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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The Industrial robot visual servo imageprocessing requires highly autonomous and intelligence robotic manipulators, with goal of performing manipulation tasks independently without human interventions. However, limit...
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The effectiveness of autonomous vehicles relies on clear visual input, which rain can significantly obstruct. Rain streaks degrade the quality of captured images and videos, affecting both user perception and the func...
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ISBN:
(纸本)9781510679344;9781510679351
The effectiveness of autonomous vehicles relies on clear visual input, which rain can significantly obstruct. Rain streaks degrade the quality of captured images and videos, affecting both user perception and the functionality of outdoor vision systems, such as those in autonomous vehicles. This visual degradation impacts the vehicle's ability to interpret its environment, increasing the risk of driving in rainy conditions. Researchers have responded to this challenge by developing various rain removal algorithms, ranging from single-image to video-based approaches, each with its own strengths and weaknesses. This research aims to develop two novel, efficient single-image rain removal algorithms that strike a balance between high performance and quick execution. The proposed algorithms will address the need for an effective de-raining technique suitable for real-time use in autonomous vehicles. By improving visibility in rainy conditions, this innovation will enhance the performance and safety of autonomous vehicles, contributing to advancements in the field. A survey involving ten imageprocessing experts and professionals, who evaluated the results of both algorithms based on perceived quality and improvement, revealed that Algorithm 1 received a higher average rating (0.58) compared to Algorithm 2 (0.43). Although Algorithm 1 is slightly preferred based on average participant ratings, Algorithm 2's superior edge preservation and image sharpness make it more favorable for applications demanding high accuracy and detailed image retention. Overall, the project meets the demand for real-time rain removal in autonomous vehicles and provides valuable insights into the effectiveness of Algorithm 1 in de-raining images compared to Algorithm
In this study, an automatic monitoring system based on convolutional neural network (CNN) is proposed to address the automation and accuracy of remote sensing imageprocessing. With the maturity and wide application o...
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imageprocessing (IP) technology has emerged on the basis of AI, digital imaging technology, and multimedia technology, and people began to use computers to process images to improve image quality and improve human vi...
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This research paper introduces a Rule-Based Expert System designed for the automated editing of documents in PDF and PPT formats. The system employs a set of predefined rules, extracted from guideline documents using ...
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ISBN:
(数字)9783031585616
ISBN:
(纸本)9783031585609;9783031585616
This research paper introduces a Rule-Based Expert System designed for the automated editing of documents in PDF and PPT formats. The system employs a set of predefined rules, extracted from guideline documents using a Large Language Model (LLM), to execute tasks such as redaction of sensitive text/logo detection and annotation of text elements that deviate from prescribed font size guidelines. Following the detection and annotation process, the system further enhances documents by resizing the detected text elements based on the predefined rules. To achieve these editing tasks, the system integrates advanced imageprocessing techniques, leveraging fine-tuned Optical Character Recognition (OCR) for accurate text extraction from document images. Furthermore, Natural Language processing (NLP) algorithms are utilized to analyze and interpret textual content. The combination of imageprocessing, OCR, NLP, and rule extraction using LLM ensures a comprehensive approach to document editing, enhancing efficiency and accuracy. The proposed system addresses the need for automated and rule-driven document editing, contributing to advancements in information security and document standardization.
This work offers a thorough method for real-time dehazing of drone-captured images by different filtering techniques with post-processing improvements. Enhancing visibility and picture clarity in hazy situations is th...
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Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is ver...
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ISBN:
(纸本)9798350369298;9798350369304
Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its structure can be captured via an affinity graph. This allows us to utilize ideas from graph signal processing. In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise. The signals are assumed to follow a prior distribution defined in the frequency domain which favors signals which are smooth across the edges of the graph. By pairing this prior distribution with our three models of noise generation, we propose Maximum A Posteriori (M.A.P.) estimates of the true signal in the presence of noisy data and provide algorithms for computing the M.A.P. Finally, we demonstrate the algorithms' ability to effectively restore signals from white noise on image data and from severe dropout in single-cell RNA sequence data.
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