Image Processing
With the fast growth of deep learning, trainable frameworks have been presented to restore hazy images. However, the capability of most existing learning-based methods is limited since the parameters learned in an end-
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In international contexts, natural scenes may include text in multiple languages. Especially, Latin and Arabic scene character image dataset is essential for training models to accurately detect and recognize text regions within real-world images. This is crucial for applications such as text translation, image search, content analysis, and autonomous vehicles that need to interpret text in different languages.
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The MalariaSD dataset encompasses diverse stages and classes of malaria parasites, including Plasmodium falciparum, Plasmodium malariae, Plasmodium vivax, and Plasmodium ovale, categorized into four phases: ring, schizont, trophozoite, and gametocyte.
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This is the dataset used in the paper "Application of improved lightweight network and Choquet fuzzy ensemble technology for soybean disease identification". This data set contains 6 types of soybean disease leaves collected from Xiangyang Farm, Nengjiang Farm and Jiusan Farm of Northeast Agricultural University in Heilongjiang Province from early June to late September 2019. All images are collected in natural scenes. A total of 1620 disease images of soybean leaves were collected.
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The presented dataset encompasses a diverse collection of road images captured under a multitude of environmental conditions, specifically sourced from Tunisian highways. Comprising textual annotations in two languages, this dataset is tailored to facilitate research and development in the domain of scene understanding, language processing, and bilingual context analysis. The collection includes 2006 word pictures with Latin and Arabic text occurrences that were taken from 3000 road scene images. The dataset's versatility enables investigations into the robustness of lang
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The presented dataset encompasses a diverse collection of road images captured under a multitude of environmental conditions, specifically sourced from Tunisian highways. Comprising textual annotations in two languages, this dataset is tailored to facilitate research and development in the domain of scene understanding, language processing, and bilingual context analysis. The collection includes 2006 word pictures with Latin and Arabic text occurrences that were taken from 3000 road scene images. The dataset's versatility enables investigations into the robustness of lang
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42 Views
SAR-optical remote sensing couples are widely exploited for their complementarity for land-cover and crops classifications, image registration, change detections and early warning systems. Nevertheless, most of these applications are performed on flat areas and cannot be generalized to mountainous regions. Indeed, steep slopes are disturbing the range sampling which causes strong distortions in radar acquisitions - namely, foreshortening, shadows and layovers.
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463 Views
In contemporary digital environments, the development of a high-resolution synthetic Latin character dataset holds paramount significance across various real-world applications within the domains of computer vision and artificial intelligence. This relevance extends from tasks such as image restoration to the implementation of sophisticated recognition systems.
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504 Views
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The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification.
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1090 Views