Automatic object segmentation with 3-D cameras
Haowei Liu, Matthai Philipose, Ming–Ting Sun · 2012
Recently, active 3-D cameras, which provide streams of depth and color images, have become widespread and popular. The depth data provides useful information for identifying object boundaries, making automatic object segmentation possible. However, the depth images are extremely noisy, and due to different response time of the color and depth sensors, the depth and color information often lose synchronization when the object is moving fast. In this work, we show how to combine depth and color information to clean up the depth maps and produce an accurate segmentation of the object. On a large dataset, we show that our proposed techniques are effective.