image segmentation is a crucial step in imageprocessing having various applications in biomedical image analysis. Segmentation of the magnetic resonance images of the brain is one such key area in biomedical image an...
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ISBN:
(纸本)9783031585340;9783031585357
image segmentation is a crucial step in imageprocessing having various applications in biomedical image analysis. Segmentation of the magnetic resonance images of the brain is one such key area in biomedical image analysis that segments various tissues in the brain and detects tumor regions. In this paper, an unsupervised rough spatial ensemble kernelized fuzzy clustering segmentation algorithm is presented for automated segmentation of magnetic resonance images of the brain. The proposed algorithm is an integration of Rough Fuzzy C Means clustering and the kernel method with a novel ensemble kernel being a combination of spherical kernel, Gaussian, and Cauchy kernels, which improves the performance of the segmentation algorithm. The proposed algorithm performs better than the existing clustering algorithms across a wide range of magnetic resonance images of the brain along with visual indications obtained from the results.
Multispectral (MS) imaging systems have a wide range of applications for computer vision and computational photography tasks, but do not yet enjoy widespread adoption due to their prohibitive costs. Recently, advances...
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ISBN:
(纸本)9798350318920;9798350318937
Multispectral (MS) imaging systems have a wide range of applications for computer vision and computational photography tasks, but do not yet enjoy widespread adoption due to their prohibitive costs. Recently, advances in the design and fabrication of photonic metamaterials have enabled the development of MS sensors suitable for integration into consumer grade mobile devices. Augmenting existing RGB cameras and their processingalgorithms with richer spectral information has the potential to be utilized in many steps of the imageprocessing pipeline, but diverse real world datasets suitable for conducting such research are not freely available. We introduce Beyond RGB(1), a real-world dataset comprising thousands of multispectral and RGB images in diverse real world and lab conditions that is suitable for the development and evaluation of algorithms utilizing multispectral and RGB data. All the scenes in our dataset include a colorimetric reference and a measurement of the spectrum of the scene illuminant. Additionally, we demonstrate the practical use of our dataset through the introduction of a novel illuminant spectral estimation (ISE) algorithm. Our algorithm surpasses the current state-of-the-art (SoTA) by up to 58.6% on established benchmarks and sets a baseline performance on our own dataset.
This paper aims to investigate a system that uses various machine-learning algorithms to predict symptoms and deep-learning techniques for imageprocessing that leads to early disease prediction, an essential aspect o...
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In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional imageprocessing...
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ISBN:
(纸本)9781510673854;9781510673847
In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional imageprocessing tasks. However, many of the existing solutions in this domain are burdened by computational complexity, rendering them unsuitable for real-time deployment on standard devices as they often necessitate complex systems and substantial energy consumption. This work addresses the growing paradigm of edge computing for real-time applications by introducing a novel, on-edge device solution. This innovative approach aims to strike a balance between efficiency and accuracy, adhering to the practical constraints of real-world deployment. By presenting demonstrations of the proposed solution's performance on readily available devices, we provide tangible evidence of its applicability and viability in real-world scenarios. This advance contributes to the ongoing dialogue about the need for accessible and efficient image compression algorithms that can be deployed real-time applications on edge devices, bridging the gap between the demanding computational requirements of deep learning and the practical limitations of everyday hardware. As data continues to surge, solutions like this become ever more critical in ensuring effective image compression, aligning with on-edge computing within AI. This research paves the way for improved imageprocessing in real-time applications while conserving computational resources and energy consumption.
Biomedical image analysis has benefited tremendously from the advent of artificial intelligence. Machine learning and deep learning-based algorithms are increasingly utilized in real time to assist clinicians with mak...
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ISBN:
(纸本)9798350325744
Biomedical image analysis has benefited tremendously from the advent of artificial intelligence. Machine learning and deep learning-based algorithms are increasingly utilized in real time to assist clinicians with making crucial decisions for patients. Explainability and interpretability of these algorithms are critical for doctors and patients to develop trust in the automated decision making-process. Furthermore, designing specialized solutions based on clinical applications of these algorithms is non-trivial, due to the heterogeneity of imaging data available. Therefore, we propose an adaptive and interpretable framework for biomedical image analysis with novel applications to transcranial magnetic resonance-guided focused ultrasound thalamotomy for the treatment of essential tremor. The algorithm automatically configures itself to analyze brain lesions based on heterogeneous magnetic resonance images and subsequently predicts short-term clinical outcomes utilizing random forest and SHAP values, while ensuring interpretability for this process.
Obtaining satellite images has created the need to process these images and turn them into meaningful data. Every day, new methods and algorithms are designed in the literature to meet this demand. These allow the imp...
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ISBN:
(数字)9798350388961
ISBN:
(纸本)9798350388978;9798350388961
Obtaining satellite images has created the need to process these images and turn them into meaningful data. Every day, new methods and algorithms are designed in the literature to meet this demand. These allow the improvement of parameters that evaluate the performance of imaging systems, such as the Modulation Transfer Function (MTF). Within the scope of this study, the MTF value was calculated by means of edge detection filters and mathematical operators. The algorithm was developed using the Python 3.11.1 programming language. The developed algorithm was tested for two different reference electro-optical satellite images. In this study, a literature search on the subject headings is included, and the methods and purposes of the procedures are explained.
Revolutionizing industries with aerial vehicles using mobile for surveillance is the proposed title of this paper. The availability of drones has created several opportunities, particularly in the areas of agriculture...
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ISBN:
(数字)9798350379990
ISBN:
(纸本)9798350391558;9798350379990
Revolutionizing industries with aerial vehicles using mobile for surveillance is the proposed title of this paper. The availability of drones has created several opportunities, particularly in the areas of agriculture, transportation, and security. This study is a follow-up to the creation of a drone controlled by Wi-Fi-based UAV and advanced imageprocessing. To provide simple and seamless control, a mobile application has been released. Drone-taken photos and videos can also be processed and studied right away. Increasing the degree of autonomous vehicle operations, improving the accuracy of picture identification/analysis algorithms, and ensuring continuous communication between the drone and the control software are perhaps the key objectives of this work. The imageprocessing component aims to take into account sophisticated improvements such as terrain analysis, object detection, and anomaly detection, which are crucial, particularly in the context of security surveillance. Ensuring steady and long-range connectivity while taking certain environmental elements into account can be critical. Enhancing the imageprocessing technique to run smoothly on the off-board drone's hardware constraints is also a crucial step. However, integrating hardware and software continues to be a major challenge in the implementation of a dependable, high-accuracy, real-time analog-to-digital conversion system. The purpose of this research is to develop novel ways for improving UAV systems by integrating tough control systems with complicated image analysis capabilities. Keeping the aforementioned limits in mind, the purpose of this work is to improve understanding of how to create more self-sufficient and adaptive drones that can contribute to a broader range of professions or do more specific activities with minimal human interaction.
Coastal flooding events have caused many issues to infrastructure including bridges and highways. How to assess the flooding level and infrastructure damages in a low-cost, rapid, and accurate approach is critical to ...
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ISBN:
(纸本)9781510660793;9781510660809
Coastal flooding events have caused many issues to infrastructure including bridges and highways. How to assess the flooding level and infrastructure damages in a low-cost, rapid, and accurate approach is critical to the infrastructure performance recovery. Due to the limited access to infrastructure during the post-flooding events, it is very challenging to evaluate infrastructure conditions closely. With the help of small unmanned aerial vehicles and onboard cameras, it provides the possibility to inspect the infrastructure conditions from images captured by drones remotely. With the additional help of imageprocessingalgorithms, it can help capture the infrastructure conditions and flooding levels from the imageries automatically with post-processing analysis. In this paper, we apply several different imageprocessingalgorithms to assess the infrastructure conditions by segmenting the flooding zone from the infrastructure. The performance of these algorithms in assessing infrastructure conditions is compared based on different factors with previously taken airborne imageries of infrastructure and flooding events. The performance of imageprocessing is summarized and future work of assessing the infrastructure post-flooding damages is discussed.
The International Workshop on Artificial Intelligence for Signal, imageprocessing, and Multimedia (AI-SIPM) aims to provide a platform for researchers, practitioners, and industry professionals to exchange ideas, dis...
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ISBN:
(纸本)9798400706028
The International Workshop on Artificial Intelligence for Signal, imageprocessing, and Multimedia (AI-SIPM) aims to provide a platform for researchers, practitioners, and industry professionals to exchange ideas, discuss recent advancements, and explore future directions in the field of artificial intelligence (AI) applied to signal processing, imageprocessing, and multimedia technologies. This workshop will feature presentations of novel research findings, practical applications, and innovative solutions addressing various challenges and opportunities in AI-driven signal and imageprocessing, as well as multimedia analysis and understanding. Researchers and practitioners from academia, industry, and government agencies are invited to submit their original research contributions and participate in discussions that foster collaboration and knowledge sharing across different domains. Through this workshop, we aim to accelerate advancements in AI-driven technologies for signal processing, image analysis, and multimedia applications, contributing to the advancement of research and innovation in this rapidly evolving field.
In the post-harvest stages of agricultural products, labor shortages and poor-quality control lead to significant market losses. The automated industries for agricultural products that use machine learning are evolvin...
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