RGBD IMAGE SEGMENTATION USING DEEP EDGE

In the past a few decades, many schemes have been proposed for segmenting a color image into meaningful regions. However, the newly availability of depth data provides opportunities to explore and improve the image segmentation performance. In addition, the new image processing tools based on deep learning technology are aggressively developed recently. This paper proposes a method of combining color and depth data to segment an image. As an initial stage, we partition a color image into regions using the DeepEdge tool, an image edge detection scheme developed based on the CNN (Convolutional Neural Net) technique. Then, we use the RANSAC tool to identify and merge regions with similar planar geometry (based on the depth information). At the final stage, guided by the DeepEdge information, a region merging method is employed to fine-tune the merged regions based on the color and depth similarity. Comparing to our previous results, the DeepEdge method together with the depth information helps in improving the segmentation result in most cases.