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Stereo Vision Sensing and Image Processing
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Stereo Vision Sensing and Image Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 10 May 2024 | Viewed by 5481

Special Issue Editor

Dr. Pei-Ju Chiang

E-Mail Website
Guest Editor
Department of Systems & Naval Mechatronic Engineering, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan
Interests: 3D reconstruction; stereo vision; machine learning; image processing; practical applications of image processing

Special Issue Information

Dear Colleagues,

With the development of image acquisition technology, researchers are trying to make computer analysis of images as effective as human vision. Consequentially, relevant technologies, such as algorithms and systems of stereo vision and image processing are experiencing rapid growth. Computational efficiency, more sophisticated and effective algorithms and tools are also undergoing enhanced development.

This Special Issue on “Stereo Vision Sensing and Image Processing” will provide researchers an opportunity to explore new trends, latest achievements and research directions, and other current research on stereo vision and image processing. We are seeking original contributions on novel active 3D sensors, stereo reconstruction approaches, and image processing and recognition algorithms. Articles on 3D point cloud/mesh processing, artificial intelligence tools in stereo vision or image analysis are also of interest.

Potential topics include (but are not limited to):

  • Stereo vision;
  • Stereo reconstruction;
  • Three-dimensional point cloud/mesh processing;
  • Active/passive 3D sensors;
  • Sensor calibration;
  • Point cloud/mesh processing;
  • Shape analysis and recognition;
  • Image quality analysis;
  • Image filtering, restoration and enhancement;
  • Image segmentation;
  • Machine/deep learning and artificial intelligence in stereo vision or image analysis;
  • Pattern recognition algorithms.

Dr. Pei-Ju Chiang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • stereo vision
  • stereo reconstruction
  • 3D point cloud
  • image processing
  • image recognition

Published Papers (5 papers)

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Research

18 pages, 36613 KiB  
Article
A Light Multi-View Stereo Method with Patch-Uncertainty Awareness
by Zhen Liu, Guangzheng Wu, Tao Xie, Shilong Li, Chao Wu, Zhiming Zhang and Jiali Zhou
Sensors 2024, 24(4), 1293; https://doi.org/10.3390/s24041293 - 17 Feb 2024
Viewed by 485
Abstract
Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a fixed range for all the pixels during inverse depth [...] Read more.
Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a fixed range for all the pixels during inverse depth sampling can adversely affect depth estimation. To address these challenges, we present a novel learning-based multi-view stereo method incorporating attention mechanisms and an adaptive depth sampling strategy. Firstly, we propose a lightweight, coarse-feature-enhanced feature pyramid network in the feature extraction stage, augmented by a coarse-feature-enhanced module. This module integrates features with channel and spatial attention, enriching the contextual features that are crucial for the initial depth estimation. Secondly, we introduce a novel patch-uncertainty-based depth sampling strategy for depth refinement, dynamically configuring depth sampling ranges within the GRU-based optimization process. Furthermore, we incorporate an edge detection operator to extract edge features from the reference image’s feature map. These edge features are additionally integrated into the iterative cost volume construction, enhancing the reconstruction accuracy. Lastly, our method is rigorously evaluated on the DTU and Tanks and Temples benchmark datasets, revealing its low GPU memory consumption and competitive reconstruction quality compared to other learning-based MVS methods. Full article
(This article belongs to the Special Issue Stereo Vision Sensing and Image Processing)
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16 pages, 11264 KiB  
Article
Calibration-Free Mobile Eye-Tracking Using Corneal Imaging
by Moayad Mokatren, Tsvi Kuflik and Ilan Shimshoni
Sensors 2024, 24(4), 1237; https://doi.org/10.3390/s24041237 - 15 Feb 2024
Viewed by 709
Abstract
In this paper, we present and evaluate a calibration-free mobile eye-traking system. The system’s mobile device consists of three cameras: an IR eye camera, an RGB eye camera, and a front-scene RGB camera. The three cameras build a reliable corneal imaging system that [...] Read more.
In this paper, we present and evaluate a calibration-free mobile eye-traking system. The system’s mobile device consists of three cameras: an IR eye camera, an RGB eye camera, and a front-scene RGB camera. The three cameras build a reliable corneal imaging system that is used to estimate the user’s point of gaze continuously and reliably. The system auto-calibrates the device unobtrusively. Since the user is not required to follow any special instructions to calibrate the system, they can simply put on the eye tracker and start moving around using it. Deep learning algorithms together with 3D geometric computations were used to auto-calibrate the system per user. Once the model is built, a point-to-point transformation from the eye camera to the front camera is computed automatically by matching corneal and scene images, which allows the gaze point in the scene image to be estimated. The system was evaluated by users in real-life scenarios, indoors and outdoors. The average gaze error was 1.6∘ indoors and 1.69∘ outdoors, which is considered very good compared to state-of-the-art approaches. Full article
(This article belongs to the Special Issue Stereo Vision Sensing and Image Processing)
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24 pages, 132107 KiB  
Article
TranSpec3D: A Novel Measurement Principle to Generate A Non-Synthetic Data Set of Transparent and Specular Surfaces without Object Preparation
by Christina Junger, Henri Speck, Martin Landmann, Kevin Srokos and Gunther Notni
Sensors 2023, 23(20), 8567; https://doi.org/10.3390/s23208567 - 18 Oct 2023
Viewed by 910
Abstract
Estimating depth from images is a common technique in 3D perception. However, dealing with non-Lambertian materials, e.g., transparent or specular, is still nowadays an open challenge. However, to overcome this challenge with deep stereo matching networks or monocular depth estimation, data sets with [...] Read more.
Estimating depth from images is a common technique in 3D perception. However, dealing with non-Lambertian materials, e.g., transparent or specular, is still nowadays an open challenge. However, to overcome this challenge with deep stereo matching networks or monocular depth estimation, data sets with non-Lambertian objects are mandatory. Currently, only few real-world data sets are available. This is due to the high effort and time-consuming process of generating these data sets with ground truth. Currently, transparent objects must be prepared, e.g., painted or powdered, or an opaque twin of the non-Lambertian object is needed. This makes data acquisition very time consuming and elaborate. We present a new measurement principle for how to generate a real data set of transparent and specular surfaces without object preparation techniques, which greatly reduces the effort and time required for data collection. For this purpose, we use a thermal 3D sensor as a reference system, which allows the 3D detection of transparent and reflective surfaces without object preparation. In addition, we publish the first-ever real stereo data set, called TranSpec3D, where ground truth disparities without object preparation were generated using this measurement principle. The data set contains 110 objects and consists of 148 scenes, each taken in different lighting environments, which increases the size of the data set and creates different reflections on the surface. We also show the advantages and disadvantages of our measurement principle and data set compared to the Booster data set (generated with object preparation), as well as the current limitations of our novel method. Full article
(This article belongs to the Special Issue Stereo Vision Sensing and Image Processing)
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16 pages, 4046 KiB  
Article
Eye Segmentation Method for Telehealth: Application to the Myasthenia Gravis Physical Examination
by Quentin Lesport, Guillaume Joerger, Henry J. Kaminski, Helen Girma, Sienna McNett, Mohammad Abu-Rub and Marc Garbey
Sensors 2023, 23(18), 7744; https://doi.org/10.3390/s23187744 - 07 Sep 2023
Viewed by 1377
Abstract
Due to the precautions put in place during the COVID-19 pandemic, utilization of telemedicine has increased quickly for patient care and clinical trials. Unfortunately, teleconsultation is closer to a video conference than a medical consultation, with the current solutions setting the patient and [...] Read more.
Due to the precautions put in place during the COVID-19 pandemic, utilization of telemedicine has increased quickly for patient care and clinical trials. Unfortunately, teleconsultation is closer to a video conference than a medical consultation, with the current solutions setting the patient and doctor into an evaluation that relies entirely on a two-dimensional view of each other. We are developing a patented telehealth platform that assists with diagnostic testing of ocular manifestations of myasthenia gravis. We present a hybrid algorithm combining deep learning with computer vision to give quantitative metrics of ptosis and ocular muscle fatigue leading to eyelid droop and diplopia. The method works both on a fixed image and frame by frame of the video in real-time, allowing capture of dynamic muscular weakness during the examination. We then use signal processing and filtering to derive robust metrics of ptosis and l ocular misalignment. In our construction, we have prioritized the robustness of the method versus accuracy obtained in controlled conditions in order to provide a method that can operate in standard telehealth conditions. The approach is general and can be applied to many disorders of ocular motility and ptosis. Full article
(This article belongs to the Special Issue Stereo Vision Sensing and Image Processing)
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12 pages, 2838 KiB  
Article
Research on 3D Reconstruction of Binocular Vision Based on Thermal Infrared
by Huaizhou Li, Shuaijun Wang, Zhenpeng Bai, Hong Wang, Sen Li and Shupei Wen
Sensors 2023, 23(17), 7372; https://doi.org/10.3390/s23177372 - 24 Aug 2023
Cited by 3 | Viewed by 1413
Abstract
Thermal infrared imaging is less affected by lighting conditions and smoke compared to visible light imaging. However, thermal infrared images often have lower resolution and lack rich texture details, making them unsuitable for stereo matching and 3D reconstruction. To enhance the quality of [...] Read more.
Thermal infrared imaging is less affected by lighting conditions and smoke compared to visible light imaging. However, thermal infrared images often have lower resolution and lack rich texture details, making them unsuitable for stereo matching and 3D reconstruction. To enhance the quality of infrared stereo imaging, we propose an advanced stereo matching algorithm. Firstly, the images undergo preprocessing using a non-local mean noise reduction algorithm to remove thermal noise and achieve a smoother result. Subsequently, we perform camera calibration using a custom-made chessboard calibration board and Zhang’s camera calibration method to obtain accurate camera parameters. Finally, the disparity map is generated using the SGBM (semi-global block matching) algorithm based on the weighted least squares method, enabling the 3D point cloud reconstruction of the object. The experimental results demonstrate that the proposed algorithm performs well in objects with sufficient thermal contrast and relatively simple scenes. The proposed algorithm reduces the average error value by 10.9 mm and the absolute value of the average error by 1.07% when compared with the traditional SGBM algorithm, resulting in improved stereo matching accuracy for thermal infrared imaging. While ensuring accuracy, our proposed algorithm achieves the stereo reconstruction of the object with a good visual effect, thereby holding high practical value. Full article
(This article belongs to the Special Issue Stereo Vision Sensing and Image Processing)
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