Image Processing

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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This is a data set to show the advantage of the method in this paper, the data set has two kinds of data: the multi-sine phase-shifting images and comparison method images, the images are all modulated for different targets, such as: the white ceramic cuboid, The half of white ping pong ball and no targets. In this data set, the “3phase.bmp” are the images for the method in this paper, the ”2plus1.bmp” are the images for all kinds of two phase-shifting methods, where R layer image is average intensity image, G layer and B layer images are phase-shifting images.

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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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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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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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21 Views

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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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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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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