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
传统的快速激光雷达里程计与建图(Fast LiDAR odometry and mapping,F-LOAM)算法虽然对特征点进行了两级去畸变处理,但仅对第1阶段的特征点进行去畸变,第2阶段的去畸变主要用于建图,这导致位姿估计的准确性不高。为了解决这一问题,提出...传统的快速激光雷达里程计与建图(Fast LiDAR odometry and mapping,F-LOAM)算法虽然对特征点进行了两级去畸变处理,但仅对第1阶段的特征点进行去畸变,第2阶段的去畸变主要用于建图,这导致位姿估计的准确性不高。为了解决这一问题,提出了一种改进的三级去畸变机制,结合基于体素化网格的分层降采样机制,以提高算法的实时性。经过改进的F-LOAM算法在KITTI数据集上的测试表现出色。三级去畸变机制和分层降采样策略不仅有效降低了计算负担,还确保了特征点的有效性和全局地图的精度。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.
文摘传统的快速激光雷达里程计与建图(Fast LiDAR odometry and mapping,F-LOAM)算法虽然对特征点进行了两级去畸变处理,但仅对第1阶段的特征点进行去畸变,第2阶段的去畸变主要用于建图,这导致位姿估计的准确性不高。为了解决这一问题,提出了一种改进的三级去畸变机制,结合基于体素化网格的分层降采样机制,以提高算法的实时性。经过改进的F-LOAM算法在KITTI数据集上的测试表现出色。三级去畸变机制和分层降采样策略不仅有效降低了计算负担,还确保了特征点的有效性和全局地图的精度。
