scholarly journals Disparity estimation using Graph cuts for road applications

2021 ◽  
Vol 297 ◽  
pp. 01055
Author(s):  
Mohamed El Ansari ◽  
Ilyas El Jaafari ◽  
Lahcen Koutti
Keyword(s):  
Stereo Matching ◽  
Optimal Solution ◽  
Similarity Criterion ◽  
Graph Cuts ◽  
Search Space ◽  
Disparity Estimation ◽  
Stereo Images ◽  
Disparity Map ◽  
Current Frame ◽  
Edge Points

This paper proposes a new edge based stereo matching approach for road applications. The new approach consists in matching the edge points extracted from the input stereo images using temporal constraints. At the current frame, we propose to estimate a disparity range for each image line based on the disparity map of its preceding one. The stereo images are divided into multiple parts according to the estimated disparity ranges. The optimal solution of each part is independently approximated via the state-of-the-art energy minimization approach Graph cuts. The disparity search space at each image part is very small compared to the global one, which improves the results and reduces the execution time. Furthermore, as a similarity criterion between corresponding edge points, we propose a new cost function based on the intensity, the gradient magnitude and gradient orientation. The proposed method has been tested on virtual stereo images, and it has been compared to a recently proposed method and the results are satisfactory.

scholarly journals Stereo Matching Algorithm Based on 2D Delaunay Triangulation

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Xue-he Zhang ◽  
Ge Li ◽  
Chang-le Li ◽  
He Zhang ◽  
Jie Zhao ◽  
...  
Keyword(s):  
Delaunay Triangulation ◽  
Stereo Matching ◽  
Robot Vision ◽  
Depth Estimation ◽  
Planning System ◽  
Disparity Map ◽  
Matching Method ◽  
Running Speed ◽  
Edge Operator ◽  
Edge Points

To fulfill the applications on robot vision, the commonly used stereo matching method for depth estimation is supposed to be efficient in terms of running speed and disparity accuracy. Based on this requirement, Delaunay-based stereo matching method is proposed to achieve the aforementioned standards in this paper. First, a Canny edge operator is used to detect the edge points of an image as supporting points. Those points are then processed using a Delaunay triangulation algorithm to divide the whole image into a series of linked triangular facets. A proposed module composed of these facets performs a rude estimation of image disparity. According to the triangular property of shared vertices, the estimated disparity is then refined to generate the disparity map. The method is tested on Middlebury stereo pairs. The running time of the proposed method is about 1 s and the matching accuracy is 93%. Experimental results show that the proposed method improves both running speed and disparity accuracy, which forms a steady foundation and good application prospect for a robot’s path planning system with stereo camera devices.

The Implementation of Parallel Speed up Stereo Matching Based on Reduced Graph Cuts

2014 ◽  
Vol 631-632 ◽  
pp. 486-489
Author(s):  
Ping Zhou ◽  
Mei Liu
Keyword(s):  
Stereo Matching ◽  
Optimal Solution ◽  
Maximum Flow ◽  
Graph Cuts ◽  
Network Graph ◽  
Time And Space ◽  
Matching Problems ◽  
Speed Up ◽  
Set Up ◽  
The Many

The Many stereo matching problems can be converted to energy minimization problem, by establishment of special network graph to obtain the minimum graph cut, and then obtaining the optimal solution. For graph cuts algorithm, complete network graph include all vertices and disparity edges, the computation of time and space is huge. In this paper, we put forward a method by combing local and global stereo matching, set up a reduced network graph by the possible disparities values for each pixel, and then global optimization, to slove the maximum flow polynomial through CUDA parallel computing, greatly reduced the consumption of time and space.

scholarly journals A Survey of Learning Approaches and Application for 3D Vision

2018 ◽  
Vol 173 ◽  
pp. 03053
Author(s):  
Luanhao Lu
Keyword(s):  
Stereo Matching ◽  
Mean Shift ◽  
Three Dimensional ◽  
Image Synthesis ◽  
Learning Approaches ◽  
Median Filtering ◽  
Stereo Images ◽  
Disparity Map ◽  
3D Vision ◽  
2D Images

Three-dimensional (3D) vision extracted from the stereo images or reconstructed from the two-dimensional (2D) images is the most effective topic in computer vision and video surveillance. Three-dimensional scene is constructed through two stereo images which existing disparity map by Stereo vision. Many methods of Stereo matching which contains median filtering, mean-shift segmentation, guided filter and joint trilateral filters [1] are used in many algorithms to construct the precise disparity map. These methods committed to figure out the image synthesis range in different Stereo matching fields and among these techniques cannot perform perfectly every turn. The paper focuses on 3D vision, introduce the background and process of 3D vision, reviews several classical datasets in the field of 3D vision, based on which the learning approaches and several types of applications of 3D vision were evaluated and analyzed.

scholarly journals MDEAN: Multi-View Disparity Estimation with an Asymmetric Network

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 924 ◽  
Author(s):  
Zhao Pei ◽  
Deqiang Wen ◽  
Yanning Zhang ◽  
Miao Ma ◽  
Min Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Stereo Matching ◽  
Estimation Method ◽  
Disparity Estimation ◽  
Estimation Methods ◽  
Disparity Map ◽  
Plane Sweep ◽  
Flat Surfaces ◽  
Outdoor Scenes ◽  
Traditional Image

In recent years, disparity estimation of a scene based on deep learning methods has been extensively studied and significant progress has been made. In contrast, a traditional image disparity estimation method requires considerable resources and consumes much time in processes such as stereo matching and 3D reconstruction. At present, most deep learning based disparity estimation methods focus on estimating disparity based on monocular images. Motivated by the results of traditional methods that multi-view methods are more accurate than monocular methods, especially for scenes that are textureless and have thin structures, in this paper, we present MDEAN, a new deep convolutional neural network to estimate disparity using multi-view images with an asymmetric encoder–decoder network structure. First, our method takes an arbitrary number of multi-view images as input. Next, we use these images to produce a set of plane-sweep cost volumes, which are combined to compute a high quality disparity map using an end-to-end asymmetric network. The results show that our method performs better than state-of-the-art methods, in particular, for outdoor scenes with the sky, flat surfaces and buildings.

scholarly journals Disparity Map Generation from Illumination Variant Stereo Images Using Efficient Hierarchical Dynamic Programming

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Viral H. Borisagar ◽  
Mukesh A. Zaveri
Keyword(s):  
Dynamic Programming ◽  
Stereo Matching ◽  
Stereo Image ◽  
Stereo Pair ◽  
Image Pair ◽  
Stereo Images ◽  
Disparity Map ◽  
Illumination Variation ◽  
Matching Problem ◽  
Wide Range

A novel hierarchical stereo matching algorithm is presented which gives disparity map as output from illumination variant stereo pair. Illumination difference between two stereo images can lead to undesirable output. Stereo image pair often experience illumination variations due to many factors like real and practical situation, spatially and temporally separated camera positions, environmental illumination fluctuation, and the change in the strength or position of the light sources. Window matching and dynamic programming techniques are employed for disparity map estimation. Good quality disparity map is obtained with the optimized path. Homomorphic filtering is used as a preprocessing step to lessen illumination variation between the stereo images. Anisotropic diffusion is used to refine disparity map to give high quality disparity map as a final output. The robust performance of the proposed approach is suitable for real life circumstances where there will be always illumination variation between the images. The matching is carried out in a sequence of images representing the same scene, however in different resolutions. The hierarchical approach adopted decreases the computation time of the stereo matching problem. This algorithm can be helpful in applications like robot navigation, extraction of information from aerial surveys, 3D scene reconstruction, and military and security applications. Similarity measure SAD is often sensitive to illumination variation. It produces unacceptable disparity map results for illumination variant left and right images. Experimental results show that our proposed algorithm produces quality disparity maps for both wide range of illumination variant and invariant stereo image pair.

scholarly journals Depth Estimation for Light-Field Images Using Stereo Matching and Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6188
Author(s):  
Ségolène Rogge ◽  
Ionut Schiopu ◽  
Adrian Munteanu
Keyword(s):  
Machine Learning ◽  
Light Field ◽  
Stereo Matching ◽  
Estimation Method ◽  
Similarity Index ◽  
Depth Estimation ◽  
Residual Error ◽  
Disparity Estimation ◽  
Machine Learning Techniques ◽  
Disparity Map

The paper presents a novel depth-estimation method for light-field (LF) images based on innovative multi-stereo matching and machine-learning techniques. In the first stage, a novel block-based stereo matching algorithm is employed to compute the initial estimation. The proposed algorithm is specifically designed to operate on any pair of sub-aperture images (SAIs) in the LF image and to compute the pair’s corresponding disparity map. For the central SAI, a disparity fusion technique is proposed to compute the initial disparity map based on all available pairwise disparities. In the second stage, a novel pixel-wise deep-learning (DL)-based method for residual error prediction is employed to further refine the disparity estimation. A novel neural network architecture is proposed based on a new structure of layers. The proposed DL-based method is employed to predict the residual error of the initial estimation and to refine the final disparity map. The experimental results demonstrate the superiority of the proposed framework and reveal that the proposed method achieves an average improvement of 15.65% in root mean squared error (RMSE), 43.62% in mean absolute error (MAE), and 5.03% in structural similarity index (SSIM) over machine-learning-based state-of-the-art methods.

scholarly journals THE EFFECT OF SHADOW AREA ON SGM ALGORITHM AND DISPARITY MAP REFINEMENT FROM HIGH RESOLUTION SATELLITE STEREO IMAGES

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 281-286
Author(s):  
N. Tatar ◽  
M. Saadatseresht ◽  
H. Arefi
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
High Performance ◽  
Stereo Matching ◽  
Image Data ◽  
Stereo Pair ◽  
Stereo Images ◽  
Disparity Map ◽  
Shadow Area ◽  
Global Matching

Semi Global Matching (SGM) algorithm is known as a high performance and reliable stereo matching algorithm in photogrammetry community. However, there are some challenges using this algorithm especially for high resolution satellite stereo images over urban areas and images with shadow areas. As it can be seen, unfortunately the SGM algorithm computes highly noisy disparity values for shadow areas around the tall neighborhood buildings due to mismatching in these lower entropy areas. In this paper, a new method is developed to refine the disparity map in shadow areas. The method is based on the integration of potential of panchromatic and multispectral image data to detect shadow areas in object level. In addition, a RANSAC plane fitting and morphological filtering are employed to refine the disparity map. The results on a stereo pair of GeoEye-1 captured over Qom city in Iran, shows a significant increase in the rate of matched pixels compared to standard SGM algorithm.

scholarly journals A stereo matching algorithm based on the improved PSMNet

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0251657
Author(s):  
Zedong Huang ◽  
Jinan Gu ◽  
Jing Li ◽  
Xuefei Yu
Keyword(s):  
Feature Extraction ◽  
Stereo Matching ◽  
Feature Fusion ◽  
Disparity Estimation ◽  
Disparity Map ◽  
Matching Network ◽  
Matching Effect ◽  
Matching Cost ◽  
Feature Information ◽  
3D Cnn

Deep learning based on a convolutional neural network (CNN) has been successfully applied to stereo matching. Compared with the traditional method, the speed and accuracy of this method have been greatly improved. However, the existing stereo matching framework based on a CNN often encounters two problems. First, the existing stereo matching network has many parameters, which leads to the matching running time being too long. Second, the disparity estimation is inadequate in some regions where reflections, repeated textures, and fine structures may lead to ill-posed problems. Through the lightweight improvement of the PSMNet (Pyramid Stereo Matching Network) model, the common matching effect of ill-conditioned areas such as repeated texture areas and weak texture areas is solved. In the feature extraction part, ResNeXt is introduced to learn unitary feature extraction, and the ASPP (Atrous Spatial Pyramid Pooling) module is trained to extract multiscale spatial feature information. The feature fusion module is designed to effectively fuse the feature information of different scales to construct the matching cost volume. The improved 3D CNN uses the stacked encoding and decoding structure to further regularize the matching cost volume and obtain the corresponding relationship between feature points under different parallax conditions. Finally, the disparity map is obtained by a regression. We evaluate our method on the Scene Flow, KITTI 2012, and KITTI 2015 stereo datasets. The experiments show that the proposed stereo matching network achieves a comparable prediction accuracy and much faster running speed compared with PSMNet.

scholarly journals Fast global stereo matching via energy pyramid minimization

2014 ◽  
Vol II-3 ◽  
pp. 41-48
Author(s):  
B. Conejo ◽  
S. Leprince ◽  
F. Ayoub ◽  
J. P. Avouac
Keyword(s):  
Random Fields ◽  
Stereo Matching ◽  
Conditional Random Fields ◽  
Optimal Solution ◽  
Stereo Pair ◽  
Optimization Approach ◽  
Disparity Map ◽  
Image Pyramids ◽  
Global Matching ◽  
Coarse To Fine

We define a global matching framework based on energy pyramid, the Global Matching via Energy Pyramid (GM-EP) algorithm, which estimates the disparity map from a single stereo-pair by solving an energy minimization problem. We efficiently address this minimization by globally optimizing a coarse to fine sequence of sparse Conditional Random Fields (CRF) directly defined on the energy. This global discrete optimization approach guarantees that at each scale we obtain a near optimal solution, and we demonstrate its superiority over state of the art image pyramid approaches through application to real stereo-pairs. We conclude that multiscale approaches should be build on energy pyramids rather than on image pyramids.

scholarly journals Accurate hierarchical stereo matching based on 3D plane labeling of superpixel for stereo images from rovers

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142110021
Author(s):  
Haichao Li ◽  
Zhi Li ◽  
Jianbin Huang ◽  
Bo Meng ◽  
Zhimin Zhang
Keyword(s):  
Large Scale ◽  
Stereo Matching ◽  
State Of The Art ◽  
The State ◽  
Normal Vector ◽  
Stereo Images ◽  
Disparity Map ◽  
Matching Method ◽  
Search Range ◽  
Coarse To Fine

An accurate hierarchical stereo matching method is proposed based on continuous 3D plane labeling of superpixel for rover’s stereo images. This method can infer the 3D plane label of each pixel combined with the slanted-patch matching strategy and coarse-to-fine constraints, which is especially suitable for large-scale scene matching with low-texture or textureless regions. At every level, the stereo matching method based on superpixel segmentation makes the iteration convergence faster and avoids huge redundant computations. In the coarse-to-fine matching scheme, we propose disparity constraint and 3D normal vector constraint between adjacent levels through which the disparity map and 3D normal vector map at a coarser level are used to restrict the search range of disparity and normal vector at a fine level. The experimental results with the Chang’e-3 rover dataset and the KITTI dataset show that the proposed stereo matching method is efficiently and accurately compared with the state-of-the-art 3D labeling algorithm, especially in low-texture or textureless regions. The computational efficiency of this method is about five to six times faster than the state-of-the-art 3D labeling method, and the accuracy is better.

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