Introduction: In recent years, various deep learning algorithms have exhibited remarkable performance in various data-rich applications, like health care, medical imaging, as well as in computer vision. COvID-19, whic...
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Introduction: In recent years, various deep learning algorithms have exhibited remarkable performance in various data-rich applications, like health care, medical imaging, as well as in computer vision. COvID-19, which is a rapidly spreading virus, has affected people of all ages both socially and economically. Early detection of this virus is therefore important in order to prevent its further spread. Methods: COvID-19 crisis has also galvanized researchers to adopt various machine learning as well as deep learning techniques in order to combat the pandemic. Lung images can be used in the diagnosis of COvID-19. Results: In this paper, we have analysed the COvID-19 chest CT image classification efficiency using multilayer perceptron with different imaging filters, like edge histogram filter, colour histogram equalization filter, color-layout filter, and Garbo filter in the WEKA environment. Conclusion: The performance of CT image classification has also been compared comprehensively with the deep learning classifier Dl4jMlp. It was observed that the multilayer perceptron with edge histogram filter outperformed other classifiers compared in this paper with 89.6% of correctly classified instances.
Recent developments in image analysis and interpretation using computer vision techniques have shown potential for novel applications in microbiology laboratories to support the task of automation, aiming for faster a...
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Rice is a staple food for a significant portion of the global population, making accurate classification of rice varieties essential for farming and consumer protection. This review provides a focused analysis of the ...
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Rice is a staple food for a significant portion of the global population, making accurate classification of rice varieties essential for farming and consumer protection. This review provides a focused analysis of the current advancements and challenges in applying computer vision (Cv) techniques to rice variety classification. The study examines key steps in the automation process, including image acquisition, pre-processing, feature extraction, and classification algorithms, with particular emphasis on machine learning and deep learning methods such as Convolutional Neural Networks (CNNs), which have demonstrated exceptional performance in recent research. However, practical implementation faces challenges, including the availability of high-quality datasets, the impact of environmental variations on image quality, and the computational demands of complex models. Our study discusses these obstacles and highlights the importance of developing resilient and scalable systems for real-world applications. By synthesizing findings from various studies, this review proposes future directions for advancing rice variety classification, focusing on improved feature extraction techniques, enhanced dataset management, and integrating innovative machine learning paradigms. This work is a valuable resource for researchers and practitioners aiming to advance rice classification technologies and contribute to food security and agricultural sustainability.
This paper introduces the structure and operation mode of automatic production line based on the actual situation of laser quenching automatic production line of tool in enterprises. Robot vision integrates workpiece ...
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ISBN:
(纸本)9781665464680
This paper introduces the structure and operation mode of automatic production line based on the actual situation of laser quenching automatic production line of tool in enterprises. Robot vision integrates workpiece positioning coordinates with robot coordinates to realize the positioning and grasping function of robot through machinevision. Focus on OpenCvimageprocessing methods. This paper describes its principle and possible problems from the aspects of system structure, robot coordinate calibration, visual identification and positioning and software design.
Superpixels play a crucial role in imageprocessing by partitioning an image into clusters of pixels with similar visual attributes. This facilitates subsequent imageprocessing tasks, offering computational advantage...
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ISBN:
(纸本)9798350353013;9798350353006
Superpixels play a crucial role in imageprocessing by partitioning an image into clusters of pixels with similar visual attributes. This facilitates subsequent imageprocessing tasks, offering computational advantages over the manipulation of individual pixels. While numerous oversegmentation techniques have emerged in recent years, many rely on predefined initialization and termination criteria. In this paper, a novel top-down superpixel segmentation algorithm called Hierarchical Histogram Threshold Segmentation (HHTS) is introduced. It eliminates the need for initialization and implements auto-termination, outperforming state-of-the-art methods w.r.t. boundary recall. This is achieved by iteratively partitioning individual pixel segments into foreground and background and applying intensity thresholding across multiple color channels. The underlying iterative process constructs a superpixel hierarchy that adapts to local detail distributions until color information exhaustion. Experimental results demonstrate the superiority of the proposed approach in terms of boundary adherence, while maintaining competitive runtime performance on the BSDS500 and NYUv2 datasets. Furthermore, an application of HHTS in refining machine learning-based semantic segmentation masks produced by the Segment Anything Foundation Model (SAM) is presented.
Accurate plant identification is critical for applications such as automated agriculture and plant monitoring systems. However, traditional classification methods often face challenges in balancing accuracy and comput...
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Accurate plant identification is critical for applications such as automated agriculture and plant monitoring systems. However, traditional classification methods often face challenges in balancing accuracy and computational efficiency, particularly when handling large datasets or real-time processing. This research aims to develop a classification scheme that efficiently identifies plant types based on color and shape attributes, achieving high accuracy with minimal computational complexity. To address this, we propose a two-level classification approach using a Naive Bayes classifier in a hierarchical structure. The first stage utilizes simple color features to categorize the majority of images with high accuracy and low computational overhead. In cases where classification remains uncertain, the second stage extracts additional color and shape attributes, offering a more refined analysis of complex samples. The scheme is implemented within an Internet of Things (IoT)-enabled data acquisition framework, enabling real-time image data collection. The system was evaluated using four types of artificial plants placed in a growth chamber equipped with image sensors and LED lighting, with data processed through a cloud service. The results demonstrate that the two-level classifier outperforms single-level approaches, maintaining high accuracy by deferring more complex samples to the second stage without significantly increasing computational costs. This hierarchical classification scheme successfully balances efficiency and accuracy, making it well-suited for large-scale applications such as smart greenhouses, where reliable and rapid plant classification is essential.
This study investigates the capabilities and flexibility of edge devices for real-time data processing near the source. A configurable Nvidia Jetson Nano system is used to deploy nine pre-trained computer vision model...
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The main research content of machinevision technology is to identify objects through computers, extract the features of objects, and then divide the categories of objects according to the feature value and threshold....
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This research conducted a bibliometric analysis of scholarly literature on fruit sorting and grading using machinevision, identifying primary themes, sources, most -cited publications, and countries. The literature a...
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This research conducted a bibliometric analysis of scholarly literature on fruit sorting and grading using machinevision, identifying primary themes, sources, most -cited publications, and countries. The literature and bibliometric analysis were thoroughly evaluated to consolidate knowledge, identify research trends, and propose specific research opportunities within the context of machinevision for fruit sorting and grading. Research articles from 2011 to 2023, indexed in the main collections of the Dimensions, Web -of -science, and Scopus databases, were examined. Findings were presented quantitatively, using tables and graphs to emphasize the key performance factors for article writing and citation. Upon applying inclusion and exclusion criteria, 129 out of 1812 discovered articles were included for examination, while 1683 studies were excluded due to noncompliance with the requirements and duplicates. Thirty-four (34) case study publications on machinevisionapplications for fruit sorting and grading were comprehensively examined to identify the adopted methodologies and future research opportunities. Covered methodologies include fruit varieties, data volumes, data collection, classification methods, and accuracy metrics. The study's findings indicate a significant increase in deep learning applications for fruit recognition in the recent five years (2019-2023), with excellent results achieved either by utilizing new models or with pre -trained networks for transfer learning. The research also identifies gaps and future directions for machinevision in fruit sorting and grading, such as enhancing system robustness, scalability, and adaptability, integrating multiple sensors and technological methods, and developing evaluation and comparison standards and criteria. The paper concludes that machinevision holds promise as a potent tool for fruit quality assessment, but further research and development are needed to address existing challenges and meet the grow
In recent years, computer vision has made significant strides in enabling machines to perform a wide range of tasks, from image classification and segmentation to image generation and video analysis. It is a rapidly e...
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ISBN:
(数字)9781510661936
ISBN:
(纸本)9781510661929;9781510661936
In recent years, computer vision has made significant strides in enabling machines to perform a wide range of tasks, from image classification and segmentation to image generation and video analysis. It is a rapidly evolving field that aims to enable machines to interpret and understand visual information from the environment. One key task in computer vision is image classification, where algorithms identify and categorize objects in images based on their visual features. image classification has a wide range of applications, from image search and recommendation systems to autonomous driving and medical diagnosis. However, recent research has highlighted the presence of bias in image classification algorithms, particularly with respect to human-sensitive attributes such as gender, race, and ethnicity. Some examples are computer programmers being predicted better in the context of men in images compared to women, and the accuracy of the algorithm being better on greyscale images compared to colored images. This discrepancy in identifying objects is developed through correlation the algorithm learns from the objects in context known as contextual bias. This bias can result in inaccurate decisions, with potential consequences in areas such as hiring, healthcare, and security. In this paper, we conduct an empirical study to investigate bias in the image classification domain based on sensitive attribute gender using deep convolutional neural networks (CNN) through transfer learning and minimize bias within the image context using data augmentation to improve overall model performance. In addition, cross-data generalization experiments are conducted to evaluate model robustness across popular open-source image datasets.
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