Learning morphological operators for depth completion

Depth images generated by direct projection of LiDAR point clouds on the image plane suffer from a great level of sparsity which is difficult to interpret by classical computer vision algorithms. We propose a method for completing sparse depth images in a semantically accurate manner by training a novel morphological neural network. Our method approximates morphological operations by Contraharmonic Mean Filter layers which are easily trained in a contemporary deep learning framework. An early fusion U-Net architecture then combines dilated depth channels and RGB using multi-scale processing. Using a large scale RGBD dataset we are able to learn the optimal morphological and convolutional filter shapes that produce an accurate and fully sampled depth image at the output. Independent experimental evaluation confirms that our method outperforms classical image restoration techniques as well as current state-of-the-art neural networks. The resulting depth images preserve object boundaries and can easily be used to augment various tasks in intelligent vehicles perception systems

Morphological feature extraction for statistical learning with applications to solar image data

Abstract: Many areas of science are generating large volumes of digital image data. In order to take full advantage of the high-resolution and high-cadence images modern technology is producing, methods to automatically process and analyze large batches of such images are needed. This involves reducing complex images to simple representations such as binary sketches or numerical summaries that capture embedded scientific information. Using techniques derived from mathematical morphology, we demonstrate how to reduce solar images into simple ‘sketch ’ representations and numerical summaries that can be used for statistical learning. We demonstrate our general techniques on two specific examples: classifying sunspot groups and recognizing coronal loop structures. Our methodology reproduces manual classifications at an overall rate of 90 % on a set of 119 magnetogram and white light images of sunspot groups. We also show that our methodology is competitive with other automated algorithms at producing coronal loop tracings and demonstrate robustness through noise simulations. 2013 Wile