ABSTRACT We propose a new approach which is a three-stage pipeline to fast and accurate segment hand from a single depth image. Firstly, a depth frame is segmented into several regions by histogram-based thresholds selection algorithm and tracing the exterior boundaries of objects. We found that MINIMUM, MEAN and MEDIAN are effective ways to separate objects and the threshold in the valley between two maxima similar to MINIMUM algorithm with a minimum error. Then, each segmentation proposal is evaluated by a 3-layers shallow convolutional neural network (CNN) which is trained as a binary classification function to predict whether it is a partition of hand. Finally, all hand components are merged as our hand segmentation result. In our experiment, we use a set of real data containing more than 200,000 frames of depth images. Compared with the results achieved by approaches based on RDF and SegNet, results demonstrate that our approach achieves better performance in high-accuracy (88.34% mean IoU) within shorter processing time (8 ms).

中文翻译：

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来自深度图的手部分割管道：直方图阈值选择和浅层 CNN 分类的集成方法

摘要 我们提出了一种新方法，它是一个三级流水线，可以从单个深度图像中快速准确地分割手部。首先，通过基于直方图的阈值选择算法并跟踪对象的外部边界，将深度帧分割成多个区域。我们发现 MINIMUM、MEAN 和 MEDIAN 是在两个最大值之间的山谷中分离对象和阈值的有效方法，类似于 MINIMUM 算法，误差最小。然后，每个分割提议都由一个 3 层浅层卷积神经网络 (CNN) 评估，该网络被训练为二元分类函数，以预测它是否是手的分区。最后，将所有手部组件合并为我们的手部分割结果。在我们的实验中，我们使用了一组包含超过 200,000 帧深度图像的真实数据。