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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 17 July 2024 | Viewed by 12674

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Prof. Dr. Inseop Na

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Division of Culture Contents, Graduate School of Data Science, AI Convergence and Open Sharing System, Chonnam National University, Gwangju 61186, Republic of Korea
Interests: object/image detection; segmentation; recognition; tracking; image understanding; action/behavior/gesture recognition; emotion recognition
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Prof. Dr. Soo-Hyung Kim

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Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Gwangju 61186, Korea
Interests: deep-learning-based emotion recognition; medical image analysis; pattern recognition
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Prof. Dr. Hieyong Jeong

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Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Korea
Interests: bio-mechanics; robotics; data/image analysis; human pose estimation; IoT system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image segmentation is a core task in image processing with software and hardware applications such as image understanding, medical image analysis, image classification, emotion recognition, object recognition and tracking, object retrieval, video surveillance, augmented reality and meta-verse, and autonomous vehicles. Image segmentation enables the extraction of tumor boundaries and the measurement of tissue volume, the detection of pedestrians for autonomous vehicle operation, the detection of traffic signals and navigable surfaces, and it is even possible to recognize the meaning of emotions and actions from facial expressions and actions.

Image segmentation can be divided into semantic segmentation, instance segmentation, and panoptic segmentation, which draws both together. Semantic segmentation is the operation of labeling all pixels by classifying them into meaningful units such as people, cups, and airplanes. It is the task of segmenting the target image according to its meaning including the background. Instance segmentation is a partitioning operation for individual objects, predicting class labels for all objects in the image, and additionally assigning random IDs. Its main difference from semantic segmentation is that each overlapping object is detected, a class label is not assigned to an object without a fixed shape such as the sky or a road, and a random ID is assigned to each object, for example. When multiple people are photographed in one image, each person is recognized as a distinct object.

Topics of interests include, but are not limited to:

Image segmentations: semantic segmentation, instance segmentation, panoptic segmentation;
Image segmentation methods: legacy methods (histogram-based bundling, region growing, k-means clustering, watershed methods, active contours, graph cuts, cMRF, sparsity-based methods) and deep learning methods (encoder–decoder-based model, multiscale and pyramid network, R-CNN, dilated convolutional model, recurrent neural network, generative adversarial network, attention-based model, graph-based model);
Image segmentation appllications: medical image segmentation, autonomous vechicles, emotion recognition, image understanding and captioning, augented reality and meta-verse, gesture and behavior recognition, etc.;
Segmentation image datasets and performance;
2D, 3D segmentation and devices;
Survey for image segmentation.

Prof. Dr. Inseop Na
Prof. Dr. Soo-Hyung Kim
Prof. Dr. Hieyong Jeong
Guest Editors

Manuscript Submission Information

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Keywords

computer vision
image processing
image segmentation
artificial intelligence
medical image analysis
deep learning
semantic segmentation
instance segmentation
panoptic segmentation
video survilliance
augmented reality
meta-verse
autonomous vehicle

Published Papers (8 papers)

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14 pages, 16557 KiB

Open AccessArticle

Analysis of Training Data Augmentation for Diabetic Foot Ulcer Semantic Segmentation

by Arturas Kairys and Vidas Raudonis

Electronics 2023, 12(22), 4624; https://doi.org/10.3390/electronics12224624 - 12 Nov 2023

Cited by 1 | Viewed by 683

Abstract

Deep learning model training and achieved performance relies on available data. Diabetic foot ulcers and other image processing applications in the medical domain add another layer of complexity to training data collection. Data collection is troublesome and data annotation requires medical expertise. This problem is usually solved by employing training data augmentation. Although in previous research augmentation was facilitated in various ways, it is rarely evaluated or reported how much it contributes to achieved performance. The current research seeks to answer this question by applying individual photometric and geometric augmentation techniques and comparing the model performance achieved for semantic segmentation of diabetic foot ulcers. It was found that geometric augmentation techniques help achieve a better model performance when compared with photometric techniques. The model trained using an augmented dataset and applying a shear technique was found to improve segmentation results the most; the benchmark dice score was increased by 6%. An additional improvement over the benchmark was observed (a total of 6.9%) when the model was trained using data combining image sets generated by the three best-performing augmentation techniques. The highest test dice score achieved was 91%. Full article

(This article belongs to the Special Issue Image Segmentation)

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20 pages, 15469 KiB

Open AccessArticle

High-Resolution Remote Sensing Image Segmentation Algorithm Based on Improved Feature Extraction and Hybrid Attention Mechanism

by Min Huang, Wenhui Dai, Weihao Yan and Jingyang Wang

Electronics 2023, 12(17), 3660; https://doi.org/10.3390/electronics12173660 - 30 Aug 2023

Cited by 2 | Viewed by 1154

Abstract

Segmentation of high-resolution remote sensing images is one of the hottest topics in deep learning. Compared to ordinary images, high-resolution remote sensing images possess characteristics such as higher intra-class diversity and lower inter-class separability. Additionally, the objects in these images are complex and have smaller sizes. Aiming at the classical segmentation network in remote sensing images, there are some problems, such as inaccurate edge object segmentation, inconsistent segmentation of different types of objects, low detection accuracy, and a high false detection rate. This paper proposes a new hybrid attention model (S-CA), a new coordinate efficient channel attention module (C-ECA), and a new small-target feature extraction network (S-FE). The S-CA model enhances important spatial and channel features in shallow layers, allowing for more detailed feature extraction. The C-ECA model utilizes convolutional layers to capture complex dependencies between variations, thereby better capturing feature information at each position and reducing redundancy in feature channels. The S-FE network can capture the local feature information of different targets more effectively. It enhances the recognition and classification capabilities of various targets and improves the detection rate of small targets. The algorithm is used for segmentation in high-resolution remote sensing images. Experiments were conducted on the public dataset GID-15 based on Gaofen-2 satellite remote sensing images. The experimental results demonstrate that the improved DeepLabV3+ segmentation algorithm for remote sensing images achieved a mean intersection over union (mIoU), mean pixel accuracy (mPA), and mean precision (mP) of 91.6%, 96.1%, and 95.5%, respectively. The improved algorithm is more effective than current mainstream segmentation networks. Full article

(This article belongs to the Special Issue Image Segmentation)

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22 pages, 38901 KiB

Open AccessArticle

Real-Time Pose Estimation Based on ResNet-50 for Rapid Safety Prevention and Accident Detection for Field Workers

by Jieun Lee, Tae-yong Kim, Seunghyo Beak, Yeeun Moon and Jongpil Jeong

Electronics 2023, 12(16), 3513; https://doi.org/10.3390/electronics12163513 - 19 Aug 2023

Cited by 1 | Viewed by 1867

Abstract

The present study proposes a Real-Time Pose Estimation technique using OpenPose based on ResNet-50 that enables rapid safety prevention and accident detection among field workers. Field workers perform tasks in high-risk environments, and accurate Pose Estimation is a crucial aspect of ensuring worker safety. However, it is difficult for Real-Time Pose Estimation to be conducted in such a way as to simultaneously meet Real-Time processing requirements and accuracy in complex environments. To address these issues, the current study uses the OpenPose algorithm based on ResNet-50, which is a neural network architecture that performs well in both image classification and feature extraction tasks, thus providing high accuracy and efficiency. OpenPose is an algorithm specialized for multi-human Pose Estimation that can be used to estimate the body structure and joint positions of a large number of individuals in real time. Here, we train ResNet-50-based OpenPose for Real-Time Pose Estimation and evaluate it on various datasets, including actions performed by real field workers. The experimental results show that the proposed algorithm achieves high accuracy in the Real-Time Pose Estimation of field workers. It also provides stable results while maintaining a fast image processing speed, thus confirming its applicability in real field environments. Full article

(This article belongs to the Special Issue Image Segmentation)

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16 pages, 42680 KiB

Open AccessArticle

Patch-Based Difference-in-Level Detection with Segmented Ground Mask

by Yusuke Nonaka, Hideaki Uchiyama, Hideo Saito, Shoji Yachida and Kota Iwamoto

Electronics 2023, 12(4), 806; https://doi.org/10.3390/electronics12040806 - 06 Feb 2023

Viewed by 1110

Abstract

Difference-in-level detection in outdoor scenes has various possible applications, including walking assistance for blind people, robot walking assistance, and mapping the hazards of factory premises. It is difficult to detect all outdoor differences in level, such as RGB or RGB-D images, not only including road curbs, which are often targeted for detection in automated driving, but also differences in level on factory premises and sidewalks, because the pattern of outdoor differences in level is abundant and complex. This paper proposes a novel method for detecting differences in level from RGB-D images with segmented ground masks. First, image patches of differences in level were extracted from outdoor images to create the dataset. The change in the normal vector of the contour part on the detected plane is used to generate image patches of the difference in level, but this method strongly depends on the accuracy of planar detection, and it detects only some differences in level. Then, we created the dataset, consisting of image patches and including the extracted differences in level. The dataset is used for training a deep learning model for detecting differences in level in outdoor images without limitations. In addition, because the purpose of this paper is to detect differences in level in outdoor walking areas, regions in the image other than the target areas were excluded by the segmented ground mask. For the performance evaluation, we implemented our algorithm using a modern smartphone with a high-performance depth camera. To evaluate the effectiveness of the proposed method, the results from various inputs, such as RGB, depth, grayscale, normal, and combinations of them, were qualitatively and quantitatively evaluated, and Blender was used to generate synthetic test images for a quantitative evaluation of the difference in level. We confirm that the suggested method successfully detects various types of differences in level in outdoor images, even in complex scenes. Full article

(This article belongs to the Special Issue Image Segmentation)

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12 pages, 13033 KiB

Open AccessArticle

Mulvernet: Nucleus Segmentation and Classification of Pathology Images Using the HoVer-Net and Multiple Filter Units

by Vi Thi-Tuong Vo and Soo-Hyung Kim

Electronics 2023, 12(2), 355; https://doi.org/10.3390/electronics12020355 - 10 Jan 2023

Cited by 2 | Viewed by 2795

Abstract

Nucleus segmentation and classification are crucial in pathology image analysis. Automated nuclear classification and segmentation methods support analysis and understanding of cell characteristics and functions, and allow the analysis of large-scale nuclear forms in the diagnosis and treatment of diseases. Common problems in these tasks arise from the inconsistent sizes and shapes of the cells in each pathology image. This study aims to develop a new method to address these problems based primarily on the horizontal and vertical distance network (HoVer-Net), multiple filter units, and attention gate mechanisms. The results of the study will significantly impact cell segmentation and classification by showing that a multiple filter unit improves the performance of the original HoVer-Net model. In addition, our experimental results show that the Mulvernet achieves outperforming results in both nuclei segmentation and classification compared to several methods. The ability to segment and classify different types of nuclei automatically has a direct influence on further pathological analysis, offering great potential not only to accelerate the diagnostic process in clinics but also for enhancing our understanding of tissue and cell properties to improve patient care and management. Full article

(This article belongs to the Special Issue Image Segmentation)

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13 pages, 1135 KiB

Open AccessFeature PaperArticle

Intrinsic Emotion Recognition Considering the Emotional Association in Dialogues

by Myung-Jin Lim, Moung-Ho Yi and Ju-Hyun Shin

Electronics 2023, 12(2), 326; https://doi.org/10.3390/electronics12020326 - 08 Jan 2023

Cited by 1 | Viewed by 1403

Abstract

Computer communication via text messaging or Social Networking Services (SNS) has become increasingly popular. At this time, many studies are being conducted to analyze user information or opinions and recognize emotions by using a large amount of data. Currently, the methods for the emotion recognition of dialogues requires an analysis of emotion keywords or vocabulary, and dialogue data are mostly classified as a single emotion. Recently, datasets classified as multiple emotions have emerged, but most of them are composed of English datasets. For accurate emotion recognition, a method for recognizing various emotions in one sentence is required. In addition, multi-emotion recognition research in Korean dialogue datasets is also needed. Since dialogues are exchanges between speakers. One’s feelings may be changed by the words of others, and feelings, once generated, may last for a long period of time. Emotions are expressed not only through vocabulary, but also indirectly through dialogues. In order to improve the performance of emotion recognition, it is necessary to analyze Emotional Association in Dialogues (EAD) to effectively reflect various factors that induce emotions. Therefore, in this paper, we propose a more accurate emotion recognition method to overcome the limitations of single emotion recognition. We implement Intrinsic Emotion Recognition (IER) to understand the meaning of dialogue and recognize complex emotions. In addition, conversations are classified according to their characteristics, and the correlation between IER is analyzed to derive Emotional Association in Dialogues (EAD) and apply them. To verify the usefulness of the proposed technique, IER applied with EAD is tested and evaluated. This evaluation determined that Micro-F1 of the proposed method exhibited the best performance, with 74.8% accuracy. Using IER to assess the EAD proposed in this paper can improve the accuracy and performance of emotion recognition in dialogues. Full article

(This article belongs to the Special Issue Image Segmentation)

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15 pages, 9650 KiB

Open AccessArticle

Consideration for Positive and Negative Effect of Multi-Sensory Environment Interventions on Disabled Patients through Electrocardiography

by Hieyong Jeong and Yuko Ohno

Electronics 2022, 11(22), 3692; https://doi.org/10.3390/electronics11223692 - 11 Nov 2022

Viewed by 1263

Abstract

Many studies have supported the efficacy of multi-sensory environment (MSE) interventions in reducing behavioral and psychological symptoms and improving the quality of life for disabled patients. However, it is difficult to identify the groups that are helped and those who are harmed. This study verified the effect of multi-sensory environment interventions on disabled patients by using thermal images, then addressed the precaution using electrocardiography (ECG). Twenty disabled patients participated in experiments for 12 min: ten with muscular dystrophy (MD) and ten with severe motor and intellectual disabilities (SMID). The continuous measurement of nasal temperature evaluated the emotional arousal after facial detection. The QT-RR relation was used to assess the risk degree. It was found that the continuous measurement of nasal temperature enabled us to evaluate the emotional arousal of disabled patients in MSE with the comparison of ECG. Through the QT-RR relation, it was found that the risk assessment for the patient with SMID was 11 times higher than those with MD because the QT was below 300 ms. Therefore, it was concluded that the specification for the risky group was related to the kind of prescribed medication through continuous measurement. Full article

(This article belongs to the Special Issue Image Segmentation)

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12 pages, 4581 KiB

Open AccessEssay

Research on Algorithm for Improving Infrared Image Defect Segmentation of Power Equipment

by Jingwen Zhang and Wu Zhu

Electronics 2023, 12(7), 1588; https://doi.org/10.3390/electronics12071588 - 28 Mar 2023

Cited by 1 | Viewed by 1045

Abstract

The existing infrared image processing technology mainly relies on the traditional segmentation algorithm, which is not only inefficient, but also has problems such as blurred edges, poor segmentation accuracy, and insufficient extraction of key power equipment features for the infrared image defect segmentation of power equipment. A CS_DeeplabV3+ network for the accurate segmentation of the infrared image defect segmentation of power equipment is designed for the situation of leakage and false detection after segmentation by traditional algorithms. The ASPP module is improved in the encoder part to enable the network to obtain a denser pixel sampling, an improved attention mechanism is introduced to enhance the sensitivity and accuracy of the network for feature extraction, and a semantic segmentation feature enhancement module—the structured feature enhancement module (SFEM)—is introduced in the decoder part to enhance the feature processing to improve the segmentation accuracy. The CS_DeeplabV3+ network is validated using the dataset, and the experimental comparison proves that the improved model has finer contours compared with other models for segmenting infrared images of power equipment defects, and MPA is improved by 5.6% and MIOU is improved by 7.3% compared with the DeeplabV3+ network. Full article

(This article belongs to the Special Issue Image Segmentation)

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