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Applied Sciences
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Intelligent Processing on Image and Optical Information, Volume III
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Intelligent Processing on Image and Optical Information, Volume III

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 18993

Special Issue Editor

Prof. Dr. Seokwon Yeom

E-Mail Website
Guest Editor
School of AI, Degu University, Gyeongsan 38453, Republic of Korea
Interests: intelligent image processing; deep/machine learning; target tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent image and optical information processing have significantly contributed to the recent epoch of artificial intelligence and smart cars. Certainly, information acquired by various imaging techniques is of tremendous value, and thus, intelligent analysis of them is necessary to make the best use of them.

This Special Issue focuses on the vast range of imaging methods to acquire intelligent processing of image and optical information. Images are commonly formed via visible light; three-dimensional information is acquired by multiview imaging or digital holography; and infrared, terahertz, and millimeter waves are good resources in a nonvisible environment. Synthetic aperture radar and radiographic or ultrasonic imaging constitute military, industrial, and medical regimes. The objectives of intelligent processing range from the refinement of raw data to the symbolic representation and visualization of the real world. This comes through unsupervised or supervised learning based on statistical and mathematical models or computational algorithms.

Intelligent processing on image and optical information has been widely involved in a variety of research fields, such as video surveillance, biometric recognition, non-destructive testing, medical diagnosis, robotic sensing, compressed sensing, autonomous driving, three-dimensional scene reconstruction, and others. The latest technological developments will be shared through this Special Issue. We invite researchers and investigators to contribute their original research or review articles to this Special Issue.

Prof. Dr. Seokwon Yeom
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.

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Keywords

  • Intelligent image processing
  • Machine and robot vision
  • Optical information processing
  • IR, THz, MMW, SAR image analysis
  • Bio-medical image analysis
  • Three-dimensional information processing
  • Image detection, recognition, and tracking
  • Segmentation and feature extraction
  • Image registration and fusion
  • Image enhancement and restoration

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Published Papers (6 papers)

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Editorial

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3 pages, 165 KiB  
Editorial
Special Issue on Intelligent Processing on Image and Optical Information III
by Seokwon Yeom
Appl. Sci. 2023, 13(15), 8898; https://doi.org/10.3390/app13158898 - 2 Aug 2023
Viewed by 640
Abstract
Intelligent image and optical information processing have paved the way for the recent epoch of the new intelligence and information era [...] Full article
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information, Volume III)

Research

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21 pages, 29412 KiB  
Article
BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network
by Abu Saleh Musa Miah, Jungpil Shin, Md Al Mehedi Hasan and Md Abdur Rahim
Appl. Sci. 2022, 12(8), 3933; https://doi.org/10.3390/app12083933 - 13 Apr 2022
Cited by 41 | Viewed by 4127
Abstract
Sign language recognition is one of the most challenging applications in machine learning and human-computer interaction. Many researchers have developed classification models for different sign languages such as English, Arabic, Japanese, and Bengali; however, no significant research has been done on the general-shape [...] Read more.
Sign language recognition is one of the most challenging applications in machine learning and human-computer interaction. Many researchers have developed classification models for different sign languages such as English, Arabic, Japanese, and Bengali; however, no significant research has been done on the general-shape performance for different datasets. Most research work has achieved satisfactory performance with a small dataset. These models may fail to replicate the same performance for evaluating different and larger datasets. In this context, this paper proposes a novel method for recognizing Bengali sign language (BSL) alphabets to overcome the issue of generalization. The proposed method has been evaluated with three benchmark datasets such as ‘38 BdSL’, ‘KU-BdSL’, and ‘Ishara-Lipi’. Here, three steps are followed to achieve the goal: segmentation, augmentation, and Convolutional neural network (CNN) based classification. Firstly, a concatenated segmentation approach with YCbCr, HSV and watershed algorithm was designed to accurately identify gesture signs. Secondly, seven image augmentation techniques are selected to increase the training data size without changing the semantic meaning. Finally, the CNN-based model called BenSignNet was applied to extract the features and classify purposes. The performance accuracy of the model achieved 94.00%, 99.60%, and 99.60% for the BdSL Alphabet, KU-BdSL, and Ishara-Lipi datasets, respectively. Experimental findings confirmed that our proposed method achieved a higher recognition rate than the conventional ones and accomplished a generalization property in all datasets for the BSL domain. Full article
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information, Volume III)
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11 pages, 3205 KiB  
Article
Weakly Supervised Learning for Object Localization Based on an Attention Mechanism
by Nojin Park and Hanseok Ko
Appl. Sci. 2021, 11(22), 10953; https://doi.org/10.3390/app112210953 - 19 Nov 2021
Cited by 2 | Viewed by 2057
Abstract
Recently, deep learning has been successfully applied to object detection and localization tasks in images. When setting up deep learning frameworks for supervised training with large datasets, strongly labeling the objects facilitates good performance; however, the complexity of the image scene and large [...] Read more.
Recently, deep learning has been successfully applied to object detection and localization tasks in images. When setting up deep learning frameworks for supervised training with large datasets, strongly labeling the objects facilitates good performance; however, the complexity of the image scene and large size of the dataset make this a laborious task. Hence, it is of paramount importance that the expensive work associated with the tasks involving strong labeling, such as bounding box annotation, is reduced. In this paper, we propose a method to perform object localization tasks without bounding box annotation in the training process by means of employing a two-path activation-map-based classifier framework. In particular, we develop an activation-map-based framework to judicially control the attention map in the perception branch by adding a two-feature extractor so that better attention weights can be distributed to induce improved performance. The experimental results indicate that our method surpasses the performance of the existing deep learning models based on weakly supervised object localization. The experimental results show that the proposed method achieves the best performance, with 75.21% Top-1 classification accuracy and 55.15% Top-1 localization accuracy on the CUB-200-2011 dataset. Full article
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information, Volume III)
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18 pages, 6597 KiB  
Article
Multilateration Approach for Wide Range Visible Light Indoor Positioning System Using Mobile CMOS Image Sensor
by Md Habibur Rahman, Mohammad Abrar Shakil Sejan and Wan-Young Chung
Appl. Sci. 2021, 11(16), 7308; https://doi.org/10.3390/app11167308 - 9 Aug 2021
Cited by 5 | Viewed by 2825
Abstract
Visible light positioning (VLP) is a cost-effective solution to the increasing demand for real-time indoor positioning. However, owing to high computational costs and complicated image processing procedures, most of the existing VLP systems fail to deliver real-time positioning ability and better accuracy for [...] Read more.
Visible light positioning (VLP) is a cost-effective solution to the increasing demand for real-time indoor positioning. However, owing to high computational costs and complicated image processing procedures, most of the existing VLP systems fail to deliver real-time positioning ability and better accuracy for image sensor-based large-area indoor environments. In this study, an effective method is proposed to receive coordinate information from multiple light-emitting diode (LED) lights simultaneously. It provides better accuracy in large experimental areas with many LEDs by using a smartphone-embedded image sensor as a terminal device and the existing LED lighting infrastructure. A flicker-free frequency shift on–off keying line coding modulation scheme was designed for the positioning system to ensure a constant modulated frequency. We tested the performance of the decoding accuracy with respect to vertical and horizontal distance, which utilizes a rolling shutter mechanism of a complementary metal-oxide-semiconductor image sensor. The experimental results of the proposed positioning system can provide centimeter-level accuracy with low computational time, rendering it a promising solution for the future direction of large-area indoor positioning systems. Full article
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information, Volume III)
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20 pages, 551 KiB  
Article
BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition
by Abu Sayeed, Jungpil Shin, Md. Al Mehedi Hasan, Azmain Yakin Srizon and Md. Mehedi Hasan
Appl. Sci. 2021, 11(15), 6845; https://doi.org/10.3390/app11156845 - 25 Jul 2021
Cited by 20 | Viewed by 4115
Abstract
As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the [...] Read more.
As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters. Full article
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information, Volume III)
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Review

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15 pages, 3470 KiB  
Review
A Survey of Multi-Focus Image Fusion Methods
by Youyong Zhou, Lingjie Yu, Chao Zhi, Chuwen Huang, Shuai Wang, Mengqiu Zhu, Zhenxia Ke, Zhongyuan Gao, Yuming Zhang and Sida Fu
Appl. Sci. 2022, 12(12), 6281; https://doi.org/10.3390/app12126281 - 20 Jun 2022
Cited by 19 | Viewed by 3995
Abstract
As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the [...] Read more.
As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the methods based on boundary segmentation was put forward as a group of image fusion method. Thus, a novel classification method of image fusion algorithms is proposed: transform domain methods, boundary segmentation methods, deep learning methods, and combination fusion methods. In addition, the subjective and objective evaluation standards are listed, and eight common objective evaluation indicators are described in detail. On the basis of lots of literature, this paper compares and summarizes various representative methods. At the end of this paper, some main limitations in current research are discussed, and the future development of multi-focus image fusion is prospected. Full article
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information, Volume III)
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