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J. Imaging, Volume 10, Issue 12 (December 2024) – 6 articles

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17 pages, 925 KiB  
Article
Deep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall
by Cheng Jian, Tiancheng Xie, Xiaojian Hu and Jian Lu
J. Imaging 2024, 10(12), 301; https://doi.org/10.3390/jimaging10120301 - 22 Nov 2024
Abstract
In recent years, advancements in computer vision have yielded new prospects for intelligent transportation applications, specifically in the realm of automated traffic flow data collection. Within this emerging trend, the ability to swiftly and accurately detect vehicles and extract traffic flow parameters from [...] Read more.
In recent years, advancements in computer vision have yielded new prospects for intelligent transportation applications, specifically in the realm of automated traffic flow data collection. Within this emerging trend, the ability to swiftly and accurately detect vehicles and extract traffic flow parameters from videos captured during snowfall conditions has become imperative for numerous future applications. This paper proposes a new analytical framework designed to extract traffic flow parameters from traffic flow videos recorded under snowfall conditions. The framework encompasses four distinct stages aimed at addressing the challenges posed by image degradation and the diminished accuracy of traffic flow parameter recognition caused by snowfall. The initial two stages propose a deep learning network for removing snow particles and snow streaks, resulting in an 8.6% enhancement in vehicle recognition accuracy after snow removal, specifically under moderate snow conditions. Additionally, the operation speed is significantly enhanced. Subsequently, the latter two stages encompass yolov5-based vehicle recognition and the employment of the virtual coil method for traffic flow parameter estimation. Following rigorous testing, the accuracy of traffic flow parameter estimation reaches 97.2% under moderate snow conditions. Full article
(This article belongs to the Section Computer Vision and Pattern Recognition)
22 pages, 18461 KiB  
Article
Learning More May Not Be Better: Knowledge Transferability in Vision-and-Language Tasks
by Tianwei Chen, Noa Garcia, Mayu Otani, Chenhui Chu, Yuta Nakashima and Hajime Nagahara
J. Imaging 2024, 10(12), 300; https://doi.org/10.3390/jimaging10120300 - 22 Nov 2024
Abstract
Is learning more knowledge always better for vision-and-language models? In this paper, we study knowledge transferability in multi-modal tasks. The current tendency in machine learning is to assume that by joining multiple datasets from different tasks, their overall performance improves. However, we show [...] Read more.
Is learning more knowledge always better for vision-and-language models? In this paper, we study knowledge transferability in multi-modal tasks. The current tendency in machine learning is to assume that by joining multiple datasets from different tasks, their overall performance improves. However, we show that not all knowledge transfers well or has a positive impact on related tasks, even when they share a common goal. We conducted an exhaustive analysis based on hundreds of cross-experiments on twelve vision-and-language tasks categorized into four groups. While tasks in the same group are prone to improve each other, results show that this is not always the case. In addition, other factors, such as dataset size or the pre-training stage, may have a great impact on how well the knowledge is transferred. Full article
(This article belongs to the Special Issue Deep Learning in Image Analysis: Progress and Challenges)
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26 pages, 100117 KiB  
Article
Enhanced Atrous Spatial Pyramid Pooling Feature Fusion for Small Ship Instance Segmentation
by Rabi Sharma, Muhammad Saqib, C. T. Lin and Michael Blumenstein
J. Imaging 2024, 10(12), 299; https://doi.org/10.3390/jimaging10120299 - 21 Nov 2024
Viewed by 323
Abstract
In the maritime environment, the instance segmentation of small ships is crucial. Small ships are characterized by their limited appearance, smaller size, and ships in distant locations in marine scenes. However, existing instance segmentation algorithms do not detect and segment them, resulting in [...] Read more.
In the maritime environment, the instance segmentation of small ships is crucial. Small ships are characterized by their limited appearance, smaller size, and ships in distant locations in marine scenes. However, existing instance segmentation algorithms do not detect and segment them, resulting in inaccurate ship segmentation. To address this, we propose a novel solution called enhanced Atrous Spatial Pyramid Pooling (ASPP) feature fusion for small ship instance segmentation. The enhanced ASPP feature fusion module focuses on small objects by refining them and fusing important features. The framework consistently outperforms state-of-the-art models, including Mask R-CNN, Cascade Mask R-CNN, YOLACT, SOLO, and SOLOv2, in three diverse datasets, achieving an average precision (mask AP) score of 75.8% for ShipSG, 69.5% for ShipInsSeg, and 54.5% for the MariBoats datasets. Full article
(This article belongs to the Special Issue Image Processing and Computer Vision: Algorithms and Applications)
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28 pages, 374 KiB  
Review
Image Processing Hardware Acceleration—A Review of Operations Involved and Current Hardware Approaches
by Costin-Emanuel Vasile, Andrei-Alexandru Ulmămei and Călin Bîră
J. Imaging 2024, 10(12), 298; https://doi.org/10.3390/jimaging10120298 - 21 Nov 2024
Viewed by 189
Abstract
This review provides an in-depth analysis of current hardware acceleration approaches for image processing and neural network inference, focusing on key operations involved in these applications and the hardware platforms used to deploy them. We examine various solutions, including traditional CPU–GPU systems, custom [...] Read more.
This review provides an in-depth analysis of current hardware acceleration approaches for image processing and neural network inference, focusing on key operations involved in these applications and the hardware platforms used to deploy them. We examine various solutions, including traditional CPU–GPU systems, custom ASIC designs, and FPGA implementations, while also considering emerging low-power, resource-constrained devices. Full article
(This article belongs to the Section Image and Video Processing)
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21 pages, 3959 KiB  
Article
Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients
by Maryem Rhanoui, Mounia Mikram, Kamelia Amazian, Abderrahim Ait-Abderrahim, Siham Yousfi and Imane Toughrai
J. Imaging 2024, 10(12), 297; https://doi.org/10.3390/jimaging10120297 - 21 Nov 2024
Viewed by 232
Abstract
Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients’ quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine [...] Read more.
Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients’ quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights. In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. Additionally, we identify key predictive factors for each quality of life indicator, thereby enabling clinicians to make more informed treatment decisions and ultimately enhance patient outcomes. Our approach integrates data from multiple sources, enhancing the performance of our predictive models. The analysis demonstrates a notable improvement in accuracy for some indicators, with results for the Wexner score increasing from 24% to 48% and for the Anorectal Ultrasound score from 88% to 96% after integrating data from different modalities. These results highlight the potential of multimodal learning to provide valuable insights and improve patient care in real-world applications. Full article
(This article belongs to the Section Medical Imaging)
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16 pages, 5582 KiB  
Article
Evaluating Brain Tumor Detection with Deep Learning Convolutional Neural Networks Across Multiple MRI Modalities
by Ioannis Stathopoulos, Luigi Serio, Efstratios Karavasilis, Maria Anthi Kouri, Georgios Velonakis, Nikolaos Kelekis and Efstathios Efstathopoulos
J. Imaging 2024, 10(12), 296; https://doi.org/10.3390/jimaging10120296 - 21 Nov 2024
Viewed by 248
Abstract
Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of [...] Read more.
Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection. In this study, we evaluate the diagnostic performance of six fundamental MRI sequences in detecting tumor-involved brain slices using four distinct CNN architectures enhanced with transfer learning techniques. Our dataset comprises 1646 MRI slices from the examinations of 62 patients, encompassing both tumor-bearing and normal findings. With our approach, we achieved a classification accuracy of 98.6%, underscoring the high potential of CNN-based models in this context. Additionally, we assessed the performance of each MRI sequence across the different CNN models, identifying optimal combinations of MRI modalities and neural networks to meet radiologists’ screening requirements effectively. This study offers critical insights into the integration of deep learning with MRI for brain tumor detection, with implications for improving diagnostic workflows in clinical settings. Full article
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