This paper presents a multi-face detection method for color images. The method is based on the assumption that faces are well separated from the background by skin color detection. These faces can be located by the pr...This paper presents a multi-face detection method for color images. The method is based on the assumption that faces are well separated from the background by skin color detection. These faces can be located by the proposed method which modifies the subtractive clustering. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. Downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. The effectiveness of the proposed method is illustrated by three experiments.展开更多
<strong>Background:</strong> High-resolution medical images often need to be downsampled because of the memory limitations of the hardware used for machine learning. Although various image interpolation me...<strong>Background:</strong> High-resolution medical images often need to be downsampled because of the memory limitations of the hardware used for machine learning. Although various image interpolation methods are applicable to downsampling, the effect of data preprocessing on the learning performance of convolutional neural networks (CNNs) has not been fully investigated. <strong>Methods:</strong> In this study, five different pixel interpolation algorithms (nearest neighbor, bilinear, Hamming window, bicubic, and Lanczos interpolation) were used for image downsampling to investigate their effects on the prediction accuracy of a CNN. Chest X-ray images from the NIH public dataset were examined by downsampling 10 patterns. <strong>Results:</strong> The accuracy improved with a decreasing image size, and the best accuracy was achieved at 64 × 64 pixels. Among the interpolation methods, bicubic interpolation obtained the highest accuracy, followed by the Hamming window.展开更多
SAR-BM3D is one of the state of the art despeckling algorithms for SAR images. However, when tackling with high resolution SAR images, it often has an unsatisfying despeckling performance in the homogeneous smooth reg...SAR-BM3D is one of the state of the art despeckling algorithms for SAR images. However, when tackling with high resolution SAR images, it often has an unsatisfying despeckling performance in the homogeneous smooth regions, together with a high time complexity. In this paper, a novel downsampled SAR-BM3D despeckling approach combined with edge compensation is proposed. The proposed algorithm consists of two steps. First, despeckle the image which is a downsampled version of original image with SAR-BM3D. Then, compensate edges in each level when upsampling. This approach not only utilizes the good ability of feature preservation, but also improves performance of smoothing homogenous regions. When it comes to high resolution SAR images, the efficiency can be raised by six to seven times, compared to original SAR-BM3D. Experiments on simulated and real SAR images show that the proposed method reaches a high level in terms of visual quality and act more efficiently.展开更多
目前的绝缘子及缺陷目标检测算法中普遍存在着诸如误检、漏检和检测精度低等一系列问题,提出一种改进的YOLOv9绝缘子及缺陷目标检测算法来更好地检测绝缘子及其残缺缺陷。首先,在YOLOv9的核心模块RepNCSPELAN中嵌入多样性分支块DBB,DBB...目前的绝缘子及缺陷目标检测算法中普遍存在着诸如误检、漏检和检测精度低等一系列问题,提出一种改进的YOLOv9绝缘子及缺陷目标检测算法来更好地检测绝缘子及其残缺缺陷。首先,在YOLOv9的核心模块RepNCSPELAN中嵌入多样性分支块DBB,DBB可以增强单个卷积的表示能力,丰富特征空间,提高模型的特征提取能力,提升模型性能,同时基本不增加推理时间成本。其次,使用Haar小波的下采样HWD替换传统下采样,可以降低特征图的空间分辨率,同时保留尽可能多的信息,并且与传统的下采样方法相比,可以有效降低信息不确定性。最后使用MPDIoU作为模型的损失函数,MPDIoU通过直接计算预测框和真实框之间的关键点距离,能更准确地反映预测框和真实框之间的差异,从而提升模型的平均精度。在绝缘子及缺陷数据集上,改进后的算法YOLOv9-DHM的平均检测精度(Mean Average Precision,mAP)提高至96.8%,相比于原始算法提高了2.2%,精确率和召回率分别提高至95.4%和94.5%。改进后的算法相比原始算法,平均检测精度有明显提升,证明了算法改进后的可行性。展开更多
文摘This paper presents a multi-face detection method for color images. The method is based on the assumption that faces are well separated from the background by skin color detection. These faces can be located by the proposed method which modifies the subtractive clustering. The modified clustering algorithm proposes a new definition of distance for multi-face detection, and its key parameters can be predetermined adaptively by statistical information of face objects in the image. Downsampling is employed to reduce the computation of clustering and speed up the process of the proposed method. The effectiveness of the proposed method is illustrated by three experiments.
文摘<strong>Background:</strong> High-resolution medical images often need to be downsampled because of the memory limitations of the hardware used for machine learning. Although various image interpolation methods are applicable to downsampling, the effect of data preprocessing on the learning performance of convolutional neural networks (CNNs) has not been fully investigated. <strong>Methods:</strong> In this study, five different pixel interpolation algorithms (nearest neighbor, bilinear, Hamming window, bicubic, and Lanczos interpolation) were used for image downsampling to investigate their effects on the prediction accuracy of a CNN. Chest X-ray images from the NIH public dataset were examined by downsampling 10 patterns. <strong>Results:</strong> The accuracy improved with a decreasing image size, and the best accuracy was achieved at 64 × 64 pixels. Among the interpolation methods, bicubic interpolation obtained the highest accuracy, followed by the Hamming window.
文摘SAR-BM3D is one of the state of the art despeckling algorithms for SAR images. However, when tackling with high resolution SAR images, it often has an unsatisfying despeckling performance in the homogeneous smooth regions, together with a high time complexity. In this paper, a novel downsampled SAR-BM3D despeckling approach combined with edge compensation is proposed. The proposed algorithm consists of two steps. First, despeckle the image which is a downsampled version of original image with SAR-BM3D. Then, compensate edges in each level when upsampling. This approach not only utilizes the good ability of feature preservation, but also improves performance of smoothing homogenous regions. When it comes to high resolution SAR images, the efficiency can be raised by six to seven times, compared to original SAR-BM3D. Experiments on simulated and real SAR images show that the proposed method reaches a high level in terms of visual quality and act more efficiently.
文摘目前的绝缘子及缺陷目标检测算法中普遍存在着诸如误检、漏检和检测精度低等一系列问题,提出一种改进的YOLOv9绝缘子及缺陷目标检测算法来更好地检测绝缘子及其残缺缺陷。首先,在YOLOv9的核心模块RepNCSPELAN中嵌入多样性分支块DBB,DBB可以增强单个卷积的表示能力,丰富特征空间,提高模型的特征提取能力,提升模型性能,同时基本不增加推理时间成本。其次,使用Haar小波的下采样HWD替换传统下采样,可以降低特征图的空间分辨率,同时保留尽可能多的信息,并且与传统的下采样方法相比,可以有效降低信息不确定性。最后使用MPDIoU作为模型的损失函数,MPDIoU通过直接计算预测框和真实框之间的关键点距离,能更准确地反映预测框和真实框之间的差异,从而提升模型的平均精度。在绝缘子及缺陷数据集上,改进后的算法YOLOv9-DHM的平均检测精度(Mean Average Precision,mAP)提高至96.8%,相比于原始算法提高了2.2%,精确率和召回率分别提高至95.4%和94.5%。改进后的算法相比原始算法,平均检测精度有明显提升,证明了算法改进后的可行性。
