COMPUTATION AND PERFORMANCE ANALYSIS OF DOUBLE STAGE FILTER FOR IMAGE PROCESSING

2015 ◽  
Vol 77 (19) ◽  
Author(s):  
Teo Chee Huat ◽  
Nurulfajar Abdul Manap ◽  
Masrullizam Mat Ibrahim
Keyword(s):  
Stereo Matching ◽  
Ground Truth ◽  
Block Matching ◽  
Basic Block ◽  
Median Filtering ◽  
Disparity Map ◽  
Evaluation Functions ◽  
Map Segmentation ◽  
Disparity Maps ◽  
And Performance

Double Stage Filter (DSF) is a hybrid stereo matching algorithm which consists of basic block matching and dynamic programming algorithms, basic median filtering and new technique of segmentation. The algorithm acquire disparity maps which will be analyzed by using evaluation functions such as PSNR, MSE and SSIM. The computation of DSF and existing algorithms are presented in this paper. The Phase 2 in DSF is to remove the unwanted aspects such as depth discontinuities and holes from occlusion from the raw disparity map. Segmentation, merging and median filtering are the major parts for post processing of DSF algorithm. From the results of evaluation functions, the disparity maps attained by DSF is closer to the ground truth compared to other algorithms while its computation takes only few seconds longer than DP algorithm but its capable to obtain better results of disparity map.

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.

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

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 HYBRID-BASED DENSE STEREO MATCHING

2016 ◽  
Vol XLI-B3 ◽  
pp. 495-501 ◽  
Author(s):  
T. Y. Chuang ◽  
H. W. Ting ◽  
J. J. Jaw
Keyword(s):  
Stereo Matching ◽  
Penalty Parameter ◽  
Disparity Map ◽  
Cost Aggregation ◽  
Satisfactory Performance ◽  
Dense Stereo Matching ◽  
Illumination Changes ◽  
Global Matching ◽  
Disparity Maps ◽  
Image Edges

Stereo matching generating accurate and dense disparity maps is an indispensable technique for 3D exploitation of imagery in the fields of Computer vision and Photogrammetry. Although numerous solutions and advances have been proposed in the literature, occlusions, disparity discontinuities, sparse texture, image distortion, and illumination changes still lead to problematic issues and await better treatment. In this paper, a hybrid-based method based on semi-global matching is presented to tackle the challenges on dense stereo matching. To ease the sensitiveness of SGM cost aggregation towards penalty parameters, a formal way to provide proper penalty estimates is proposed. To this end, the study manipulates a shape-adaptive cross-based matching with an edge constraint to generate an initial disparity map for penalty estimation. Image edges, indicating the potential locations of occlusions as well as disparity discontinuities, are approved by the edge drawing algorithm to ensure the local support regions not to cover significant disparity changes. Besides, an additional penalty parameter 𝑃𝑒 is imposed onto the energy function of SGM cost aggregation to specifically handle edge pixels. Furthermore, the final disparities of edge pixels are found by weighting both values derived from the SGM cost aggregation and the U-SURF matching, providing more reliable estimates at disparity discontinuity areas. Evaluations on Middlebury stereo benchmarks demonstrate satisfactory performance and reveal the potency of the hybrid-based dense stereo matching method.

scholarly journals HYBRID-BASED DENSE STEREO MATCHING

2016 ◽  
Vol XLI-B3 ◽  
pp. 495-501
Author(s):  
T. Y. Chuang ◽  
H. W. Ting ◽  
J. J. Jaw
Keyword(s):  
Stereo Matching ◽  
Penalty Parameter ◽  
Disparity Map ◽  
Cost Aggregation ◽  
Satisfactory Performance ◽  
Dense Stereo Matching ◽  
Illumination Changes ◽  
Global Matching ◽  
Disparity Maps ◽  
Image Edges

Stereo matching generating accurate and dense disparity maps is an indispensable technique for 3D exploitation of imagery in the fields of Computer vision and Photogrammetry. Although numerous solutions and advances have been proposed in the literature, occlusions, disparity discontinuities, sparse texture, image distortion, and illumination changes still lead to problematic issues and await better treatment. In this paper, a hybrid-based method based on semi-global matching is presented to tackle the challenges on dense stereo matching. To ease the sensitiveness of SGM cost aggregation towards penalty parameters, a formal way to provide proper penalty estimates is proposed. To this end, the study manipulates a shape-adaptive cross-based matching with an edge constraint to generate an initial disparity map for penalty estimation. Image edges, indicating the potential locations of occlusions as well as disparity discontinuities, are approved by the edge drawing algorithm to ensure the local support regions not to cover significant disparity changes. Besides, an additional penalty parameter 𝑃𝑒 is imposed onto the energy function of SGM cost aggregation to specifically handle edge pixels. Furthermore, the final disparities of edge pixels are found by weighting both values derived from the SGM cost aggregation and the U-SURF matching, providing more reliable estimates at disparity discontinuity areas. Evaluations on Middlebury stereo benchmarks demonstrate satisfactory performance and reveal the potency of the hybrid-based dense stereo matching method.

scholarly journals Application of morphological processing methods in computer stereo vision

2021 ◽  
Vol 6 (131) ◽  
pp. 18-27
Author(s):  
Oleh Prokopchuk ◽  
Serhii Vovk
Keyword(s):  
Mathematical Morphology ◽  
Stereo Vision ◽  
Real World ◽  
Stereo Matching ◽  
Morphological Processing ◽  
Morphological Operations ◽  
Disparity Map ◽  
Real Objects ◽  
Disparity Maps ◽  
The Impact

Computer vision algorithms are important for many areas of human activity. In particular, the number of applications related to the need to process images of real-world objects with computerized tools and the subsequent use of descriptive information in a variety of interactive and automated decision-making systems is increased. An important tool for analyzing real-world scenes are approaches to the application of stereo vision algorithms. The important step of many stereo matching algorithms is a disparity map. Depending on the content of the observed scene, part of the values on the disparity map can be immediately attributed to background values on a certain basis, or form a "natural" background, which is characterized by loss of informative data due to unacceptable error of subsequent resultant distance values. The calculated disparity map of any algorithm may contain some shortcomings in the form of discontinuities of continuous information areas caused by the complexity of shooting conditions, the impact of noise of various natures, hardware imperfections, and so on. An approach to mitigating the undesirable influence of negative factors on the resulting disparity is the use of mathematical morphology operations to process disparity maps at the post-processing stage. This paper presents information technology for increasing the content of disparity maps based on the mathematical morphology methods. The technology is based on a combination of morphological operations of erosion and dilation, which eliminates the typical problems of discontinuities of monotone regions and erroneous values on disparity maps. The proposed approach allows reducing the impact of common problems that arise during the operation of stereo matching algorithms, as well as increase the overall informativeness of disparity maps for images of real objects in the absence of partial or complete initial data on the characteristics of the observed scene. The results of testing morphological operations with disparity maps for real objects allow us to conclude about the possibility of partial restoration of areas of disparity maps with gaps in continuous information areas, as well as to reduce the impact of random anomalous values on the overall content of the disparity maps.

An unsupervised stereo matching cost based on sparse representation

2020 ◽  
pp. 2050060
Author(s):  
Chunbo Cheng ◽  
Hong Li ◽  
Liming Zhang
Keyword(s):  
Sparse Representation ◽  
Stereo Matching ◽  
Video Clip ◽  
Ground Truth ◽  
Model Parameters ◽  
Parallel Method ◽  
Monocular Video ◽  
Matching Cost ◽  
Disparity Maps ◽  
Public Datasets

Supervised stereo matching costs need to learn model parameters from public datasets with ground truth disparity maps. However, it is not so easy to obtain the ground truth disparity maps, thus making the supervised stereo matching costs difficult to apply in practice. This paper proposes an unsupervised stereo matching cost based on sparse representation (USMCSR). This method does not rely on the ground truth disparity maps, besides, it also can reduce the effects of the illumination and exposure changes, thus making it suitable for measuring similarity between pixels in stereo matching. In order to achieve higher computational efficiency, we further propose an efficient parallel method for solving sparse representation coefficients. The extended experimental results on three commonly used datasets demonstrate the effectiveness of the proposed method. Finally, the verification results on the monocular video clip show the USMCSR can also work well without ground truth disparity maps.

Disparity Map Acquisition Based on Matlab Calibration Toolbox and OpenCV Stereo Matching Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 3030-3033 ◽  
Author(s):  
Wei Gu ◽  
Jing Yin ◽  
Xiao Fang Yang ◽  
Pu Liu
Keyword(s):  
Information Acquisition ◽  
Stereo Matching ◽  
Block Matching ◽  
3D Measurement ◽  
Image Correction ◽  
Disparity Map ◽  
Matching Algorithm ◽  
3D Data ◽  
Difficult Issue

The key and difficult issue in the research of binocular vision-based 3D measurement is the accurate calibration of internal and external parameters of the camera and stereo matching. Matlab calibration is more efficient and accurate compared with manual or OpenCV calibration. In this paper, binocular camera is calibrated by Matlab calibration toolbox, and calibration parameters imported in OpenCV for follow-up image correction and stereo matching. By studying and comparing Block Matching (BM) and Graph Cut (GC) stereo matching algorithms, a disparity image of the object is obtained, thus laying foundation for follow-up 3D data information acquisition and reconstruction.

scholarly journals Learned Collaborative Stereo Refinement

2021 ◽  
Author(s):  
Patrick Knöbelreiter ◽  
Thomas Pock
Keyword(s):  
Structural Properties ◽  
Gradient Method ◽  
Color Image ◽  
Image Space ◽  
Activation Functions ◽  
Disparity Map ◽  
Proximal Gradient Method ◽  
Variational Structure ◽  
Disparity Maps ◽  
The Individual

AbstractIn this work, we propose a learning-based method to denoise and refine disparity maps. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. To this end, we can visualize and interpret the learned filters and activation functions and prove the increased reliability of the predicted pixel-wise confidence maps. Furthermore, the optimization based structure of our refinement module allows us to compute eigen disparity maps, which reveal structural properties of our refinement module. The efficiency of our method is demonstrated on the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.

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