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Advances in Image Processing and Pattern Recognition
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Advances in Image Processing and Pattern Recognition

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 8131

Special Issue Editors

Dr. Yinsheng Chen

E-Mail Website
Guest Editor
1. Signal and Information Processing for Sensing Systems, Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain
2. School of Measurements and Communication Engineering, Harbin University of Science and Technology, Harbin, China
Interests: intelligent sensing technology; machine vision; machine olfaction; pattern recognition; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Aili Wang

E-Mail Website
Co-Guest Editor
Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China
Interests: remote sensing image processing; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haibin Wu

E-Mail Website
Co-Guest Editor
Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China
Interests: machine vision; visual detection and image processing; medical virtual reality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, image processing technology has developed rapidly and become the most successful applied intelligent sensing technology. Image is the main source of human acquisition and exchange of information, so the application of image processing is inevitably involved in all aspects of human life and work. At present, image processing technology has played an important role in the fields of aerospace, public security, biomedicine, industrial engineering, and business communication. Image processing addresses the signal output from image sensors and involves many signal processing methods. Pattern recognition is an important research field in image processing that includes image preprocessing, feature extraction and selection, classifier design, and classification decision.

In this context, this Special Issue on “Advances in Image Processing and Pattern Recognition” invites original research and comprehensive reviews on, including but not limited to:

  • Advances in image preprocessing;
  • Advances in features and selection of images;
  • Advances in pattern recognition in image processing technology;
  • Applications of image signal processing in:
  • Fire detection based on image processing, including indoor fire, forest fire, grassland fire, etc.;
  • Image processing in intelligent transportation;
  • Hyperspectral image processing;
  • Biomedical image processing;
  • Image processing in intelligent monitoring;
  • Deep learning for image processing;
  • Smart monitoring and assisted living systems;
  • AI-based image processing, understanding, recognition, compression, and reconstruction.

Dr. Yinsheng Chen
Dr. Aili Wang
Dr. Haibin Wu
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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • image preprocessing technology
  • image feature extraction and selection
  • image identification
  • hyperspectral image processing
  • medical image processing
  • public safety monitoring
  • exploration of resources and mineral deposits
  • environmental monitoring

Published Papers (3 papers)

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Research

16 pages, 836 KiB  
Article
Cascading Pose Features with CNN-LSTM for Multiview Human Action Recognition
by Najeeb ur Rehman Malik, Syed Abdul Rahman Abu-Bakar, Usman Ullah Sheikh, Asma Channa and Nirvana Popescu
Signals 2023, 4(1), 40-55; https://doi.org/10.3390/signals4010002 - 04 Jan 2023
Cited by 8 | Viewed by 3111
Abstract
Human Action Recognition (HAR) is a branch of computer vision that deals with the identification of human actions at various levels including low level, action level, and interaction level. Previously, a number of HAR algorithms have been proposed based on handcrafted methods for [...] Read more.
Human Action Recognition (HAR) is a branch of computer vision that deals with the identification of human actions at various levels including low level, action level, and interaction level. Previously, a number of HAR algorithms have been proposed based on handcrafted methods for action recognition. However, the handcrafted techniques are inefficient in case of recognizing interaction level actions as they involve complex scenarios. Meanwhile, the traditional deep learning-based approaches take the entire image as an input and later extract volumes of features, which greatly increase the complexity of the systems; hence, resulting in significantly higher computational time and utilization of resources. Therefore, this research focuses on the development of an efficient multi-view interaction level action recognition system using 2D skeleton data with higher accuracy while reducing the computation complexity based on deep learning architecture. The proposed system extracts 2D skeleton data from the dataset using the OpenPose technique. Later, the extracted 2D skeleton features are given as an input directly to the Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) architecture for action recognition. To reduce the complexity, instead of passing the whole image, only extracted features are given to the CNN-LSTM architecture, thus eliminating the need for feature extraction. The proposed method was compared with other existing methods, and the outcomes confirm the potential of the proposed technique. The proposed OpenPose-CNNLSTM achieved an accuracy of 94.4% for MCAD (Multi-camera action dataset) and 91.67% for IXMAS (INRIA Xmas Motion Acquisition Sequences). Our proposed method also significantly decreases the computational complexity by reducing the number of inputs features to 50. Full article
(This article belongs to the Special Issue Advances in Image Processing and Pattern Recognition)
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13 pages, 2749 KiB  
Article
Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
by Jonathan Piper, Peter W. T. Yuen and David James
Signals 2022, 3(4), 752-764; https://doi.org/10.3390/signals3040045 - 26 Oct 2022
Cited by 1 | Viewed by 1423
Abstract
In recent years, a wide range of hyperspectral imaging systems using coded apertures have been proposed. Many implement compressive sensing to achieve faster acquisition of a hyperspectral data cube, but it is also potentially beneficial to use coded aperture imaging in sensors that [...] Read more.
In recent years, a wide range of hyperspectral imaging systems using coded apertures have been proposed. Many implement compressive sensing to achieve faster acquisition of a hyperspectral data cube, but it is also potentially beneficial to use coded aperture imaging in sensors that capture full-rank (non-compressive) measurements. In this paper we analyse the signal-to-noise ratio for such a sensor, which uses a Hadamard code pattern of slits instead of the single slit of a typical pushbroom imaging spectrometer. We show that the coded slit sensor may have performance advantages in situations where the dominant noise sources do not depend on the signal level; but that where Shot noise dominates a conventional single-slit sensor would be more effective. These results may also have implications for the utility of compressive sensing systems. Full article
(This article belongs to the Special Issue Advances in Image Processing and Pattern Recognition)
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18 pages, 759 KiB  
Article
An Empirical Study on Ensemble of Segmentation Approaches
by Loris Nanni, Alessandra Lumini, Andrea Loreggia, Alberto Formaggio and Daniela Cuza
Signals 2022, 3(2), 341-358; https://doi.org/10.3390/signals3020022 - 01 Jun 2022
Cited by 12 | Viewed by 2642
Abstract
Recognizing objects in images requires complex skills that involve knowledge about the context and the ability to identify the borders of the objects. In computer vision, this task is called semantic segmentation and it pertains to the classification of each pixel in an [...] Read more.
Recognizing objects in images requires complex skills that involve knowledge about the context and the ability to identify the borders of the objects. In computer vision, this task is called semantic segmentation and it pertains to the classification of each pixel in an image. The task is of main importance in many real-life scenarios: in autonomous vehicles, it allows the identification of objects surrounding the vehicle; in medical diagnosis, it improves the ability of early detecting of dangerous pathologies and thus mitigates the risk of serious consequences. In this work, we propose a new ensemble method able to solve the semantic segmentation task. The model is based on convolutional neural networks (CNNs) and transformers. An ensemble uses many different models whose predictions are aggregated to form the output of the ensemble system. The performance and quality of the ensemble prediction are strongly connected with some factors; one of the most important is the diversity among individual models. In our approach, this is enforced by adopting different loss functions and testing different data augmentations. We developed the proposed method by combining DeepLabV3+, HarDNet-MSEG, and Pyramid Vision Transformers. The developed solution was then assessed through an extensive empirical evaluation in five different scenarios: polyp detection, skin detection, leukocytes recognition, environmental microorganism detection, and butterfly recognition. The model provides state-of-the-art results. Full article
(This article belongs to the Special Issue Advances in Image Processing and Pattern Recognition)
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