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Special Issue "3D Images, Point Clouds and Videos Classification and Management by Means of Artificial Intelligence Approaches from Industrial to Agriculture Applications"

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

Deadline for manuscript submissions: 15 October 2020.

Special Issue Editors

Prof. Dr. Basilio Sierra
Website
Guest Editor
Universidad del Pais Vasco, Leioa, Spain
Interests: supervised classification; one-class classification; machine learning; pattern precognition; nearest neighbor algorithm; genetic algorithms; estimation of distribution algorithms; Bayesian networks; applications in medicine; machine learning and data mining in general
Dr. Naiara Aginako
Website
Guest Editor
Universidad del Pais Vasco, Leioa, Spain
Interests: computer vision; biometric applications; machine learning; 3D vision; deep learning
Dr. José María Martínez-Otzeta
Website
Guest Editor
Universidad del Pais Vasco, Leioa, Spain
Interests: machine learning; computer vision; 3D vision; deep learning; video action recognition

Special Issue Information

Dear Colleagues,

In recent years, image capture systems have increasingly been used in industrial, commercial, and agricultural applications as a means to obtain information which could improve the quality of the obtained products. The methodologies and technologies related to video processing, imaging processing, 3D modeling, and multimedia have significantly been implemented in the field of computer vision. The continuous development of these technologies has led to an introduction of new methodologies and applications in this field. Machine learning algorithms and deep learning provide paradigms to process and classify obtained 3D images or point clouds, as well as video action recognition.

Recently, advancements in sensor technologies that acquire point cloud data have paved the way for the development of new ideas, methodologies, and solutions in countless remote sensing applications.

This Special Issue addresses the latest research advances in 3D image, point cloud, and video classification. This covers a wide area of applications, such as industrial pieces recognition, crop status management, video action recognition, etc. and several techniques: machine learning, deep learning, etc. The papers will help readers to explore and share their knowledge and experience in technologies and development techniques.

Prof. Dr. Basilio Sierra
Dr. Naiara Aginako
Dr. José María Martínez-Otzeta
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 papers will be 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 2000 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

  • artificial intelligence
  • machine learning
  • 3D model processing
  • robotic applications
  • deep learning for point cloud processing
  • point cloud classification
  • modeling urban and natural environments from aerial and mobile LiDAR/image-based point clouds
  • industrial applications with large-scale point clouds
  • machine and robot vision
  • 3D Image detection, recognition, and tracking
  • biometrics and biomedical image analysis
  • action recognition
  • mathematical methods in image processing, analysis, and representation
  • artificial intelligence tools in image analysis
  • pattern recognition algorithms applied for images

Published Papers (2 papers)

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Research

Open AccessArticle
Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition
Sensors 2020, 20(15), 4250; https://doi.org/10.3390/s20154250 - 30 Jul 2020
Abstract
Palmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose a correntropy-induced discriminative nonnegative sparse coding method for robust palmprint [...] Read more.
Palmprint recognition has been widely studied for security applications. However, there is a lack of in-depth investigations on robust palmprint recognition. Regression analysis being intuitively interpretable on robustness design inspires us to propose a correntropy-induced discriminative nonnegative sparse coding method for robust palmprint recognition. Specifically, we combine the correntropy metric and l1-norm to present a powerful error estimator that gains flexibility and robustness to various contaminations by cooperatively detecting and correcting errors. Furthermore, we equip the error estimator with a tailored discriminative nonnegative sparse regularizer to extract significant nonnegative features. We manage to explore an analytical optimization approach regarding this unified scheme and figure out a novel efficient method to address the challenging non-negative constraint. Finally, the proposed coding method is extended for robust multispectral palmprint recognition. Namely, we develop a constrained particle swarm optimizer to search for the feasible parameters to fuse the extracted robust features of different spectrums. Extensive experimental results on both contactless and contact-based multispectral palmprint databases verify the flexibility and robustness of our methods. Full article
Open AccessArticle
GAS-GCN: Gated Action-Specific Graph Convolutional Networks for Skeleton-Based Action Recognition
Sensors 2020, 20(12), 3499; https://doi.org/10.3390/s20123499 - 21 Jun 2020
Abstract
Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs). Many existing GCNs-based models only use the fixed hand-crafted adjacency matrix to describe the connections between human body joints. This omits the important implicit connections between joints, which [...] Read more.
Skeleton-based action recognition has achieved great advances with the development of graph convolutional networks (GCNs). Many existing GCNs-based models only use the fixed hand-crafted adjacency matrix to describe the connections between human body joints. This omits the important implicit connections between joints, which contain discriminative information for different actions. In this paper, we propose an action-specific graph convolutional module, which is able to extract the implicit connections and properly balance them for each action. In addition, to filter out the useless and redundant information in the temporal dimension, we propose a simple yet effective operation named gated temporal convolution. These two major novelties ensure the superiority of our proposed method, as demonstrated on three large-scale public datasets: NTU-RGB + D, Kinetics, and NTU-RGB + D 120, and also shown in the detailed ablation studies. Full article
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