Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects
Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects
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DOI：
10.1609/aaai.v32i1.12306
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发表时间：
2017-11
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影响因子：
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通讯作者：
Yuhang Lu;Jun Zhou;Jing Wang;Jun Chen;Karen Smith;Colin Wilder;Song Wang
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文献类型：
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作者：
Yuhang Lu;Jun Zhou;Jing Wang;Jun Chen;Karen Smith;Colin Wilder;Song Wang
受探索文化遗产物体的重要考古应用的启发，在本文中，我们研究了自动分割曲线结构的挑战性问题，这些曲线结构以高噪声深度图的形式在物体表面上非常弱地冲压或雕刻。与大多数已知对噪声和遮挡非常敏感的经典低级图像分割方法不同，我们提出了一种基于卷积神经网络（CNN）的新监督学习算法，以隐式学习和利用更多曲线几何和图案信息来解决这一具有挑战性的问题。更具体地说，我们首先提出一个全卷积网络（FCN）来估计曲线结构的骨架，并在每个骨架像素处估计一个比例值以反映局部曲线宽度。然后我们提出一个密集的预测网络来细化估计的曲线骨架。基于估计的尺度值，我们最终开发了一种自适应阈值算法来实现曲线结构的最终分割。在实验中，我们在从北美东南部林地时期出土的陶器碎片扫描的深度图像数据集上验证了所提出方法的性能。
Motivated by the important archaeological application of exploring cultural heritage objects, in this paper we study the challenging problem of automatically segmenting curve structures that are very weakly stamped or carved on an object surface in the form of a highly noisy depth map. Different from most classical low-level image segmentation methods that are known to be very sensitive to the noise and occlusions, we propose a new supervised learning algorithm based on Convolutional Neural Network (CNN) to implicitly learn and utilize more curve geometry and pattern information for addressing this challenging problem. More specifically, we first propose a Fully Convolutional Network (FCN) to estimate the skeleton of curve structures and at each skeleton pixel, a scale value is estimated to reflect the local curve width. Then we propose a dense prediction network to refine the estimated curve skeletons. Based on the estimated scale values, we finally develop an adaptive thresholding algorithm to achieve the final segmentation of curve structures. In the experiment, we validate the performance of the proposed method on a dataset of depth images scanned from unearthed pottery shards dating to the Woodland period of Southeastern North America.