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26 pages, 2821 KiB

Open AccessArticle

A Heart Image Segmentation Method Based on Position Attention Mechanism and Inverted Pyramid

by Jinbin Luo, Qinghui Wang, Ruirui Zou, Ying Wang, Fenglin Liu, Haojie Zheng, Shaoyi Du and Chengzhi Yuan

Sensors 2023, 23(23), 9366; https://doi.org/10.3390/s23239366 - 23 Nov 2023

Cited by 1 | Viewed by 890

Abstract

In the realm of modern medicine, medical imaging stands as an irreplaceable pillar for accurate diagnostics. The significance of precise segmentation in medical images cannot be overstated, especially considering the variability introduced by different practitioners. With the escalating volume of medical imaging data, [...] Read more.

In the realm of modern medicine, medical imaging stands as an irreplaceable pillar for accurate diagnostics. The significance of precise segmentation in medical images cannot be overstated, especially considering the variability introduced by different practitioners. With the escalating volume of medical imaging data, the demand for automated and efficient segmentation methods has become imperative. This study introduces an innovative approach to heart image segmentation, embedding a multi-scale feature and attention mechanism within an inverted pyramid framework. Recognizing the intricacies of extracting contextual information from low-resolution medical images, our method adopts an inverted pyramid architecture. Through training with multi-scale images and integrating prediction outcomes, we enhance the network’s contextual understanding. Acknowledging the consistent patterns in the relative positions of organs, we introduce an attention module enriched with positional encoding information. This module empowers the network to capture essential positional cues, thereby elevating segmentation accuracy. Our research resides at the intersection of medical imaging and sensor technology, emphasizing the foundational role of sensors in medical image analysis. The integration of sensor-generated data showcases the symbiotic relationship between sensor technology and advanced machine learning techniques. Evaluation on two heart datasets substantiates the superior performance of our approach. Metrics such as the Dice coefficient, Jaccard coefficient, recall, and F-measure demonstrate the method’s efficacy compared to state-of-the-art techniques. In conclusion, our proposed heart image segmentation method addresses the challenges posed by diverse medical images, offering a promising solution for efficiently processing 2D/3D sensor data in contemporary medical imaging. Full article

(This article belongs to the Special Issue Machine Learning Based 2D/3D Sensors Data Understanding and Analysis)

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15 pages, 12784 KiB

Open AccessArticle

Fast-MFQE: A Fast Approach for Multi-Frame Quality Enhancement on Compressed Video

by Kemi Chen, Jing Chen, Huanqiang Zeng and Xueyuan Shen

Sensors 2023, 23(16), 7227; https://doi.org/10.3390/s23167227 - 17 Aug 2023

Viewed by 883

Abstract

For compressed images and videos, quality enhancement is essential. Though there have been remarkable achievements related to deep learning, deep learning models are too large to apply to real-time tasks. Therefore, a fast multi-frame quality enhancement method for compressed video, named Fast-MFQE, is [...] Read more.

For compressed images and videos, quality enhancement is essential. Though there have been remarkable achievements related to deep learning, deep learning models are too large to apply to real-time tasks. Therefore, a fast multi-frame quality enhancement method for compressed video, named Fast-MFQE, is proposed to meet the requirement of video-quality enhancement for real-time applications. There are three main modules in this method. One is the image pre-processing building module (IPPB), which is used to reduce redundant information of input images. The second one is the spatio-temporal fusion attention (STFA) module. It is introduced to effectively merge temporal and spatial information of input video frames. The third one is the feature reconstruction network (FRN), which is developed to effectively reconstruct and enhance the spatio-temporal information. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of lightweight parameters, inference speed, and quality enhancement performance. Even at a resolution of 1080p, the Fast-MFQE achieves a remarkable inference speed of over 25 frames per second, while providing a PSNR increase of 19.6% on average when QP = 37. Full article

(This article belongs to the Special Issue Machine Learning Based 2D/3D Sensors Data Understanding and Analysis)

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21 pages, 4424 KiB

Open AccessArticle

A Salient Object Detection Method Based on Boundary Enhancement

by Falin Wen, Qinghui Wang, Ruirui Zou, Ying Wang, Fenglin Liu, Yang Chen, Linghao Yu, Shaoyi Du and Chengzhi Yuan

Sensors 2023, 23(16), 7077; https://doi.org/10.3390/s23167077 - 10 Aug 2023

Viewed by 928

Abstract

Visual saliency refers to the human’s ability to quickly focus on important parts of their visual field, which is a crucial aspect of image processing, particularly in fields like medical imaging and robotics. Understanding and simulating this mechanism is crucial for solving complex [...] Read more.

Visual saliency refers to the human’s ability to quickly focus on important parts of their visual field, which is a crucial aspect of image processing, particularly in fields like medical imaging and robotics. Understanding and simulating this mechanism is crucial for solving complex visual problems. In this paper, we propose a salient object detection method based on boundary enhancement, which is applicable to both 2D and 3D sensors data. To address the problem of large-scale variation of salient objects, our method introduces a multi-level feature aggregation module that enhances the expressive ability of fixed-resolution features by utilizing adjacent features to complement each other. Additionally, we propose a multi-scale information extraction module to capture local contextual information at different scales for back-propagated level-by-level features, which allows for better measurement of the composition of the feature map after back-fusion. To tackle the low confidence issue of boundary pixels, we also introduce a boundary extraction module to extract the boundary information of salient regions. This information is then fused with salient target information to further refine the saliency prediction results. During the training process, our method uses a mixed loss function to constrain the model training from two levels: pixels and images. The experimental results demonstrate that our salient target detection method based on boundary enhancement shows good detection effects on targets of different scales, multi-targets, linear targets, and targets in complex scenes. We compare our method with the best method in four conventional datasets and achieve an average improvement of 6.2% on the mean absolute error (MAE) indicators. Overall, our approach shows promise for improving the accuracy and efficiency of salient object detection in a variety of settings, including those involving 2D/3D semantic analysis and reconstruction/inpainting of image/video/point cloud data. Full article

(This article belongs to the Special Issue Machine Learning Based 2D/3D Sensors Data Understanding and Analysis)

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15 pages, 59748 KiB

Open AccessArticle

Affine Iterative Closest Point Algorithm Based on Color Information and Correntropy for Precise Point Set Registration

by Lexian Liang and Hailong Pei

Sensors 2023, 23(14), 6475; https://doi.org/10.3390/s23146475 - 17 Jul 2023

Cited by 1 | Viewed by 991

Abstract

In this paper, we propose a novel affine iterative closest point algorithm based on color information and correntropy, which can effectively deal with the registration problems with a large number of noise and outliers and small deformations in RGB-D datasets. Firstly, to alleviate [...] Read more.

In this paper, we propose a novel affine iterative closest point algorithm based on color information and correntropy, which can effectively deal with the registration problems with a large number of noise and outliers and small deformations in RGB-D datasets. Firstly, to alleviate the problem of low registration accuracy for data with weak geometric structures, we consider introducing color features into traditional affine algorithms to establish more accurate and reliable correspondences. Secondly, we introduce the correntropy measurement to overcome the influence of a large amount of noise and outliers in the RGB-D datasets, thereby further improving the registration accuracy. Experimental results demonstrate that the proposed registration algorithm has higher registration accuracy, with error reduction of almost 10 times, and achieves more stable robustness than other advanced algorithms. Full article

(This article belongs to the Special Issue Machine Learning Based 2D/3D Sensors Data Understanding and Analysis)

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20 pages, 5151 KiB

Open AccessArticle

An Interactive Image Segmentation Method Based on Multi-Level Semantic Fusion

by Ruirui Zou, Qinghui Wang, Falin Wen, Yang Chen, Jiale Liu, Shaoyi Du and Chengzhi Yuan

Sensors 2023, 23(14), 6394; https://doi.org/10.3390/s23146394 - 14 Jul 2023

Cited by 1 | Viewed by 1352

Abstract

Understanding and analyzing 2D/3D sensor data is crucial for a wide range of machine learning-based applications, including object detection, scene segmentation, and salient object detection. In this context, interactive object segmentation is a vital task in image editing and medical diagnosis, involving the [...] Read more.

Understanding and analyzing 2D/3D sensor data is crucial for a wide range of machine learning-based applications, including object detection, scene segmentation, and salient object detection. In this context, interactive object segmentation is a vital task in image editing and medical diagnosis, involving the accurate separation of the target object from its background based on user annotation information. However, existing interactive object segmentation methods struggle to effectively leverage such information to guide object-segmentation models. To address these challenges, this paper proposes an interactive image-segmentation technique for static images based on multi-level semantic fusion. Our method utilizes user-guidance information both inside and outside the target object to segment it from the static image, making it applicable to both 2D and 3D sensor data. The proposed method introduces a cross-stage feature aggregation module, enabling the effective propagation of multi-scale features from previous stages to the current stage. This mechanism prevents the loss of semantic information caused by multiple upsampling and downsampling of the network, allowing the current stage to make better use of semantic information from the previous stage. Additionally, we incorporate a feature channel attention mechanism to address the issue of rough network segmentation edges. This mechanism captures richer feature details from the feature channel level, leading to finer segmentation edges. In the experimental evaluation conducted on the PASCAL Visual Object Classes (VOC) 2012 dataset, our proposed interactive image segmentation method based on multi-level semantic fusion demonstrates an intersection over union (IOU) accuracy approximately 2.1% higher than the currently popular interactive image segmentation method in static images. The comparative analysis highlights the improved performance and effectiveness of our method. Furthermore, our method exhibits potential applications in various fields, including medical imaging and robotics. Its compatibility with other machine learning methods for visual semantic analysis allows for integration into existing workflows. These aspects emphasize the significance of our contributions in advancing interactive image-segmentation techniques and their practical utility in real-world applications. Full article

(This article belongs to the Special Issue Machine Learning Based 2D/3D Sensors Data Understanding and Analysis)

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12 pages, 2437 KiB

Open AccessArticle

Spatial–Temporal Self-Attention Enhanced Graph Convolutional Networks for Fitness Yoga Action Recognition

by Guixiang Wei, Huijian Zhou, Liping Zhang and Jianji Wang

Sensors 2023, 23(10), 4741; https://doi.org/10.3390/s23104741 - 14 May 2023

Cited by 1 | Viewed by 1476

Abstract

Fitness yoga is now a popular form of national fitness and sportive physical therapy. At present, Microsoft Kinect, a depth sensor, and other applications are widely used to monitor and guide yoga performance, but they are inconvenient to use and still a little [...] Read more.

Fitness yoga is now a popular form of national fitness and sportive physical therapy. At present, Microsoft Kinect, a depth sensor, and other applications are widely used to monitor and guide yoga performance, but they are inconvenient to use and still a little expensive. To solve these problems, we propose spatial–temporal self-attention enhanced graph convolutional networks (STSAE-GCNs) that can analyze RGB yoga video data captured by cameras or smartphones. In the STSAE-GCN, we build a spatial–temporal self-attention module (STSAM), which can effectively enhance the spatial–temporal expression ability of the model and improve the performance of the proposed model. The STSAM has the characteristics of plug-and-play so that it can be applied in other skeleton-based action recognition methods and improve their performance. To prove the effectiveness of the proposed model in recognizing fitness yoga actions, we collected 960 fitness yoga action video clips in 10 action classes and built the dataset Yoga10. The recognition accuracy of the model on Yoga10 achieves 93.83%, outperforming the state-of-the-art methods, which proves that this model can better recognize fitness yoga actions and help students learn fitness yoga independently. Full article

(This article belongs to the Special Issue Machine Learning Based 2D/3D Sensors Data Understanding and Analysis)

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13 pages, 757 KiB

Open AccessArticle

An Explainable Student Fatigue Monitoring Module with Joint Facial Representation

by Xiaomian Li, Jiaqin Lin, Zhiqiang Tian and Yuping Lin

Sensors 2023, 23(7), 3602; https://doi.org/10.3390/s23073602 - 30 Mar 2023

Viewed by 1250

Abstract

Online fatigue estimation is, inevitably, in demand as fatigue can impair the health of college students and lower the quality of higher education. Therefore, it is essential to monitor college students’ fatigue to diminish its adverse effects on the health and academic performance [...] Read more.

Online fatigue estimation is, inevitably, in demand as fatigue can impair the health of college students and lower the quality of higher education. Therefore, it is essential to monitor college students’ fatigue to diminish its adverse effects on the health and academic performance of college students. However, former studies on student fatigue monitoring are mainly survey-based with offline analysis, instead of using constant fatigue monitoring. Hence, we proposed an explainable student fatigue estimation model based on joint facial representation. This model includes two modules: a spacial–temporal symptom classification module and a data-experience joint status inferring module. The first module tracks a student’s face and generates spatial–temporal features using a deep convolutional neural network (CNN) for the relevant drivers of abnormal symptom classification; the second module infers a student’s status with symptom classification results with maximum a posteriori (MAP) under the data-experience joint constraints. The model was trained on the benchmark NTHU Driver Drowsiness Detection (NTHU-DDD) dataset and tested on an Online Student Fatigue Monitoring (OSFM) dataset. Our method outperformed the other methods with an accuracy rate of 94.47% under the same training–testing setting. The results were significant for real-time monitoring of students’ fatigue states during online classes and could also provide practical strategies for in-person education. Full article

(This article belongs to the Special Issue Machine Learning Based 2D/3D Sensors Data Understanding and Analysis)

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17 pages, 8360 KiB

Open AccessArticle

Joint Texture Search and Histogram Redistribution for Hyperspectral Image Quality Improvement

by Bingliang Hu, Junyu Chen, Yihao Wang, Haiwei Li and Geng Zhang

Sensors 2023, 23(5), 2731; https://doi.org/10.3390/s23052731 - 02 Mar 2023

Viewed by 1016

Abstract

Due to optical noise, electrical noise, and compression error, data hyperspectral remote sensing equipment is inevitably contaminated by various noises, which seriously affect the applications of hyperspectral data. Therefore, it is of great significance to enhance hyperspectral imaging data quality. To guarantee the [...] Read more.

Due to optical noise, electrical noise, and compression error, data hyperspectral remote sensing equipment is inevitably contaminated by various noises, which seriously affect the applications of hyperspectral data. Therefore, it is of great significance to enhance hyperspectral imaging data quality. To guarantee the spectral accuracy during data processing, band-wise algorithms are not suitable for hyperspectral data. This paper proposes a quality enhancement algorithm based on texture search and histogram redistribution combined with denoising and contrast enhancement. Firstly, a texture-based search algorithm is proposed to improve the accuracy of denoising by improving the sparsity of 4D block matching clustering. Then, histogram redistribution and Poisson fusion are used to enhance spatial contrast while preserving spectral information. Synthesized noising data from public hyperspectral datasets are used to quantitatively evaluate the proposed algorithm, and multiple criteria are used to analyze the experimental results. At the same time, classification tasks were used to verify the quality of the enhanced data. The results show that the proposed algorithm is satisfactory for hyperspectral data quality improvement. Full article

(This article belongs to the Special Issue Machine Learning Based 2D/3D Sensors Data Understanding and Analysis)

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19 pages, 5092 KiB

Open AccessArticle

A Classification Method of Point Clouds of Transmission Line Corridor Based on Improved Random Forest and Multi-Scale Features

by Qingyun Tang, Letan Zhang, Guiwen Lan, Xiaoyong Shi, Xinghui Duanmu and Kan Chen

Sensors 2023, 23(3), 1320; https://doi.org/10.3390/s23031320 - 24 Jan 2023

Cited by 2 | Viewed by 2120

Abstract

Classification of airborne laser scanning (ALS) point clouds of power lines is of great importance to their reconstruction. However, it is still a difficult task to efficiently and accurately classify the ground, vegetation, power lines and power pylons from ALS point clouds. Therefore, [...] Read more.

Classification of airborne laser scanning (ALS) point clouds of power lines is of great importance to their reconstruction. However, it is still a difficult task to efficiently and accurately classify the ground, vegetation, power lines and power pylons from ALS point clouds. Therefore, in this paper, a method is proposed to improve the accuracy and efficiency of the classification of point clouds of transmission lines, which is based on improved Random Forest and multi-scale features. The point clouds are filtered by the optimized progressive TIN densification filtering algorithm, then the elevations of the filtered point cloud are normalized. The features of the point cloud at different scales are calculated according to the basic features of the point cloud and the characteristics of transmission lines. The Relief F and Sequential Backward Selection algorithm are used to select the best subset of features to estimate the parameters of the learning model, then an Improved Random Forest classification model is built to classify the point clouds. The proposed method is verified by using three different samples from the study area and the results show that, compared with the methods based on Support Vector Machines, AdaBoost or Random Forest, our method can reduce feature redundancy and has higher classification accuracy and efficiency. Full article

(This article belongs to the Special Issue Machine Learning Based 2D/3D Sensors Data Understanding and Analysis)

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