Research on Image Analysis and Computer Vision

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 3280

Special Issue Editors

Department of Information Science, Xi’an Jiaotong University, Xi’an 710049, China
Interests: machine learning; hyperspectral unmixing of remote sensing images; remote sensing image fusion; data mining; intelligent computing
Special Issues, Collections and Topics in MDPI journals
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: scientific computing; machine learning; computer vision

Special Issue Information

Dear Colleagues,

Image analysis and computer vision mainly focuses on processing, analyzing and understanding digital images. Through model building and algorithm designing, this field attempts to solve many practical problems in human society. In the context of large models with big data, establishing novel models, designing effective algorithms, and exploring the underlying mechanisms of images and vision have been becoming the research focus in this field. This Special Issue aims at the latest and full-length research in the areas of image analysis and computer vision. Particularly, this Special Issue focuses on low-/high-level tasks of image analysis and vision. We are especially looking for bio-based, statistical, learning-based, or other research. Theoretical or heuristic approaches are suitable. Containing multi-level research, this Special Issue is expected to bring comprehensive advances in the field of image analysis and computer vision.

Topics of interest include but are not limited to the following points:

  1. General image analysis, such as classification, object detection, segmentation, super-resolution, visualization, etc.
  2. Remote sensing image analysis, such as spectral unmixing, pansharpening, fusion, etc.
  3. Combining of traditional methods and deep learning for image analysis and vision tasks.
  4. Multi-modality image analysis and computer vision.
  5. Few-shot and no-shot approaches for image analysis.
  6. Unsupervised, generative, transfer learning models.

Prof. Dr. Junmin Liu
Prof. Dr. Xi-Le Zhao
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

  • image analysis
  • computer vision
  • machine learning
  • artificial intelligence

Published Papers (3 papers)

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Research

22 pages, 7249 KiB  
Article
Harnessing Energy Balance and Genetic Algorithms for Efficient Building Demolition
by Kun Chen, Yun Wang and Zenggang Lin
Appl. Sci. 2023, 13(22), 12491; https://doi.org/10.3390/app132212491 - 19 Nov 2023
Viewed by 575
Abstract
In the realm of building demolition, ensuring the uniform distribution of energy from multiple sources is of paramount significance for the systematic deconstruction of large structures. This study presents an integrated methodology that combines genetic optimization and potential energy balance to determine the [...] Read more.
In the realm of building demolition, ensuring the uniform distribution of energy from multiple sources is of paramount significance for the systematic deconstruction of large structures. This study presents an integrated methodology that combines genetic optimization and potential energy balance to determine the most suitable locations for multiple energy release points, thereby enhancing the efficiency and reliability of the demolition process. We initiate our approach by randomly selecting energy release points within a building model and subsequently simulate energy dispersion utilizing a potential function until reaching stable boundaries. In instances where the discrepancy in the area between the regions with maximum and minimum energy dispersion exceeds a predefined threshold, we instigate an optimization process employing genetic algorithms. This optimization process involves genetic crossover and mutation operations, followed by subsequent energy balance calculations. The result is not only an improvement in demolition efficiency but also an assurance of even energy coverage throughout the target area. Full article
(This article belongs to the Special Issue Research on Image Analysis and Computer Vision)
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23 pages, 3279 KiB  
Article
An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images
by Teresa Kwamboka Abuya, Richard Maina Rimiru and George Onyango Okeyo
Appl. Sci. 2023, 13(21), 12069; https://doi.org/10.3390/app132112069 - 06 Nov 2023
Viewed by 1311
Abstract
Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures [...] Read more.
Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation. Full article
(This article belongs to the Special Issue Research on Image Analysis and Computer Vision)
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15 pages, 4273 KiB  
Article
Fusion of Attention-Based Convolution Neural Network and HOG Features for Static Sign Language Recognition
by Diksha Kumari and Radhey Shyam Anand
Appl. Sci. 2023, 13(21), 11993; https://doi.org/10.3390/app132111993 - 03 Nov 2023
Cited by 1 | Viewed by 822
Abstract
The deaf and hearing-impaired community expresses their emotions, communicates with society, and enhances the interaction between humans and computers using sign language gestures. This work presents a strategy for efficient feature extraction that uses a combination of two different methods that are the [...] Read more.
The deaf and hearing-impaired community expresses their emotions, communicates with society, and enhances the interaction between humans and computers using sign language gestures. This work presents a strategy for efficient feature extraction that uses a combination of two different methods that are the convolutional block attention module (CBAM)-based convolutional neural network (CNN) and standard handcrafted histogram of oriented gradients (HOG) feature descriptor. The proposed framework aims to enhance accuracy by extracting meaningful features and resolving issues like rotation, similar hand orientation, etc. The HOG feature extraction technique provides a compact feature representation that signifies meaningful information about sign gestures. The CBAM attention module is incorporated into the structure of CNN to enhance feature learning using spatial and channel attention mechanisms. Then, the final feature vector is formed by concatenating these features. This feature vector is provided to the classification layers to predict static sign gestures. The proposed approach is validated on two publicly available static Massey American Sign Language (ASL) and Indian Sign Language (ISL) databases. The model’s performance is evaluated using precision, recall, F1-score, and accuracy. Our proposed methodology achieved 99.22% and 99.79% accuracy for the ASL and ISL datasets. The acquired results signify the efficiency of the feature fusion and attention mechanism. Our network performed better in accuracy compared to the earlier studies. Full article
(This article belongs to the Special Issue Research on Image Analysis and Computer Vision)
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