Publications by authors named "Enpeng Liu"

Introduction: Fusarium verticillioides (F. verticillioides) is a prevalent phytopathogen that incites severe diseases in maize, resulting in substantial reductions in grain yield and quality. Despite its widespread impact, the genetic mechanisms underlying resistance to this pathogen remain elusive, with only a limited of resistant genes having been identified to date.

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Multi-modal homography estimation aims to spatially align the images from different modalities, which is quite challenging since both the image content and resolution are variant across modalities. In this paper, we introduce a novel framework namely CrossHomo to tackle this challenging problem. Our framework is motivated by two interesting findings which demonstrate the mutual benefits between image super-resolution and homography estimation.

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Fusarium verticillioides (F. verticillioides) is a widely distributed phytopathogen that incites multiple destructive diseases in maize, posing a grave threat to corn yields and quality worldwide. However, there are few reports of resistance genes to F.

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Multi-modal image registration aims to spatially align two images from different modalities to make their feature points match with each other. Captured by different sensors, the images from different modalities often contain many distinct features, which makes it challenging to find their accurate correspondences. With the success of deep learning, many deep networks have been proposed to align multi-modal images, however, they are mostly lack of interpretability.

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Internal fungal contamination in cereal grains may affect plant growth and result in health concerns for humans and animals. is a seedborne fungus that can systemically infect maize. However, few efforts had been devoted to studying the genetics of maize resistance to seedborne .

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