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Computational Intelligence in Image Analysis
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Computational Intelligence in Image Analysis

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 10435

Special Issue Editors

Prof. Dr. Piotr S. Szczepaniak

E-Mail
Guest Editor
Institute of Information Technology, Lodz University of Technology, 90-924 Lodz, Poland
Interests: automatic image analysis and interpretation; pattern recognition; computational intelligence; knowledge extraction; medical applications
Dr. Arkadiusz Tomczyk

E-Mail Website
Guest Editor
Institute of Information Technology, Lodz University of Technology, 90-924 Lodz, Poland
Interests: automatic image analysis and interpretation; machine learning; deep learning; active contours; structured prediction; alternative image representations; medical applications

Special Issue Information

Dear Colleagues,

Image analysis is a classic yet still developing area of significant practical importance. There are different goals and numerous approaches related to image-based services. However, typical image analysis techniques have limitations. For increasingly challenging computer vision tasks, it is not always possible to fully understand the application domain and, in consequence, to perform precise mathematical modelling of the considered problem. Nature-inspired computational methodologies, called computational intelligence, can overcome those problems. They can automatically find a solution based on data or experimental observation. The number of methods and their variants within computational intelligence is growing, and they are characterized by a commonly used term, machine learning. Currently, deep learning is the most popular approach. 

The purpose of this Special Issue is to encourage researchers to present and exchange ideas. The connection of classic image analysis techniques with modern computational intelligence can be beneficial for both of these domains. Machine learning can be a powerful tool, allowing us to tune parameters of well-designed mathematical formulas, whereas domain-knowledge can enable the learning of models if the amount of training data is not sufficient.  

Papers must be related to computational intelligence. Topics included but are not limited to:  

  • Intelligent image registration and fusion. 
  • Intelligent feature extraction and feature selection. 
  • Intelligent granular computing:
    • Novel alternative image representations. 
    • Granule-based segmentation and classification. 
  • Deep learning.  
  • Segmentation approaches:  
    • Structure based.  
    • Active contours.
  • Classification of objects and images:  
    • Fuzzy classification. 
    • Rule-based approach.  
    • Neural methods. 
    • Multi-label classification.  
  • Intelligent object detection. 
  • Medical applications. 

Prof. Dr. Piotr S. Szczepaniak
Dr. Arkadiusz Tomczyk
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. 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 2600 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.

Published Papers (5 papers)

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Research

17 pages, 9092 KiB  
Article
Segmentation for Multi-Rock Types on Digital Outcrop Photographs Using Deep Learning Techniques
by Owais A. Malik, Idrus Puasa and Daphne Teck Ching Lai
Sensors 2022, 22(21), 8086; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218086 - 22 Oct 2022
Cited by 7 | Viewed by 1471
Abstract
The basic identification and classification of sedimentary rocks into sandstone and mudstone are important in the study of sedimentology and they are executed by a sedimentologist. However, such manual activity involves countless hours of observation and data collection prior to any interpretation. When [...] Read more.
The basic identification and classification of sedimentary rocks into sandstone and mudstone are important in the study of sedimentology and they are executed by a sedimentologist. However, such manual activity involves countless hours of observation and data collection prior to any interpretation. When such activity is conducted in the field as part of an outcrop study, the sedimentologist is likely to be exposed to challenging conditions such as the weather and their accessibility to the outcrops. This study uses high-resolution photographs which are acquired from a sedimentological study to test an alternative basic multi-rock identification through machine learning. While existing studies have effectively applied deep learning techniques to classify the rock types in field rock images, their approaches only handle a single rock-type classification per image. One study applied deep learning techniques to classify multi-rock types in each image; however, the test was performed on artificially overlaid images of different rock types in a test sample and not of naturally occurring rock surfaces of multiple rock types. To the best of our knowledge, no study has applied semantic segmentation to solve the multi-rock classification problem using digital photographs of multiple rock types. This paper presents the application of two state-of-the-art segmentation models, namely U-Net and LinkNet, to identify multiple rock types in digital photographs by segmenting the sandstone, mudstone, and background classes in a self-collected dataset of 102 images from a field in Brunei Darussalam. Four pre-trained networks, including Resnet34, Inceptionv3, VGG16, and Efficientnetb7 were used as a backbone for both models, and the performances of the individual models and their ensembles were compared. We also investigated the impact of image enhancement and different color representations on the performances of these segmentation models. The experiment results of this study show that among the individual models, LinkNet with Efficientnetb7 as a backbone had the best performance with a mean over intersection (MIoU) value of 0.8135 for all of the classes. While the ensemble of U-Net models (with all four backbones) performed slightly better than the LinkNet with Efficientnetb7 did with an MIoU of 0.8201. When different color representations and image enhancements were explored, the best performance (MIoU = 0.8178) was noticed for the L*a*b* color representation with Efficientnetb7 using U-Net segmentation. For the individual classes of interest (sandstone and mudstone), U-Net with Efficientnetb7 was found to be the best model for the segmentation. Thus, this study presents the potential of semantic segmentation in automating the reservoir characterization process whereby we can extract the patches of interest from the rocks for much deeper study and modeling to be conducted. Full article
(This article belongs to the Special Issue Computational Intelligence in Image Analysis)
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12 pages, 4419 KiB  
Article
Rethinking Gradient Weight’s Influence over Saliency Map Estimation
by Masud An Nur Islam Fahim, Nazmus Saqib, Shafkat Khan Siam and Ho Yub Jung
Sensors 2022, 22(17), 6516; https://0-doi-org.brum.beds.ac.uk/10.3390/s22176516 - 29 Aug 2022
Cited by 1 | Viewed by 1534
Abstract
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural network’s prediction. Gradient-based methods are generally faster than other branches of vision interpretability and independent of human guidance. The performance of CAM-like studies depends on the governing [...] Read more.
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural network’s prediction. Gradient-based methods are generally faster than other branches of vision interpretability and independent of human guidance. The performance of CAM-like studies depends on the governing model’s layer response and the influences of the gradients. Typical gradient-oriented CAM studies rely on weighted aggregation for saliency map estimation by projecting the gradient maps into single-weight values, which may lead to an over-generalized saliency map. To address this issue, we use a global guidance map to rectify the weighted aggregation operation during saliency estimation, where resultant interpretations are comparatively cleaner and instance-specific. We obtain the global guidance map by performing elementwise multiplication between the feature maps and their corresponding gradient maps. To validate our study, we compare the proposed study with nine different saliency visualizers. In addition, we use seven commonly used evaluation metrics for quantitative comparison. The proposed scheme achieves significant improvement over the test images from the ImageNet, MS-COCO 14, and PASCAL VOC 2012 datasets. Full article
(This article belongs to the Special Issue Computational Intelligence in Image Analysis)
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26 pages, 1592 KiB  
Article
Making the Most of Single Sensor Information: A Novel Fusion Approach for 3D Face Recognition Using Region Covariance Descriptors and Gaussian Mixture Models
by Janez Križaj, Simon Dobrišek and Vitomir Štruc
Sensors 2022, 22(6), 2388; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062388 - 20 Mar 2022
Cited by 4 | Viewed by 1703
Abstract
Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process [...] Read more.
Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process suffer. In this paper, we focus on face recognition using images captured by a single 3D sensor and propose a method based on the use of region covariance matrixes and Gaussian mixture models (GMMs). All steps of the proposed framework are automated, and no metadata, such as pre-annotated eye, nose, or mouth positions is required, while only a very simple clustering-based face detection is performed. The framework computes a set of region covariance descriptors from local regions of different face image representations and then uses the unscented transform to derive low-dimensional feature vectors, which are finally modeled by GMMs. In the last step, a support vector machine classification scheme is used to make a decision about the identity of the input 3D facial image. The proposed framework has several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrixes), the ability to explore facial images at different levels of locality, and the ability to integrate a domain-specific prior knowledge into the modeling procedure. Several normalization techniques are incorporated into the proposed framework to further improve performance. Extensive experiments are performed on three prominent databases (FRGC v2, CASIA, and UMB-DB) yielding competitive results. Full article
(This article belongs to the Special Issue Computational Intelligence in Image Analysis)
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17 pages, 21831 KiB  
Article
Detection and Mosaicing Techniques for Low-Quality Retinal Videos
by José Camara, Bruno Silva, António Gouveia, Ivan Miguel Pires, Paulo Coelho and António Cunha
Sensors 2022, 22(5), 2059; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052059 - 07 Mar 2022
Viewed by 2045
Abstract
Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence [...] Read more.
Ideally, to carry out screening for eye diseases, it is expected to use specialized medical equipment to capture retinal fundus images. However, since this kind of equipment is generally expensive and has low portability, and with the development of technology and the emergence of smartphones, new portable and cheaper screening options have emerged, one of them being the D-Eye device. When compared to specialized equipment, this equipment and other similar devices associated with a smartphone present lower quality and less field-of-view in the retinal video captured, yet with sufficient quality to perform a medical pre-screening. Individuals can be referred for specialized screening to obtain a medical diagnosis if necessary. Two methods were proposed to extract the relevant regions from these lower-quality videos (the retinal zone). The first one is based on classical image processing approaches such as thresholds and Hough Circle transform. The other performs the extraction of the retinal location by applying a neural network, which is one of the methods reported in the literature with good performance for object detection, the YOLO v4, which was demonstrated to be the preferred method to apply. A mosaicing technique was implemented from the relevant retina regions to obtain a more informative single image with a higher field of view. It was divided into two stages: the GLAMpoints neural network was applied to extract relevant points in the first stage. Some homography transformations are carried out to have in the same referential the overlap of common regions of the images. In the second stage, a smoothing process was performed in the transition between images. Full article
(This article belongs to the Special Issue Computational Intelligence in Image Analysis)
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11 pages, 4149 KiB  
Communication
A Fast Two-Stage Bilateral Filter Using Constant Time O(1) Histogram Generation
by Sheng-Wei Cheng, Yi-Ting Lin and Yan-Tsung Peng
Sensors 2022, 22(3), 926; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030926 - 25 Jan 2022
Cited by 9 | Viewed by 2716
Abstract
Bilateral Filtering (BF) is an effective edge-preserving smoothing technique in image processing. However, an inherent problem of BF for image denoising is that it is challenging to differentiate image noise and details with the range kernel, thus often preserving both noise and edges [...] Read more.
Bilateral Filtering (BF) is an effective edge-preserving smoothing technique in image processing. However, an inherent problem of BF for image denoising is that it is challenging to differentiate image noise and details with the range kernel, thus often preserving both noise and edges in denoising. This letter proposes a novel Dual-Histogram BF (DHBF) method that exploits an edge-preserving noise-reduced guidance image to compute the range kernel, removing isolated noisy pixels for better denoising results. Furthermore, we approximate the spatial kernel using mean filtering based on column histogram construction to achieve constant-time filtering regardless of the kernel radius’ size and achieve better smoothing. Experimental results on multiple benchmark datasets for denoising show that the proposed DHBF outperforms other state-of-the-art BF methods. Full article
(This article belongs to the Special Issue Computational Intelligence in Image Analysis)
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