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Applied Sciences
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Machine Intelligence in Image and Video Analysis
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Machine Intelligence in Image and Video Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 4546

Special Issue Editors

Prof. Dr. Nuno Carlos Sousa Rodrigues

E-Mail Website
Guest Editor
CIIC, ESTG, Polytechnic of Leiria, Leiria, Portugal
Interests: procedural modeling; virtual reality; augmented reality; human–computer interaction; computer vision
Prof. Dr. Paulo Manuel Almeida Costa

E-Mail Website
Guest Editor
CIIC, ESTG, Polytechnic of Leiria, 2411-901 Leiria, Portugal
Interests: computer engineering; computer vision; pattern detection; artificial intelligence

Special Issue Information

Dear Colleagues,

The increase in the digitization of processes, as is the case with industry 4.0, with the advent of cyber physical systems, has led to an exponential increase in the amount of information provided by machines. In fact, the increasingly massive use of robots and drones in industry, and other fields, which are often equipped with various types of cameras (e.g. RBG cameras, depth cameras, thermal cameras, multispectral cameras), provides a huge amount of information, more specifically images and videos which are difficult to analyze by humans. It is therefore more imperative than ever that this analysis is carried out automatically through intelligent systems which allow a proactive control of processes. This automatic monitoring allows freeing human resources bringing advantages in various industries and processes (e.g. quality control, power grid analysis, thermal inspection, security) and also helps to take guided management decisions and prediction based on data.

We invite you to submit novel and original articles that allow an advancement of knowledge in the field of machine intelligence in image and video analysis.

The topics of interest for this Special Issue include but are not limited to the following:

  • Image and video analysis in cyber physical systems;
  • Applications of machine learning models in industry 4.0;
  • Automated machine learning;
  • Big data analysis;
  • Real-time quality control;
  • Image processing;
  • Machine vision applications;
  • Computer vision techniques for surface defect detection.

Prof. Dr. Nuno Carlos Sousa Rodrigues
Prof. Dr. Paulo Manuel Almeida Costa
Guest Editors

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. Applied Sciences 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 2400 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

  • machine learning
  • automated surface analysis
  • computer vision for quality control
  • cyber physical systems

Published Papers (2 papers)

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Research

16 pages, 2179 KiB  
Article
A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
by Kai Guo, Hu Ye, Junhao Gu and Ye Tian
Appl. Sci. 2022, 12(16), 8295; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168295 - 19 Aug 2022
Cited by 4 | Viewed by 1821
Abstract
Absolute orientation estimation is one of the key steps in computer vision, and n 2D–3D point correspondences can be used to obtain the absolute orientation, which is known as the perspective-n-point problem (PnP). The lowest number of point correspondences is three if there [...] Read more.
Absolute orientation estimation is one of the key steps in computer vision, and n 2D–3D point correspondences can be used to obtain the absolute orientation, which is known as the perspective-n-point problem (PnP). The lowest number of point correspondences is three if there is no other information, and the corresponding algorithm is called the P3P solver. In practice, the real scene may consist of some geometric information, e.g., the vanishing point. When scenes contain parallel lines, they intersect at vanishing points. Hence, to reduce the number of point correspondences and increase the computational speed, we proposed a fast and simple method for absolute orientation estimation using a single vanishing point. First, the inertial measurement unit (IMU) was used to obtain the rotation of the camera around the Y-axis (i.e., roll angle), which could simplify the orientation estimation. Then, one vanishing point was used to estimate the coarse orientation because it contained direction information in both the camera frame and world frame. Finally, our proposed method used a non-linear optimization algorithm for solution refining. The experimental results show that compared with several state-of-the-art orientation estimation solvers, our proposed method had a better performance regarding numerical stability, noise sensitivity, and computational speed in synthetic data and real images. Full article
(This article belongs to the Special Issue Machine Intelligence in Image and Video Analysis)
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23 pages, 5514 KiB  
Article
Hybrid Quality Inspection for the Automotive Industry: Replacing the Paper-Based Conformity List through Semi-Supervised Object Detection and Simulated Data
by Isabel Rio-Torto, Ana Teresa Campaniço, Pedro Pinho, Vitor Filipe and Luís F. Teixeira
Appl. Sci. 2022, 12(11), 5687; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115687 - 03 Jun 2022
Cited by 2 | Viewed by 1987
Abstract
The still prevalent use of paper conformity lists in the automotive industry has a serious negative impact on the performance of quality control inspectors. We propose instead a hybrid quality inspection system, where we combine automated detection with human feedback, to increase worker [...] Read more.
The still prevalent use of paper conformity lists in the automotive industry has a serious negative impact on the performance of quality control inspectors. We propose instead a hybrid quality inspection system, where we combine automated detection with human feedback, to increase worker performance by reducing mental and physical fatigue, and the adaptability and responsiveness of the assembly line to change. The system integrates the hierarchical automatic detection of the non-conforming vehicle parts and information visualization on a wearable device to present the results to the factory worker and obtain human confirmation. Besides designing a novel 3D vehicle generator to create a digital representation of the non conformity list and to collect automatically annotated training data, we apply and aggregate in a novel way state-of-the-art domain adaptation and pseudo labeling methods to our real application scenario, in order to bridge the gap between the labeled data generated by the vehicle generator and the real unlabeled data collected on the factory floor. This methodology allows us to obtain, without any manual annotation of the real dataset, an example-based F1 score of 0.565 in an unconstrained scenario and 0.601 in a fixed camera setup (improvements of 11 and 14.6 percentage points, respectively, over a baseline trained with purely simulated data). Feedback obtained from factory workers highlighted the usefulness of the proposed solution, and showed that a truly hybrid assembly line, where machine and human work in symbiosis, increases both efficiency and accuracy in automotive quality control. Full article
(This article belongs to the Special Issue Machine Intelligence in Image and Video Analysis)
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