Efficient binocular stereo matching based on SAD and improved census transformation

Binocular stereo matching aims to obtain disparities from two very close views. Existing stereo matching methods may cause false matching when there are much image noise and disparity discontinuities. This paper proposes a novel binocular stereo matching algorithm based on SAD and improved Census transformation. We first perform improved Census transformation, and then get the matching costs by combining SAD and improved Census transformation. Finally we cluster the matching costs and calculate the disparities. To generate better disparities, we further propose the improved bilateral and selective filters to enhance the accuracy of disparities. Experimental results show that our binocular stereo matching can produce more accurate and complete disparities, and works well in complex scenes with irregular shapes and more objects , thus has wide applications in stereoscopic image processing

Wide-baseline object interpolation using shape prior regularization of epipolar plane images

This paper considers the synthesis of intermediate views of an object captured by two calibrated and widely spaced cameras. Based only on those two very different views, our paper proposes to reconstruct the object Epipolar Plane Image Volume [1] (EPIV), which describes the object transformation when continuously moving the viewpoint of the synthetic view in-between the two reference cameras. This problem is clearly ill-posed since the occlusions and the foreshortening effect make the reference views significantly different when the cameras are far apart. Our main contribution consists in disambiguating this ill-posed problem by constraining the interpolated views to be consistent with an object shape prior. This prior is learnt based on images captured by the two reference views, and consists in a nonlinear shape manifold representing the plausible silhouettes of the object described by Elliptic Fourier Descriptors. Experiments on both synthetic and natural images show that the proposed method preserves the topological structure of objects during the intermediate view synthesis, while dealing effectively with the self-occluded regions and with the severe foreshortening effect associated to wide-baseline camera configurations

An Analysis of Camera Configurations and Depth Estimation Algorithms For Triple-Camera Computer Vision Systems

The ability to accurately map and localize relevant objects surrounding a vehicle is an important task for autonomous vehicle systems. Currently, many of the environmental mapping approaches rely on the expensive LiDAR sensor. Researchers have been attempting to transition to cheaper sensors like the camera, but so far, the mapping accuracy of single-camera and dual-camera systems has not matched the accuracy of LiDAR systems. This thesis examines depth estimation algorithms and camera configurations of a triple-camera system to determine if sensor data from an additional perspective will improve the accuracy of camera-based systems. Using a synthetic dataset, the performance of a selection of stereo depth estimation algorithms is compared to the performance of two triple-camera depth estimation algorithms: disparity fusion and cost fusion. The cost fusion algorithm in both a multi-baseline and multi-axis triple-camera configuration outperformed the environmental mapping accuracy of non-CNN algorithms in a two-camera configuration

On Semantic Segmentation and Path Planning for Autonomous Vehicles within Off-Road Environments

There are many challenges involved in creating a fully autonomous vehicle capable of safely navigating through off-road environments. In this work we focus on two of the most prominent such challenges, namely scene understanding and path planning. Scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we build on recent work in urban road-scene understanding, training a state of the art CNN architecture towards the task of classifying off-road scenes. We analyse the effects of transfer learning and training data set size on CNN performance, evaluating multiple configurations of the network at multiple points during the training cycle, investigating in depth how the training process is affected. We compare this CNN to a more traditional feature-driven approach with Support Vector Machine (SVM) classifier and demonstrate state-of-the-art results in this particularly challenging problem of off-road scene understanding. We then expand on this with the addition of multi-channel RGBD data, which we encode in multiple configurations for CNN input. We evaluate each of these configuration over our own off-road RGBD data set and compare performance to that of the network model trained using RGB data. Next, we investigate end-to-end navigation, whereby a machine learning algorithm optimises to predict the vehicle control inputs of a human driver. After evaluating such a technique in an off-road environment and identifying several limitations, we propose a new approach in which a CNN learns to predict vehicle path visually, combining a novel approach to automatic training data creation with state of the art CNN architecture to map a predicted route directly onto image pixels. We then evaluate this approach using our off-road data set, and demonstrate effectiveness surpassing existing end-to-end methods