With applications including medical image analysis, picture sharpening and restoration, robot vision, pattern recognition, and video processing, among many others, image enhancement is the main topic in image processi...
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Accurate vehicle detection plays a vital role in intelligent transportation systems. Various day conditions, for instance, dawn, morning, noon, or non-uniform illuminations put restrictions on camera's visibility....
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Accurate vehicle detection plays a vital role in intelligent transportation systems. Various day conditions, for instance, dawn, morning, noon, or non-uniform illuminations put restrictions on camera's visibility. Such scenarios impact the performance of detection and recognition algorithms that are used in surveillance systems and autonomous driving. This paper aims to solve the aforementioned issues using machine learning methods, such as face detection and recognition. The core theme of this paper is the development of a vehicle detection and driver recognition system, which also focuses the situation where an input image is degraded by non-uniform illuminations. The proposed system is composed of four main processing modules: (i) image acquisition, (ii) image enhancement, (iii) object detection that locates vehicles' and drivers' faces, and (iv) the Pool of Face Recognition Algorithms (PoFRA), which uses four face recognition algorithms to conclude the driver's identity. We implement suitable algorithms for each of the above-described modules to appraise its practicability. The system can be adjusted to work in different types of extreme weather conditions, such as strong or dim light. Experimental results demonstrate that the proposed system has significant potential to take the research on automated car parking systems to the next level.
Kaziranga National Park, a UNESCO World Heritage Site and a sanctuary for the one-horned rhinoceros represents a critical ecosystem with a rich biodiversity that necessitates comprehensive monitoring and conservation ...
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ISBN:
(纸本)9798400716553
Kaziranga National Park, a UNESCO World Heritage Site and a sanctuary for the one-horned rhinoceros represents a critical ecosystem with a rich biodiversity that necessitates comprehensive monitoring and conservation efforts. This research article presents an in-depth study of the temporal changes within Kaziranga National Park over a decade, employing advanced imageprocessing techniques on satellite imagery data from 2014 to 2022. The primary objective was to quantify and analyze the changes in land cover, including vegetation density, water body dynamics, and wetland alterations within and around the park's premises. Utilizing a combination of multispectral analysis, change detection algorithms, and supervised classification methods, the assessment of the variations in the park's landscape was studied. There was a notable fluctuation in the water bodies, largely attributable to the annual flood cycles of the Brahmaputra River, which both enriches the park's alluvial soil and poses a challenge to wildlife conservation. Vegetation analysis indicated areas of regrowth and decline, highlighting the impacts of natural processes and human intervention on the park's wetlands. This study underlines the importance of leveraging satellite imagery and imageprocessing technologies for continuous environmental monitoring, providing an indispensable tool for conservationists, policymakers, and researchers dedicated to safeguarding natural habitats in the face of global environmental changes.
Electrohydrodynamic (EHD) printing is an additive manufacturing technique capable of microscale and nanoscale structures for biomedical, aerospace, and electronic applications. To realize stable printing at its full r...
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Electrohydrodynamic (EHD) printing is an additive manufacturing technique capable of microscale and nanoscale structures for biomedical, aerospace, and electronic applications. To realize stable printing at its full resolution, the monitoring of jetting behavior while printing and optimization of the printing process are necessary. Various machinevision control schemes have been developed for EHD printing. However, in-line machinevision systems are currently limited because only limited information can be captured in situ toward quality assurance and process optimization. In this article, we presented a machine learning-embedded machinevision control scheme that is able to characterize jetting and recognize the printing quality by using only low-resolution observations of the Taylor Cone. An innovative approach was introduced to identify and measure cone-jet behavior using low-fidelity image data at various applied voltage levels, stand-off distances, and printing speeds. The scaling law between voltages and the line widths enables quality prediction of final printed patterns. A voting ensemble composed of k-nearest neighbor (KNN), classification and regression tree (CART), random forest, logistic regression, gradient boost classifier, and bagging models was employed with optimized hyperparameters to classify the jets to their corresponding applied voltages, achieving an 88.43% accuracy on new experimental data. These findings demonstrate that it is possible to analyze jetting status and predict high-resolution pattern dimensions by using low-fidelity data. The voltage analysis based on the in situ data will provide additional insights for system stability, and it can be used to establish the error functions for future advanced control schemes.
Bananas, renowned for their delightful flavor, exceptional nutritional value, and digestibility, are among the most widely consumed fruits globally. The advent of advanced imageprocessing, computer vision, and deep l...
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Bananas, renowned for their delightful flavor, exceptional nutritional value, and digestibility, are among the most widely consumed fruits globally. The advent of advanced imageprocessing, computer vision, and deep learning (DL) techniques has revolutionized agricultural diagnostics, offering innovative and automated solutions for detecting and classifying fruit varieties. Despite significant progress in DL, the accurate classification of banana varieties remains challenging, particularly due to the difficulty in identifying subtle features at early developmental stages. To address these challenges, this study presents a novel hybrid framework that integrates the vision Transformer (ViT) model for global semantic feature representation with the robust classification capabilities of Support Vector machines. The proposed framework was rigorously evaluated on two datasets: the four-class BananaimageBD and the six-class BananaSet. To mitigate data imbalance issues, a robust evaluation strategy was employed, resulting in a remarkable classification accuracy rate (CAR) of 99.86%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\pm\:$$\end{document}0.099 for BananaSet and 99.70%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:\pm\:$$\end{document}0.17 for BananaimageBD, surpassing traditional methods by a margin of 1.77%. The ViT model, leveraging self-supervised and semi-supervised learning mechanisms, demonstrated exceptional promise in extracting nuanced features critical for agricultural applications. By combining ViT features with cutting-edge machine learning classifiers, the proposed system establishes a ne
object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion me...
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In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitiv...
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Artificial technologies have made rapid progress and achieved various superior tasks in the past few years, including but not limited to classification, detection, image generation and data processing. Particularly, t...
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Artificial technologies have made rapid progress and achieved various superior tasks in the past few years, including but not limited to classification, detection, image generation and data processing. Particularly, the very recent emerging Sora has demonstrated the exceptional ability of text-to-video generation lasting for 1 minute long with impressive quality. It provides a huge potential for many new applications across industries, especially social interaction in intelligent vehicles. The emergence of innovative intelligence vehicle applications has given rise to novel requirements for social and human-vehicle interaction within the associated contexts, where Sora and social vision could play an important role. In this perspective, we present a new Social Interaction framework based on Sora and parallel intelligence in intelligent vehicles and provide a novel perspective for conducting new social and human-vehicle interaction in the context of intelligent vehicles.
imageprocessing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent visionapplications. Traditionally, task-specific models are developed ...
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imageprocessing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent visionapplications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natural language processing (NLP), there is a similar trend in computer vision, which focuses on developing large-scale models through pretraining and in-context learning. This paradigm shift reduces the reliance on task-specific models, yielding a powerful unified model to deal with various tasks. However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general imageprocessing that covers image restoration, image enhancement, image feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies these diverse imageprocessing tasks within a universal framework. Inspired by NLP question answering (QA) techniques, we employ a visual prompting question answering paradigm. Specifically, we treat the input-output image pair as a structured question-answer sentence, thereby reprogramming the imageprocessing task as a prompting QA problem. PromptGIP can undertake diverse cross-domain tasks using provided visual prompts, eliminating the need for task-specific finetuning. Capable of handling up to 15 different imageprocessing tasks, PromptGIP represents a versatile and adaptive approach to general imageprocessing. Codes will be available at https://***/lyh-18/PromptGIP. Copyright 2024 by the author(s)
Traditional thresholding methods are widely used to extract objects of interest from image backgrounds in various practical applications. However, these methods often face challenges in complex scenes due to poor unif...
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Traditional thresholding methods are widely used to extract objects of interest from image backgrounds in various practical applications. However, these methods often face challenges in complex scenes due to poor uniformity, noise, and low contrast. To overcome these limitations, this paper proposes a peak-weaken Otsu method (PWOTSU) that improves the segmentation performance of the Otsu method for automatically extracting objects in complex scenes. The proposed approach uses a set of cross parameters as weights for the Otsu criterion function to adaptively weaken the between-class variance at the peak of the histogram. This ensures that an appropriate threshold value is always obtained for images with different types of histogram distribution. The improved criterion function has the advantage of obtaining a more accurate threshold value without the need for additional parameters, making it easily applicable to various practical applications. Experimental results demonstrate that the proposed method effectively improves the segmentation accuracy and robustness compared to the standard Otsu method and its modifications, as evidenced by qualitative and quantitative evaluations.
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