【摘要】 为解决临床上对注意缺陷与多动障碍诊断耗时长，存在主观因素影响等问题，提出基于深度图像的多动儿童运动量化算法。针对多动儿童目标区域，提出基于噪声位置及灰度分布信息的去噪算法，用等值线提取多动儿童目标区域的边缘信息，分割出完整的多动儿童身体。提出基于CoM(center-of-mass)的运动时长特征，对多动儿童随时间的运动进行量化。实验对多动儿童的视频数据进行测试，其结果表明，该算法对多动儿童身体区域的分割正确率为82.73%～93.77%,运动量化正确率为88.37%～92.47%。更多还原

【Abstract】 In clinic, the diagnosis of ADHD is influenced by the subjective experiences of doctors and this diagnosing process is time-consuming. To solve these problems, an automatic movement quantification algorithm for children with ADHD was proposed based on the processing of depth images. A denoising algorithm based on location parameters and gray distribution information of depth pixels was proposed to process ROI area of ADHD. The isohypse was used to extract the edge information of ROI area for ADHD children. Based on these steps, the ROI area of ADHD child was segmented. Based on the segmentation results, the features representing the movements of ADHD child were proposed to quantify the movement based on CoM(center-of-mass). Experiments were conducted on the video data of ADHD children. The results show that the accuracies of the segmentation algorithm are 82.73%-93.77%. The accuracies of movement quantification are in the range of 88.37%-92.47%.更多还原