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Applied Sciences
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Applications, Challenges and Promises of Computer Vision and Digital Imaging...
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Applications, Challenges and Promises of Computer Vision and Digital Imaging Processing

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 (30 November 2023) | Viewed by 4489

Special Issue Editors

Dr. Manuel Jesús Rodríguez Valido

E-Mail Website
Guest Editor
Department of Industrial Engineering, Universidad de La Laguna, 38203 San Cristóbal de La Laguna, Spain
Interests: smart sensor networks; FPGA image processing; Internet of Things; autonomous driving; sustainable electric mobility
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Perez Nava

E-Mail Website
Guest Editor
Department of Computer Engineering and Systems, Universidad de La Laguna, 38203 San Cristóbal de La Laguna, Spain
Interests: image and video processing; computer vision; virtual reality
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gustavo Sutter Capristo

E-Mail Website
Guest Editor
Departamento de Tecnología Electrónica y de las Comunicaciones, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Interests: high-performance computing (HPC)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer vision has recently experienced a significant increase in its overall adoption, ranging from facial recognition used in smartphones to navigation in self-driving vehicles. Part of this success can be attributed to the integration of techniques from other areas such as Artificial Intelligence (AI) within the field of Image Processing.

Real-world applications demand new ideas and techniques that solve practical problems. Consequently, this Special Issue is intended to present innovative solutions in this field, from their conception and analysis to their implementation.

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

  • Low-Level Vision Techniques
  • Detection, Recognition, Classification and Localization in 2D/3D
  • Shape estimation
  • 3D and Multiview processing and sensors
  • Motion and Tracking
  • Image and Video Understanding
  • Image/Video Synthesis and Generative Models
  • Integration of AI techniques

In addition, application areas of interest include but are not limited to:

  • Agriculture
  • Healthcare
  • Manufacturing
  • Remote Sensing
  • Retail
  • Robotics
  • Security
  • Sports
  • Transport
  • Virtual Reality

Dr. Manuel Jesús Rodríguez Valido
Dr. Fernando Perez Nava
Prof. Dr. Gustavo Sutter
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

  • computer vision
  • image and video processing
  • image and video understanding
  • computer vision applications
  • machine learning
  • artificial intelligence

Published Papers (5 papers)

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Research

29 pages, 9127 KiB  
Article
A Remote Access Server with Chatbot User Interface for Coffee Grinder Burr Wear Level Assessment Based on Imaging Granule Analysis and Deep Learning Techniques
by Chih-Yung Chen, Shang-Feng Lin, Yuan-Wei Tseng, Zhe-Wei Dong and Cheng-Han Cai
Appl. Sci. 2024, 14(3), 1315; https://doi.org/10.3390/app14031315 - 05 Feb 2024
Viewed by 721
Abstract
Coffee chains are very popular around the world. Because overly worn coffee grinder burrs can downgrade the taste of coffee, coffee experts and professional cuppers in an anonymous coffee chain have developed a manual method to classify coffee grinder burr wear so that [...] Read more.
Coffee chains are very popular around the world. Because overly worn coffee grinder burrs can downgrade the taste of coffee, coffee experts and professional cuppers in an anonymous coffee chain have developed a manual method to classify coffee grinder burr wear so that worn burrs can be replaced in time to maintain the good taste of coffee. In this paper, a remote access server system that can mimic the ability of those recognized coffee experts and professional cuppers to classify coffee grinder burr wear has been developed. Users only need to first upload a photo of coffee granules ground by a grinder to the system through a chatbot interface; then, they can receive the burr wear classification result from the remote server in a minute. The system first uses image processing to obtain the coffee granules’ size distribution. Based on the size distributions, unified length data inputs are then obtained to train and test the deep learning model so that it can classify the burr wear level into initial wear, normal wear, and severe wear with more than 96% accuracy. As only a mobile phone is needed to use this service, the proposed system is very suitable for both coffee chains and coffee lovers. Full article
(This article belongs to the Special Issue Applications, Challenges and Promises of Computer Vision and Digital Imaging Processing)
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17 pages, 2294 KiB  
Article
Action Detection for Wildlife Monitoring with Camera Traps Based on Segmentation with Filtering of Tracklets (SWIFT) and Mask-Guided Action Recognition (MAROON)
by Frank Schindler, Volker Steinhage, Suzanne T. S. van Beeck Calkoen and Marco Heurich
Appl. Sci. 2024, 14(2), 514; https://doi.org/10.3390/app14020514 - 06 Jan 2024
Cited by 1 | Viewed by 917
Abstract
Behavioral analysis of animals in the wild plays an important role for ecological research and conservation and has been mostly performed by researchers. We introduce an action detection approach that automates this process by detecting animals and performing action recognition on the detected [...] Read more.
Behavioral analysis of animals in the wild plays an important role for ecological research and conservation and has been mostly performed by researchers. We introduce an action detection approach that automates this process by detecting animals and performing action recognition on the detected animals in camera trap videos. Our action detection approach is based on SWIFT (segmentation with filtering of tracklets), which we have already shown to successfully detect and track animals in wildlife videos, and MAROON (mask-guided action recognition), an action recognition network that we are introducing here. The basic ideas of MAROON are the exploitation of the instance masks detected by SWIFT and a triple-stream network. The instance masks enable more accurate action recognition, especially if multiple animals appear in a video at the same time. The triple-stream approach extracts features for the motion and appearance of the animal. We evaluate the quality of our action recognition on two self-generated datasets, from an animal enclosure and from the wild. These datasets contain videos of red deer, fallow deer and roe deer, recorded both during the day and night. MAROON improves the action recognition accuracy compared to other state-of-the-art approaches by an average of 10 percentage points on all analyzed datasets and achieves an accuracy of 69.16% on the Rolandseck Daylight dataset, in which 11 different action classes occur. Our action detection system makes it possible todrasticallyreduce the manual work of ecologists and at the same time gain new insights through standardized results. Full article
(This article belongs to the Special Issue Applications, Challenges and Promises of Computer Vision and Digital Imaging Processing)
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23 pages, 19239 KiB  
Article
Comparison of the Performance of Convolutional Neural Networks and Vision Transformer-Based Systems for Automated Glaucoma Detection with Eye Fundus Images
by Silvia Alayón, Jorge Hernández, Francisco J. Fumero, Jose F. Sigut and Tinguaro Díaz-Alemán
Appl. Sci. 2023, 13(23), 12722; https://doi.org/10.3390/app132312722 - 27 Nov 2023
Viewed by 901
Abstract
Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification [...] Read more.
Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification of eye fundus images. Among these intelligent systems, Convolutional Neural Networks (CNNs) stand out, although alternatives have recently appeared, such as Vision Transformers (ViTs) or hybrid systems, which are also highly efficient in image processing. The question that arises in the face of so many emerging methods is whether all these new techniques are really more efficient for the problem of glaucoma diagnosis than the CNNs that have been used so far. In this article, we present a comprehensive comparative study of all these DL models in glaucoma detection, with the aim of elucidating which strategies are significantly better. Our main conclusion is that there are no significant differences between the efficiency of both DL strategies for the medical diagnostic problem addressed. Full article
(This article belongs to the Special Issue Applications, Challenges and Promises of Computer Vision and Digital Imaging Processing)
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16 pages, 6034 KiB  
Article
Computer-Aided Visual Inspection of Glass-Coated Tableware Ceramics for Multi-Class Defect Detection
by Rafaela Carvalho, Ana C. Morgado, João Gonçalves, Anil Kumar, Alberto Gil e Sá Rolo, Rui Carreira and Filipe Soares
Appl. Sci. 2023, 13(21), 11708; https://doi.org/10.3390/app132111708 - 26 Oct 2023
Cited by 1 | Viewed by 770
Abstract
Quality control procedures in the manufacturing of tableware ceramics require a demanding, monotonous, subjective, and faulty human manual inspection. This paper presents two machine learning strategies and the results of a semi-automated visual inspection of ceramics tableware applied to a private dataset acquired [...] Read more.
Quality control procedures in the manufacturing of tableware ceramics require a demanding, monotonous, subjective, and faulty human manual inspection. This paper presents two machine learning strategies and the results of a semi-automated visual inspection of ceramics tableware applied to a private dataset acquired during the VAICeramics project. In one method, an anomaly detection step was integrated to pre-select possible defective patches before passing through an object detector and defects classifier. In the alternative one, all patches are directly provided to the object detector and then go through the classification phase. Contrary to expectations, the inclusion of the anomaly detector demonstrated a slight reduction in the performance of the pipeline, which may result from error propagation. Regarding the proposed methodology for defect detection, it exhibits average performance in monochromatic images with more than 600 real defects in total, efficiently identifying the most common defect classes in highly reflective surfaces. However, when applied to newly acquired images, the pipeline encounters challenges revealing a lack of generalization ability and experiencing limitations in detecting specific defect classes, due to their appearance and limited available samples used for training. Only two defect types presented high classification performance, namely Dots and Cracked defects. Full article
(This article belongs to the Special Issue Applications, Challenges and Promises of Computer Vision and Digital Imaging Processing)
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15 pages, 6249 KiB  
Article
Software Application for Automatic Detection and Analysis of Biomass in Underwater Videos
by Manuel Rodríguez Valido, Peña Fabiani Bendicho, Miguel Martín Reyes and Alicia Rodríguez-Juncá
Appl. Sci. 2023, 13(19), 10870; https://doi.org/10.3390/app131910870 - 30 Sep 2023
Viewed by 765
Abstract
The use of underwater recording is widely implemented across different marine ecology studies as a substitute for more invasive techniques. This is the case of the Deep Scattering Layer (DSL), a biomass-rich layer in the ocean located between 400 and 600 m deep. [...] Read more.
The use of underwater recording is widely implemented across different marine ecology studies as a substitute for more invasive techniques. This is the case of the Deep Scattering Layer (DSL), a biomass-rich layer in the ocean located between 400 and 600 m deep. The data processing of underwater videos has usually been carried out manually or targets organisms above a certain size. Marine snow, or macroscopic amorphous aggregates, plays a major role in nutrient cycles and in the supply of organic material for organisms living in the deeper layers of the ocean. Marine snow, therefore, should be taken into account when estimating biomass abundance in the water column. The main objective of this project is to develop a new software application for the automatic detection and analysis of biomass abundance relative to time in underwater videos, taking into consideration small items. The application software is based on a pipeline and client-server architecture, developed in Python and using open source libraries. The software was trained with underwater videos of the DSL recorded with low-cost equipment. A usability study carried out with end-users shows satisfaction with the user-friendly interface and the expected results. The software application developed is capable of automatically detecting small items captured by underwater videos. In addition, it can be easily adapted to a web application. Full article
(This article belongs to the Special Issue Applications, Challenges and Promises of Computer Vision and Digital Imaging Processing)
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