Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs

Understanding the 3D structure of a scene is of vital importance, when it comes to developing fully autonomous robots. To this end, we present a novel deep learning based framework that estimates depth, surface normals and surface curvature by only using a single RGB image. To the best of our knowledge this is the first work to estimate surface curvature from colour using a machine learning approach. Additionally, we demonstrate that by tuning the network to infer well designed features, such as surface curvature, we can achieve improved performance at estimating depth and normals.This indicates that network guidance is still a useful aspect of designing and training a neural network. We run extensive experiments where the network is trained to infer different tasks while the model capacity is kept constant resulting in different feature maps based on the tasks at hand. We outperform the previous state-of-the-art benchmarks which jointly estimate depths and surface normals while predicting surface curvature in parallel

Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies

Many computer vision applications apply background suppression techniques for the detection and segmentation of moving objects in a scene. While these algorithms tend to work well in controlled conditions they often fail when applied to unconstrained real-world environments. This paper describes a system that detects and removes erroneously segmented foreground regions that are close to a ground plane. These regions include shadows, changing background objects and other low-lying objects such as leaves and rubbish. The system uses a set-up of two or more cameras and requires no 3D reconstruction or depth analysis of the regions. Therefore, a strong camera calibration of the set-up is not necessary. A geometric constraint called a homography is exploited to determine if foreground points are on or above the ground plane. The system takes advantage of the fact that regions in images off the homography plane will not correspond after a homography transformation. Experimental results using real world scenes from a pedestrian tracking application illustrate the effectiveness of the proposed approach

Label-free segmentation of co-cultured cells on a nanotopographical gradient

The function and fate of cells is influenced by many different factors, one of which is surface topography of the support culture substrate. Systematic studies of nanotopography and cell response have typically been limited to single cell types and a small set of topographical variations. Here, we show a radical expansion of experimental throughput using automated detection, measurement, and classification of co-cultured cells on a nanopillar array where feature height changes continuously from planar to 250 nm over 9 mm. Individual cells are identified and characterized by more than 200 descriptors, which are used to construct a set of rules for label-free segmentation into individual cell types. Using this approach we can achieve label-free segmentation with 84% confidence across large image data sets and suggest optimized surface parameters for nanostructuring of implant devices such as vascular stents

Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling

Online Mutual Foreground Segmentation for Multispectral Stereo Videos

The segmentation of video sequences into foreground and background regions is a low-level process commonly used in video content analysis and smart surveillance applications. Using a multispectral camera setup can improve this process by providing more diverse data to help identify objects despite adverse imaging conditions. The registration of several data sources is however not trivial if the appearance of objects produced by each sensor differs substantially. This problem is further complicated when parallax effects cannot be ignored when using close-range stereo pairs. In this work, we present a new method to simultaneously tackle multispectral segmentation and stereo registration. Using an iterative procedure, we estimate the labeling result for one problem using the provisional result of the other. Our approach is based on the alternating minimization of two energy functions that are linked through the use of dynamic priors. We rely on the integration of shape and appearance cues to find proper multispectral correspondences, and to properly segment objects in low contrast regions. We also formulate our model as a frame processing pipeline using higher order terms to improve the temporal coherence of our results. Our method is evaluated under different configurations on multiple multispectral datasets, and our implementation is available online.Comment: Preprint accepted for publication in IJCV (December 2018