scholarly journals IMPROVING SEMI-GLOBAL MATCHING: COST AGGREGATION AND CONFIDENCE MEASURE

2016 ◽  
Vol XLI-B1 ◽  
pp. 299-304 ◽  
Author(s):  
Pablo d’Angelo
Keyword(s):  
Belief Propagation ◽  
Stereo Pair ◽  
Confidence Measure ◽  
Remotely Sensed Imagery ◽  
Cost Aggregation ◽  
Digital Elevation ◽  
Dense Image ◽  
Global Matching ◽  
Matching Cost ◽  
Data Term

Digital elevation models are one of the basic products that can be generated from remotely sensed imagery. The Semi Global Matching (SGM) algorithm is a robust and practical algorithm for dense image matching. The connection between SGM and Belief Propagation was recently developed, and based on that improvements such as correction of over-counting the data term, and a new confidence measure have been proposed. Later the MGM algorithm has been proposed, it aims at improving the regularization step of SGM, but has only been evaluated on the Middlebury stereo benchmark so far. This paper evaluates these proposed improvements on the ISPRS satellite stereo benchmark, using a Pleiades Triplet and a Cartosat-1 Stereo pair. The over-counting correction slightly improves matching density, at the expense of adding a few outliers. The MGM cost aggregation shows leads to a slight increase of accuracy.

scholarly journals IMPROVING SEMI-GLOBAL MATCHING: COST AGGREGATION AND CONFIDENCE MEASURE

2016 ◽  
Vol XLI-B1 ◽  
pp. 299-304
Author(s):  
Pablo d’Angelo
Keyword(s):  
Belief Propagation ◽  
Stereo Pair ◽  
Confidence Measure ◽  
Remotely Sensed Imagery ◽  
Cost Aggregation ◽  
Digital Elevation ◽  
Dense Image ◽  
Global Matching ◽  
Matching Cost ◽  
Data Term

Digital elevation models are one of the basic products that can be generated from remotely sensed imagery. The Semi Global Matching (SGM) algorithm is a robust and practical algorithm for dense image matching. The connection between SGM and Belief Propagation was recently developed, and based on that improvements such as correction of over-counting the data term, and a new confidence measure have been proposed. Later the MGM algorithm has been proposed, it aims at improving the regularization step of SGM, but has only been evaluated on the Middlebury stereo benchmark so far. This paper evaluates these proposed improvements on the ISPRS satellite stereo benchmark, using a Pleiades Triplet and a Cartosat-1 Stereo pair. The over-counting correction slightly improves matching density, at the expense of adding a few outliers. The MGM cost aggregation shows leads to a slight increase of accuracy.

scholarly journals TEXTURE-AWARE DENSE IMAGE MATCHING USING TERNARY CENSUS TRANSFORM

2016 ◽  
Vol III-3 ◽  
pp. 59-66 ◽  
Author(s):  
Han Hu ◽  
Chongtai Chen ◽  
Bo Wu ◽  
Xiaoxia Yang ◽  
Qing Zhu ◽  
...  
Keyword(s):  
Image Matching ◽  
Urban Areas ◽  
Point Clouds ◽  
Parameter Tuning ◽  
Census Transform ◽  
Urban Scenes ◽  
Dense Image ◽  
Global Matching ◽  
Matching Cost ◽  
Sharp Edges

Textureless and geometric discontinuities are major problems in state-of-the-art dense image matching methods, as they can cause visually significant noise and the loss of sharp features. Binary census transform is one of the best matching cost methods but in textureless areas, where the intensity values are similar, it suffers from small random noises. Global optimization for disparity computation is inherently sensitive to parameter tuning in complex urban scenes, and must compromise between smoothness and discontinuities. The aim of this study is to provide a method to overcome these issues in dense image matching, by extending the industry proven Semi-Global Matching through 1) developing a ternary census transform, which takes three outputs in a single order comparison and encodes the results in two bits rather than one, and also 2) by using texture-information to self-tune the parameters, which both preserves sharp edges and enforces smoothness when necessary. Experimental results using various datasets from different platforms have shown that the visual qualities of the triangulated point clouds in urban areas can be largely improved by these proposed methods.

scholarly journals SEMI-GLOBAL MATCHING WITH SELF-ADJUSTING PENALTIES

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 353-360
Author(s):  
E. Karkalou ◽  
C. Stentoumis ◽  
G. Karras
Keyword(s):  
Computational Efficiency ◽  
Image Matching ◽  
3D Models ◽  
Stereo Pair ◽  
Dense Image ◽  
Wide Range ◽  
Global Matching ◽  
Active Research ◽  
Disparity Space Image ◽  
Disparity Space

The demand for 3D models of various scales and precisions is strong for a wide range of applications, among which cultural heritage recording is particularly important and challenging. In this context, dense image matching is a fundamental task for processes which involve image-based reconstruction of 3D models. Despite the existence of commercial software, the need for complete and accurate results under different conditions, as well as for computational efficiency under a variety of hardware, has kept image-matching algorithms as one of the most active research topics. Semi-global matching (SGM) is among the most popular optimization algorithms due to its accuracy, computational efficiency, and simplicity. A challenging aspect in SGM implementation is the determination of smoothness constraints, i.e. penalties P1, P2 for disparity changes and discontinuities. In fact, penalty adjustment is needed for every particular stereo-pair and cost computation. In this work, a novel formulation of <i>self-adjusting penalties</i> is proposed: <i>SGM penalties can be estimated solely from the statistical properties of the initial disparity space image</i>. The proposed method of self-adjusting penalties (SGM-SAP) is evaluated using typical cost functions on stereo-pairs from the recent Middlebury dataset of interior scenes, as well as from the EPFL Herz-Jesu architectural scenes. Results are competitive against the original SGM estimates. The significant aspects of self-adjusting penalties are: (i) the time-consuming tuning process is avoided; (ii) SGM can be used in image collections with limited number of stereo-pairs; and (iii) no heuristic user intervention is needed.

scholarly journals TEXTURE-AWARE DENSE IMAGE MATCHING USING TERNARY CENSUS TRANSFORM

2016 ◽  
Vol III-3 ◽  
pp. 59-66
Author(s):  
Han Hu ◽  
Chongtai Chen ◽  
Bo Wu ◽  
Xiaoxia Yang ◽  
Qing Zhu ◽  
...  
Keyword(s):  
Image Matching ◽  
Urban Areas ◽  
Point Clouds ◽  
Parameter Tuning ◽  
Census Transform ◽  
Urban Scenes ◽  
Dense Image ◽  
Global Matching ◽  
Matching Cost ◽  
Sharp Edges

Textureless and geometric discontinuities are major problems in state-of-the-art dense image matching methods, as they can cause visually significant noise and the loss of sharp features. Binary census transform is one of the best matching cost methods but in textureless areas, where the intensity values are similar, it suffers from small random noises. Global optimization for disparity computation is inherently sensitive to parameter tuning in complex urban scenes, and must compromise between smoothness and discontinuities. The aim of this study is to provide a method to overcome these issues in dense image matching, by extending the industry proven Semi-Global Matching through 1) developing a ternary census transform, which takes three outputs in a single order comparison and encodes the results in two bits rather than one, and also 2) by using texture-information to self-tune the parameters, which both preserves sharp edges and enforces smoothness when necessary. Experimental results using various datasets from different platforms have shown that the visual qualities of the triangulated point clouds in urban areas can be largely improved by these proposed methods.

scholarly journals REDUCTION OF THE FRONTO-PARALLEL BIAS FOR WIDE-BASELINE SEMI-GLOBAL MATCHING

2019 ◽  
Vol IV-2/W5 ◽  
pp. 69-76 ◽  
Author(s):  
L. Roth ◽  
H. Mayer
Keyword(s):  
Image Matching ◽  
Synthetic Data ◽  
Real Data ◽  
Image Warping ◽  
Disparity Map ◽  
Dense Image ◽  
Global Matching ◽  
Disparity Maps ◽  
Smoothness Term ◽  
Data Term

<p><strong>Abstract.</strong> Semi-Global Matching (SGM) is a widely-used technique for dense image matching that is popular because of its accuracy and speed. While it works well for textured scenes, it can fail on slanted surfaces particularly in wide-baseline configurations due to the so-called fronto-parallel bias. In this paper, we propose an extension of SGM that utilizes image warping to reduce the fronto-parallel bias in the data term, based on estimating dominant slanted planes. The latter are also used as surface priors improving the smoothness term. Our proposed method calculates disparity maps for each dominant slanted plane and fuses them to obtain the final disparity map. We have quantitatively evaluated our approach outperforming SGM and SGM-P on synthetic data and demonstrate its potential on real data by qualitative results. In this way, we underscore the need to tackle the fronto-parallel bias in particular for wide-baseline configurations in both the data term and the smoothness term of SGM.</p>

scholarly journals A comparison of semiglobal and local dense matching algorithms for surface reconstruction

2014 ◽  
Vol XL-5 ◽  
pp. 187-194 ◽  
Author(s):  
E. Dall'Asta ◽  
R. Roncella
Keyword(s):  
Computer Vision ◽  
Stereo Matching ◽  
Least Square ◽  
Stereo Image ◽  
Dense Matching ◽  
Consistency Constraints ◽  
Global Matching ◽  
Matching Cost ◽  
Software Codes ◽  
Matching Techniques

Encouraged by the growing interest in automatic 3D image-based reconstruction, the development and improvement of robust stereo matching techniques is one of the most investigated research topic of the last years in photogrammetry and computer vision.<br><br> The paper is focused on the comparison of some stereo matching algorithms (local and global) which are very popular both in photogrammetry and computer vision. In particular, the Semi-Global Matching (SGM), which realizes a pixel-wise matching and relies on the application of consistency constraints during the matching cost aggregation, will be discussed.<br><br> The results of some tests performed on real and simulated stereo image datasets, evaluating in particular the accuracy of the obtained digital surface models, will be presented. Several algorithms and different implementation are considered in the comparison, using freeware software codes like MICMAC and OpenCV, commercial software (e.g. Agisoft PhotoScan) and proprietary codes implementing Least Square e Semi-Global Matching algorithms. The comparisons will also consider the completeness and the level of detail within fine structures, and the reliability and repeatability of the obtainable data.

scholarly journals AN INTEGRATED PHOTOGRAMMETRIC AND PHOTOCLINOMETRIC APPROACH FOR PIXEL-RESOLUTION 3D MODELLING OF LUNAR SURFACE

2018 ◽  
Vol XLII-3 ◽  
pp. 1117-1122
Author(s):  
W. C. Liu ◽  
B. Wu
Keyword(s):  
High Resolution ◽  
Lunar Surface ◽  
Image Matching ◽  
Point Cloud ◽  
3D Modelling ◽  
Conjugate Points ◽  
Stereo Pair ◽  
Pixel Resolution ◽  
Dense Image ◽  
Lunar Reconnaissance Orbiter

High-resolution 3D modelling of lunar surface is important for lunar scientific research and exploration missions. Photogrammetry is known for 3D mapping and modelling from a pair of stereo images based on dense image matching. However dense matching may fail in poorly textured areas and in situations when the image pair has large illumination differences. As a result, the actual achievable spatial resolution of the 3D model from photogrammetry is limited by the performance of dense image matching. On the other hand, photoclinometry (i.e., shape from shading) is characterised by its ability to recover pixel-wise surface shapes based on image intensity and imaging conditions such as illumination and viewing directions. More robust shape reconstruction through photoclinometry can be achieved by incorporating images acquired under different illumination conditions (i.e., photometric stereo). Introducing photoclinometry into photogrammetric processing can therefore effectively increase the achievable resolution of the mapping result while maintaining its overall accuracy. This research presents an integrated photogrammetric and photoclinometric approach for pixel-resolution 3D modelling of the lunar surface. First, photoclinometry is interacted with stereo image matching to create robust and spatially well distributed dense conjugate points. Then, based on the 3D point cloud derived from photogrammetric processing of the dense conjugate points, photoclinometry is further introduced to derive the 3D positions of the unmatched points and to refine the final point cloud. The approach is able to produce one 3D point for each image pixel within the overlapping area of the stereo pair so that to obtain pixel-resolution 3D models. Experiments using the Lunar Reconnaissance Orbiter Camera - Narrow Angle Camera (LROC NAC) images show the superior performances of the approach compared with traditional photogrammetric technique. The results and findings from this research contribute to optimal exploitation of image information for high-resolution 3D modelling of the lunar surface, which is of significance for the advancement of lunar and planetary mapping.

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