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Developments of Computer Vision and Image Processing: Methodologies and Applications

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A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 22989

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Special Issue Editor

Dr. Manuel José Cabral dos Santos Reis

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Guest Editor

Department of Engineering/IEETA, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: signal & image processing and applications; study and development of devices & systems for friendly smart environments; development of multimedia-based teaching/learning methods and tools, with particular emphasis on the use of the internet
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of technology has enabled a vast and ever-growing number of computer applications in real scenarios of our daily life. This Special Issue, titled “Developments of Computer Vision and Image Processing: Methodologies and Applications”, aims to highlight the recent advances in the development of methodologies, algorithms, techniques and applications in the field of Computer Vision and Image Processing. Exploratory, experimental and theoretical results are very welcome. Review papers are also very welcome. There is no restriction on the length of the papers.

Topics include but are not limited to:

Image, video and scene (both 2D and 3D) processing and understanding.
Remote sensing and satellite image processing.
Construction of computer vision systems.
Medical image processing and applications.
Perceptually guided imaging and vision.
Neural networks and learning for computer vision and image processing.
Emerging techniques for image, video and 3D vision, 3D imaging, visualization, animation, virtual reality and 3DTV.
Object detection, video tracking, object recognition, image restoration, etc.

Prof. Dr. Manuel José Cabral dos Santos Reis
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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 processing
machine learning
image mining
artificial intelligence
intelligent data systems
expert systems

Published Papers (7 papers)

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Editorial

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3 pages, 161 KiB

Open AccessEditorial

Developments of Computer Vision and Image Processing: Methodologies and Applications

by Manuel J. C. S. Reis

Future Internet 2023, 15(7), 233; https://doi.org/10.3390/fi15070233 - 30 Jun 2023

Viewed by 952

Abstract

The rapid advancement of technology has enabled a vast and ever-growing number of computer applications in real scenarios of our daily life [...] Full article

(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)

Research

Jump to: Editorial

21 pages, 7660 KiB

Open AccessArticle

Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data

by Sandulika Abesinghe, Nayomi Kankanamge, Tan Yigitcanlar and Surabhi Pancholi

Future Internet 2023, 15(1), 32; https://doi.org/10.3390/fi15010032 - 9 Jan 2023

Cited by 5 | Viewed by 3020

Abstract

The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited to a small number of people. However, recently, people tend to use social media to express their thoughts and experiences of a place. Taking this into consideration, this paper attempts to explore city images through social media big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the image of a city through Lynchian elements—i.e., landmarks, paths, nodes, edges, and districts—by using community sentiments expressed and images posted on social media platforms. For that, this study conducted various analyses—i.e., descriptive, image processing, sentiment, popularity, and geo-coded social media analyses. The study findings revealed that: (a) the community sentiments toward the same landmarks, paths, nodes, edges, and districts change over time; (b) decisions related to locating landmarks, paths, nodes, edges, and districts have a significant impact on community cognition in perceiving cities; and (c) geo-coded social media data analytics is an invaluable approach to capture the image of a city. The study informs urban authorities in their placemaking efforts by introducing a novel methodological approach to capture an image of a city. Full article

(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)

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19 pages, 24483 KiB

Open AccessArticle

Comparative Analysis of Skeleton-Based Human Pose Estimation

by Jen-Li Chung, Lee-Yeng Ong and Meng-Chew Leow

Future Internet 2022, 14(12), 380; https://doi.org/10.3390/fi14120380 - 15 Dec 2022

Cited by 33 | Viewed by 9764

Abstract

Human pose estimation (HPE) has become a prevalent research topic in computer vision. The technology can be applied in many areas, such as video surveillance, medical assistance, and sport motion analysis. Due to higher demand for HPE, many HPE libraries have been developed in the last 20 years. In the last 5 years, more and more skeleton-based HPE algorithms have been developed and packaged into libraries to provide ease of use for researchers. Hence, the performance of these libraries is important when researchers intend to integrate them into real-world applications for video surveillance, medical assistance, and sport motion analysis. However, a comprehensive performance comparison of these libraries has yet to be conducted. Therefore, this paper aims to investigate the strengths and weaknesses of four popular state-of-the-art skeleton-based HPE libraries for human pose detection, including OpenPose, PoseNet, MoveNet, and MediaPipe Pose. A comparative analysis of these libraries based on images and videos is presented in this paper. The percentage of detected joints (PDJ) was used as the evaluation metric in all comparative experiments to reveal the performance of the HPE libraries. MoveNet showed the best performance for detecting different human poses in static images and videos. Full article

(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)

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16 pages, 3594 KiB

Open AccessArticle

NextDet: Efficient Sparse-to-Dense Object Detection with Attentive Feature Aggregation

by Priyank Kalgaonkar and Mohamed El-Sharkawy

Future Internet 2022, 14(12), 355; https://doi.org/10.3390/fi14120355 - 28 Nov 2022

Cited by 6 | Viewed by 2446

Abstract

Object detection is a computer vision task of detecting instances of objects of a certain class, identifying types of objects, determining its location, and accurately labelling them in an input image or a video. The scope of the work presented within this paper proposes a modern object detection network called NextDet to efficiently detect objects of multiple classes which utilizes CondenseNeXt, an award-winning lightweight image classification convolutional neural network algorithm with reduced number of FLOPs and parameters as the backbone, to efficiently extract and aggregate image features at different granularities in addition to other novel and modified strategies such as attentive feature aggregation in the head, to perform object detection and draw bounding boxes around the detected objects. Extensive experiments and ablation tests, as outlined in this paper, are performed on Argoverse-HD and COCO datasets, which provide numerous temporarily sparse to dense annotated images, demonstrate that the proposed object detection algorithm with CondenseNeXt as the backbone result in an increase in mean Average Precision (mAP) performance and interpretability on Argoverse-HD’s monocular ego-vehicle camera captured scenarios by up to 17.39% as well as COCO’s large set of images of everyday scenes of real-world common objects by up to 14.62%. Full article

(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)

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12 pages, 2727 KiB

Open AccessArticle

YOLO-DFAN: Effective High-Altitude Safety Belt Detection Network

by Wendou Yan, Xiuying Wang and Shoubiao Tan

Future Internet 2022, 14(12), 349; https://doi.org/10.3390/fi14120349 - 23 Nov 2022

Cited by 4 | Viewed by 1775

Abstract

This paper proposes the You Only Look Once (YOLO) dependency fusing attention network (DFAN) detection algorithm, improved based on the lightweight network YOLOv4-tiny. It combines the advantages of fast speed of traditional lightweight networks and high precision of traditional heavyweight networks, so it is very suitable for the real-time detection of high-altitude safety belts in embedded equipment. In response to the difficulty of extracting the features of an object with a low effective pixel ratio—which is an object with a low ratio of actual area to detection anchor area in the YOLOv4-tiny network—we make three major improvements to the baseline network: The first improvement is introducing the atrous spatial pyramid pooling network after CSPDarkNet-tiny extracts features. The second is to propose the DFAN, while the third is to introduce the path aggregation network (PANet) to replace the feature pyramid network (FPN) of the original network and fuse it with the DFAN. According to the experimental results in the high-altitude safety belt dataset, YOLO-DFAN improves the accuracy by 5.13% compared with the original network, and its detection speed meets the real-time demand. The algorithm also exhibits a good improvement on the Pascal voc07+12 dataset. Full article

(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)

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17 pages, 2913 KiB

Open AccessArticle

Facial Expression Recognition Using Dual Path Feature Fusion and Stacked Attention

by Hongtao Zhu, Huahu Xu, Xiaojin Ma and Minjie Bian

Future Internet 2022, 14(9), 258; https://doi.org/10.3390/fi14090258 - 30 Aug 2022

Cited by 2 | Viewed by 2038

Abstract

Facial Expression Recognition (FER) can achieve an understanding of the emotional changes of a specific target group. The relatively small dataset related to facial expression recognition and the lack of a high accuracy of expression recognition are both a challenge for researchers. In recent years, with the rapid development of computer technology, especially the great progress of deep learning, more and more convolutional neural networks have been developed for FER research. Most of the convolutional neural performances are not good enough when dealing with the problems of overfitting from too-small datasets and noise, due to expression-independent intra-class differences. In this paper, we propose a Dual Path Stacked Attention Network (DPSAN) to better cope with the above challenges. Firstly, the features of key regions in faces are extracted using segmentation, and irrelevant regions are ignored, which effectively suppresses intra-class differences. Secondly, by providing the global image and segmented local image regions as training data for the integrated dual path model, the overfitting problem of the deep network due to a lack of data can be effectively mitigated. Finally, this paper also designs a stacked attention module to weight the fused feature maps according to the importance of each part for expression recognition. For the cropping scheme, this paper chooses to adopt a cropping method based on the fixed four regions of the face image, to segment out the key image regions and to ignore the irrelevant regions, so as to improve the efficiency of the algorithm computation. The experimental results on the public datasets, CK+ and FERPLUS, demonstrate the effectiveness of DPSAN, and its accuracy reaches the level of current state-of-the-art methods on both CK+ and FERPLUS, with 93.2% and 87.63% accuracy on the CK+ dataset and FERPLUS dataset, respectively. Full article

(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)

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21 pages, 7028 KiB

Open AccessArticle

Seeing through Wavy Water–Air Interface: A Restoration Model for Instantaneous Images Distorted by Surface Waves

by Bijian Jian, Chunbo Ma, Dejian Zhu, Yixiao Sun and Jun Ao

Future Internet 2022, 14(8), 236; https://doi.org/10.3390/fi14080236 - 29 Jul 2022

Cited by 3 | Viewed by 2004

Abstract

Imaging through a wavy water–air interface is challenging since light rays are bent by unknown amounts, leading to complex geometric distortions. Considering the restoration of instantaneous distorted images, this paper proposes an image recovery model via structured light projection. The algorithm is composed of two separate parts. In the first part, an algorithm for the determination of the instantaneous shape of the water surface via structured light projection is developed. Then, we synchronously recover the distorted airborne scene image through reverse ray tracing in the second part. The experimental results show that, compared with the state-of-the-art methods, the proposed method not only can overcome the influence of changes in natural illumination conditions for WAI reconstruction, but also can significantly reduce the distortion and achieve better performance. Full article

(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)

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