Research on Disparity Map Generation Method of Underwater Target Based on the Improved SIFT Algorithm

2015 ◽  
Vol 741 ◽  
pp. 701-704 ◽  
Author(s):  
Di Wang ◽  
Yong Jie Pang
Keyword(s):  
Stereo Matching ◽  
Water Channel ◽  
Depth Information ◽  
Disparity Map ◽  
Sift Algorithm ◽  
Underwater Image ◽  
Map Generation ◽  
Curvature Coefficient ◽  
Bm Algorithm ◽  
Underwater Target

In order to obtain the depth information of the underwater target, it’s necessary to generate the disparity map based on binocular vision stereo matching. In the circulation water channel, the stereo matching experiments with underwater target were carried out by using the BM algorithm, SGBM algorithms and SIFT algorithm respectively. Then the characteristics of the disparity maps were analyzed for the three kinds of stereo matching algorithms. Compared with the BM algorithm and SGBM algorithms, the SIFT algorithm has been proved to be more suitable for underwater stereo matching. In order to obtain more feature points of underwater image, it is necessary to improved SIFT algorithm parameter. Underwater image matching experiments were made to determine the principal curvature coefficientγ. The results illustrated that the improvedγis better than the original value for underwater disparity map generation.

scholarly journals PDANet: Self-Supervised Monocular Depth Estimation Using Perceptual and Data Augmentation Consistency

2021 ◽  
Vol 11 (12) ◽  
pp. 5383
Author(s):  
Huachen Gao ◽  
Xiaoyu Liu ◽  
Meixia Qu ◽  
Shijie Huang
Keyword(s):  
Data Augmentation ◽  
State Of The Art ◽  
Depth Estimation ◽  
Input Image ◽  
Depth Information ◽  
Disparity Map ◽  
Estimation Model ◽  
Absolute Relative Error ◽  
Texture Region ◽  
Monocular Depth

In recent studies, self-supervised learning methods have been explored for monocular depth estimation. They minimize the reconstruction loss of images instead of depth information as a supervised signal. However, existing methods usually assume that the corresponding points in different views should have the same color, which leads to unreliable unsupervised signals and ultimately damages the reconstruction loss during the training. Meanwhile, in the low texture region, it is unable to predict the disparity value of pixels correctly because of the small number of extracted features. To solve the above issues, we propose a network—PDANet—that integrates perceptual consistency and data augmentation consistency, which are more reliable unsupervised signals, into a regular unsupervised depth estimation model. Specifically, we apply a reliable data augmentation mechanism to minimize the loss of the disparity map generated by the original image and the augmented image, respectively, which will enhance the robustness of the image in the prediction of color fluctuation. At the same time, we aggregate the features of different layers extracted by a pre-trained VGG16 network to explore the higher-level perceptual differences between the input image and the generated one. Ablation studies demonstrate the effectiveness of each components, and PDANet shows high-quality depth estimation results on the KITTI benchmark, which optimizes the state-of-the-art method from 0.114 to 0.084, measured by absolute relative error for depth estimation.

scholarly journals A Joint 2D-3D Complementary Network for Stereo Matching

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1430
Author(s):  
Xiaogang Jia ◽  
Wei Chen ◽  
Zhengfa Liang ◽  
Xin Luo ◽  
Mingfei Wu ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Computational Cost ◽  
Research Field ◽  
Disparity Map ◽  
Improve Performance ◽  
Cost Aggregation ◽  
Disparity Range ◽  
Public Datasets ◽  
Coarse To Fine ◽  
Speed And Accuracy

Stereo matching is an important research field of computer vision. Due to the dimension of cost aggregation, current neural network-based stereo methods are difficult to trade-off speed and accuracy. To this end, we integrate fast 2D stereo methods with accurate 3D networks to improve performance and reduce running time. We leverage a 2D encoder-decoder network to generate a rough disparity map and construct a disparity range to guide the 3D aggregation network, which can significantly improve the accuracy and reduce the computational cost. We use a stacked hourglass structure to refine the disparity from coarse to fine. We evaluated our method on three public datasets. According to the KITTI official website results, Our network can generate an accurate result in 80 ms on a modern GPU. Compared to other 2D stereo networks (AANet, DeepPruner, FADNet, etc.), our network has a big improvement in accuracy. Meanwhile, it is significantly faster than other 3D stereo networks (5× than PSMNet, 7.5× than CSN and 22.5× than GANet, etc.), demonstrating the effectiveness of our method.

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.

A Disparity Refinement in Stereo Matching based on Mean-shift Segmentation and Spatiotemporal Domain

2020 ◽  
Vol 64 (2) ◽  
pp. 20505-1-20505-12
Author(s):  
Hui-Yu Huang ◽  
Zhe-Hao Liu
Keyword(s):  
Stereo Matching ◽  
Mean Shift ◽  
Disparity Map ◽  
Video Streams ◽  
Segmentation Strategy ◽  
Information Matching ◽  
Color Similarity ◽  
Spatial Domains ◽  
Disparity Maps ◽  
The Cost

Abstract A stereo matching algorithm is used to find the best match between a pair of images. To compute the cost of the matching points from the sequence of images, the disparity maps from video streams are estimated. However, the estimated disparity sequences may cause undesirable flickering errors. These errors result in low visibility of the synthesized video and reduce video coding. In order to solve this problem, in this article, the authors propose a spatiotemporal disparity refinement on local stereo matching based on the segmentation strategy. Based on segmentation information, matching point searching, and color similarity, adaptive disparity values to recover the disparity errors in disparity sequences can be obtained. The flickering errors are also effectively removed, and the boundaries of objects are well preserved. The procedures of the proposed approach consist of a segmentation process, matching point searching, and refinement in the temporal and spatial domains. Experimental results verify that the proposed approach can yield a high quantitative evaluation and a high-quality disparity map compared with other methods.

Local Stereo Matching: An Adaptive Weighted Guided Image Filtering-Based Approach

2020 ◽  
pp. 2154010
Author(s):  
Ben Zhang ◽  
Denglin Zhu
Keyword(s):  
Stereo Matching ◽  
Stringent Response ◽  
Image Filtering ◽  
Absolute Difference ◽  
Map Building ◽  
Disparity Map ◽  
Vision Systems ◽  
Local Algorithms ◽  
Gradient Algorithms ◽  
Guided Image Filtering

Innovative applications in rapidly evolving domains such as robotic navigation and autonomous (driverless) vehicles rely on binocular computer vision systems that meet stringent response time and accuracy requirements. A key problem in these vision systems is stereo matching, which involves matching pixels from two input images in order to construct the output, a 3D map. Building upon the existing local stereo matching algorithms, this paper proposes a novel stereo matching algorithm that is based on a weighted guided filtering foundation. The proposed algorithm consists of three main steps; each step is designed with the goal of improving accuracy. First, the matching costs are computed using a unique combination of complementary methods (absolute difference, Census, and gradient algorithms) to reduce errors. Second, the costs are aggregated using an adaptive weighted guided image filtering method. Here, the regularization parameters are adjusted adaptively using the Canny method, further reducing errors. Third, a disparity map is generated using the winner-take-all strategy; this map is subsequently refined using a densification method to reduce errors. Our experimental results indicate that the proposed algorithm provides a higher level of accuracy in comparison to a collection of the existing state-of-the-art local algorithms.

scholarly journals Literature Survey on Stereo Vision Disparity Map Algorithms

2016 ◽  
Vol 2016 ◽  
pp. 1-23 ◽  
Author(s):  
Rostam Affendi Hamzah ◽  
Haidi Ibrahim
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Literature Survey ◽  
Processing Unit ◽  
Stereo Correspondence ◽  
Disparity Map ◽  
Central Processing ◽  
Field Programmable ◽  
Processing Module ◽  
Graphical Processing

This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.

scholarly journals Implementation of Stereo Vision Semi-Global Block Matching Methods for Distance Measurement

2018 ◽  
Vol 12 (2) ◽  
pp. 585 ◽  
Author(s):  
Raden Arief Setyawan ◽  
Rudy Sunoko ◽  
Mochammad Agus Choiron ◽  
Panca Mudji Rahardjo
Keyword(s):  
Stereo Vision ◽  
Stereo Matching ◽  
Distance Estimation ◽  
Distance Measurement ◽  
Block Matching ◽  
Disparity Map ◽  
Block Matching Algorithm ◽  
Object Distance ◽  
Left And Right ◽  
3D Movie

Stereo vision has become an attractive topic research in the last decades. Many implementations such as the autonomous car, 3D movie, 3D object generation, are produced using this technique. The advantages of using two cameras in stereo vision are the disparity map between images. Disparity map will produce distance estimation of the object. Distance measurement is a crucial parameter for an autonomous car. The distance between corresponding points between the left and right images must be precisely measured to get an accurate distance. One of the most challenging in stereo vision is to find corresponding points between left and right images (stereo matching). This paper proposed distance measurement using stereo vision using Semi-Global Block Matching algorithm for stereo matching purpose. The object is captured using a calibrated stereo camera. The images pair then optimized using WLS Filter to reduce noises. The implementation results of this algorithm are furthermore converted to a metric unit for distance measurement. The result shows that the stereo vision distance measurement using Semi-Global Block Matching gives a good result. The obtained best result of this work contains error of less than 1% for 1m distance

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