Improvement Of Local-Based Stereo Vision Disparity Map Estimation Algorithm

Stereo Vision Disparity Map (SVDM) estimation is one of the active research topics in computer vision. To improve the accuracy of SVDM is difficult and challenging. The accuracy is affected by the regions of edge discontinuities, occluded, repetitive pattern and low texture. Therefore, this thesis proposes an algorithm to handle more efficiently these challenges. Firstly, the proposed SVDM algorithm combines three matching cost features based on per pixel differences. The combination of Absolute Differences (AD) and Gradi- ent Matching (GM) features reduces the radiometric distortions. Then, both differences are combined with Census Transform (CN) feature to reduce the effect of illumination vari- ations. Secondly, this thesis also presents a new method of edge discontinuities handling which is known as iterative Guided Filter (iGF). This method is introduced to preserve and improve the object boundaries. Finally, the fill-in invalid disparity, undirected graph segmentation and plane fitting processes are utilized at the last stage in order to recover the occluded, repetitive and low texture regions on the SVDM. Based on the experimental results of standard benchmarking dataset from the Middlebury, the proposed algorithm is able to reduce 17.17% and 18.11% of all and nonocc errors, respectively, as compared to the algorithm without the proposed framework. Moreover, the proposed framework outperformed some of the state-of-the-arts algorithms in the literature

Literature Survey On Stereo Vision Disparity Map Algorithms

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

Improvement Of Stereo Matching Algorithm Based On Sum Of Gradient Magnitude Differences And Semi-Global Method With Refinement Step

A new stereo matching algorithm which uses improved matching cost computation and optimisation using the semi-global method (SGM) is proposed.The absolute difference is sensitive to low textured regions and high noise on the stereo images with radiometric distortions. To get over these problems,sum of gradient magnitude differences has been introduced at the first stage.This method is strong against the radio-metric differences on the stereo images.Hence,this approach will reduce the error of preliminary data for stereo corresponding process.The SGM is used at the aggregation,and optimisation stage uses 16 different directions of 2D path.Additionally,the iterative guided filter is utilised at the refinement stage which minimises the errors and increases the accuracy.The proposed work produces accurate results and performs much better compared with some established algorithms based on the standard stereo benchmarking evaluation from the Middlebury and KITTI

Stereo Matching Based On Absolute Differences For Multiple Objects Detection

This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detection using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods

Stereo matching based on absolute differences for multiple objects detection

This article presents a new algorithm for object detection using stereo camera system. The problem to get an accurate object detion using stereo camera is the imprecise of matching process between two scenes with the same viewpoint. Hence, this article aims to reduce the incorrect matching pixel with four stages. This new algorithm is the combination of continuous process of matching cost computation, aggregation, optimization and filtering. The first stage is matching cost computation to acquire preliminary result using an absolute differences method. Then the second stage known as aggregation step uses a guided filter with fixed window support size. After that, the optimization stage uses winner-takes-all (WTA) approach which selects the smallest matching differences value and normalized it to the disparity level. The last stage in the framework uses a bilateral filter. It is effectively further decrease the error on the disparity map which contains information of object detection and locations. The proposed work produces low errors (i.e., 12.11% and 14.01% nonocc and all errors) based on the KITTI dataset and capable to perform much better compared with before the proposed framework and competitive with some newly available methods

Development of stereo matching algorithm based on sum of absolute RGB color differences and gradient Matching

This paper proposes a new stereo matching algorithm which uses local-based method. The Sum of Absolute Differences (SAD) algorithm produces accurate result on the disparity map for the textured regions. However, this algorithm is sensitive to low texture areas and high noise on images with high different brightness and contrast. To get over these problems, the proposed algorithm utilizes SAD algorithm with RGB color channels differences and combination of gradient matching to improve the accuracy on the images with high brightness and contrast. Additionally, an edge-preserving filter is used at the second stage which is known as Bilateral Filter (BF). The BF filter is capable to work with the low texture areas and to reduce the noise and sharpen the images. Additionally, BF is strong  against the  distortions due to high brightness and contrast. The proposed work in this paper produces accurate results and performs much better compared with some established algorithms. This comparison is based on the standard quantitative measurements using the stereo benchmarking evaluation from the Middlebury

Visualization of Image Distortion on Camera Calibration for Stereo Vision Application

This paper presents a visualization of image distortion on camera calibration for stereo vision application. The 3D image plane in a group of target or image during the process of stereo pair calibration is also discussed. The extrinsic parameters of camera calibration can be viewed in 3D image or scene which contains the rotation and translation of vector. The error re-projection of a single image could determine the less error of distortion during the extraction of chessboard corner each image taken. The distortion model also generates an error coordinate system in pixel value. The 3D image will viewed the result and output of extrinsic parameters during the calibration process