithms, this study proposes a workflow for 3D modeling of complex structures with multiple-source data. A deep learning LoFTR network was used first for image matching, which can improve matching accuracy. Then, a NeuralRecon network was employed to generate a 3D point cloud with global consistency. GNSS information was used to reduce search space in image matching and produce an accurate transformation matrix between the image scene and the global reference system. In addition, to enhance the effectiveness and efficiency of the co-registration of the two-source point clouds, an RPM-net was used. The proposed workflow processed the 3D laser point cloud and UAV low-altitude multi-view image data to generate a complete, accurate, high-resolution, and detailed 3D model. Experimental validation on a real high formwork project was carried out, and the result indicates that the generated 3D model has satisfactory accuracy with a registration error value of 5 cm. Model comparison between the TLS, image-based, data fusion 1 (using the common method), and data fusion 2 (using the proposed method) models were conducted in terms of completeness, geometrical accuracy, texture appearance, and appeal to professionals. The results denote that the generated 3D model has similar accuracy to the TLS model yet also provides a complete model with a photorealistic appearance that most professionals chose as their favorite.

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“…In the designed model, they used a feature alignment layer to match the image features with the query ones. In [56], an optimization framework is proposed that uses fiducial markers placed in the scene, reducing visual artifacts caused by misalignments.…”

Section: Introductionmentioning

confidence: 99%