In this study,the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm.This study evaluates consistent alpha masks based on the Gaussian-Hermite mome...In this study,the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm.This study evaluates consistent alpha masks based on the Gaussian-Hermite moment in combination with gradient amplitude and gradient direction.The gradient direction describes the appearance and shape of local objects in the image,and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image.They selected Gaussian blur,pretzel noise,and combined noise to destroy the image,and then evaluated the consistency of the original alpha mask and noise alpha mask.To determine the robustness of the matting algorithm,they assessed the degree of consistency of the alpha mask using three different evaluation levels.The experimental results show that noise has a greater impact on the performance of the matting algorithm,which shows a decreasing trend as the noise level in the image deepens.In noisy images,the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm.Different matting algorithms present different adaptations to different noises.展开更多
Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and dive...Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and diverse detection environments, hindering their effectiveness. In this study, we present EagleEyeNet, a novel multi-scale information fusion model designed to address these challenges. EagleEyeNet leverages large models as teacher networks in a knowledge distillation framework, significantly improving detection performance. Additionally, a newly designed feature fusion architecture, integrating Transformer modules, is proposed to enable the efficient fusion of global and multi-scale features, overcoming the bottlenecks posed by Feature Pyramid Networks (FPN) structures, which in turn reduces information transmission loss between feature layers. To improve the model’s adaptability for different scenarios, we create our own QingDao Bacteria Detection (QDBD) dataset as a comprehensive evaluation benchmark for bacterial detection. Experimental results demonstrate that EagleEyeNet achieves remarkable performance improvements, with mAP50 increases of 3.1% on the QDBD dataset and 4.9% on the AGRA dataset, outperforming the State-Of-The-Art (SOTA) methods in detection accuracy. These findings underscore the transformative potential of integrating large models and deep learning for advancing bacterial detection technologies.展开更多
基金supported by the National Natural Science Foundation of China(61772319,61773244,61976125,61976124)Shandong Natural Science Foundation of China(ZR2017MF049)Yantai Key Research and Development Plan(2019XDHZ081).
文摘In this study,the authors propose a method to calculate the consistency of alpha masking to assess the robustness of the matting algorithm.This study evaluates consistent alpha masks based on the Gaussian-Hermite moment in combination with gradient amplitude and gradient direction.The gradient direction describes the appearance and shape of local objects in the image,and the gradient amplitude accurately reflects the contrast and texture changes of small details in the image.They selected Gaussian blur,pretzel noise,and combined noise to destroy the image,and then evaluated the consistency of the original alpha mask and noise alpha mask.To determine the robustness of the matting algorithm,they assessed the degree of consistency of the alpha mask using three different evaluation levels.The experimental results show that noise has a greater impact on the performance of the matting algorithm,which shows a decreasing trend as the noise level in the image deepens.In noisy images,the traditional matting algorithm exhibits better robustness compared to the recently proposed trap matting algorithm.Different matting algorithms present different adaptations to different noises.
基金supported by the Shandong Provincial Department of Education.
文摘Accurate and efficient bacterial detection is essential for public health and medical diagnostics. However, traditional detection methods are constrained by limited dataset size, complex bacterial morphology, and diverse detection environments, hindering their effectiveness. In this study, we present EagleEyeNet, a novel multi-scale information fusion model designed to address these challenges. EagleEyeNet leverages large models as teacher networks in a knowledge distillation framework, significantly improving detection performance. Additionally, a newly designed feature fusion architecture, integrating Transformer modules, is proposed to enable the efficient fusion of global and multi-scale features, overcoming the bottlenecks posed by Feature Pyramid Networks (FPN) structures, which in turn reduces information transmission loss between feature layers. To improve the model’s adaptability for different scenarios, we create our own QingDao Bacteria Detection (QDBD) dataset as a comprehensive evaluation benchmark for bacterial detection. Experimental results demonstrate that EagleEyeNet achieves remarkable performance improvements, with mAP50 increases of 3.1% on the QDBD dataset and 4.9% on the AGRA dataset, outperforming the State-Of-The-Art (SOTA) methods in detection accuracy. These findings underscore the transformative potential of integrating large models and deep learning for advancing bacterial detection technologies.
