By far, many stereo-matching techniques have been successfully proposed and applied in digital aerial photogrammetry. However, due to some problems such as large parallaxes, occlusions, geometric deformations, and repetitive patterns in convergent close range images, these methods may not be applicable to the same level of success as that of aerial imagery. In order to overcome these shortcomin...

Only few problems in computer vision have been investigated more vigorously than stereo. Nevertheless, almost all methods use only gray values and most of them are feature-based techniques, i.e., they produce only sparse depth maps. This paper presents an efficient technique for dense stereo correspondence using a new Polychromatic Block Matching. Four different color models (RGB, XYZ, IlI2I 3,...

Prediction errors are commonly used when analyzing the performance of a multi-camera stereo system using at least three cameras. This paper discusses this methodology for performance evaluation on long stereo sequences (in the context of vision-based driver assistance systems). Three cameras are calibrated in an ego-vehicle, and prediction error analysis is performed on recorded stereo sequence...

This paper presents a fast approach for matching stereoscopic images acquired by stereo cameras mounted aboard a moving car. The proposed approach exploits the spatio-temporal consistency between consecutive frames in stereo sequences to improve matching results. This means that the matching process at current frame uses the matching results obtained at its preceding one. The preceding frame al...

Our project starts from a practical specific application of stereo vision (matching) on a robot arm, which is first building up a vision system for a robot arm to make it obtain the capability of detecting the objects 3D information on a plane. The kernel of the vision system is stereo matching. Stereo matching(correspondence) problem has been studied for a few decades; it is one of the most in...

We propose a non-learning depth completion method for sparse map captured using light detection and ranging (LiDAR) sensor guided by pair of stereo images. Generally, conventional stereo-aided methods have two limiations. (i) They assume the given is accurately aligned to input image, whereas alignment difficult achieve in practice. (ii) limited accuracy long range because estimated pixel dispa...

In the research literature, maximum likelihood principles were applied to stereo matching by altering the stereo pair so that the difference would have a Gaussian distribution. Here in this paper we present a novel method of applying maximum likelihood to stereo matching. In our approach, we measure the real noise distribution from a training set, and then construct a new metric which we denote...

Recent work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNNs). However, current architectures rely on patch-based Siamese networks, lacking the means to exploit context information for finding correspondence in illposed regions. To tackle this problem, we propose PSMNet, a pyramid...

The present work investigates the potential of neural adaptive learning to solve the correspondence problem within a two-frame adaptive area matching approach. A novel method is proposed based on the use of the Zero Mean Normalized Cross Correlation Coefficient integrated within a neural network model which use least-mean-square delta rule for training. Two experiments were conducted for evalua...

Determination of the depth of the image feature distinctive automation and other industries of machine vision and computer vision technology in everyday life are becoming increasingly popular. Some techniques have been proposed to extract from the current depth of a 2D image of the feature, which defines a particular object or structure of the information. In many cases, these techniques are au...