scholarly journals Multi-Label Learning based Semi-Global Matching Forest

2020 ◽  
Vol 12 (7) ◽  
pp. 1069
Author(s):  
Yuanxin Xia ◽  
Pablo d’Angelo ◽  
Jiaojiao Tian ◽  
Friedrich Fraundorfer ◽  
Peter Reinartz
Keyword(s):  
Random Forest ◽  
Stereo Matching ◽  
Training Sample ◽  
Disparity Estimation ◽  
Good Prediction ◽  
Dense Matching ◽  
Learning Procedure ◽  
Global Matching ◽  
Good Trade ◽  
Label Strategy

Semi-Global Matching (SGM) approximates a 2D Markov Random Field (MRF) via multiple 1D scanline optimizations, which serves as a good trade-off between accuracy and efficiency in dense matching. Nevertheless, the performance is limited due to the simple summation of the aggregated costs from all 1D scanline optimizations for the final disparity estimation. SGM-Forest improves the performance of SGM by training a random forest to predict the best scanline according to each scanline’s disparity proposal. The disparity estimated by the best scanline acts as reference to adaptively adopt close proposals for further post-processing. However, in many cases more than one scanline is capable of providing a good prediction. Training the random forest with only one scanline labeled may limit or even confuse the learning procedure when other scanlines can offer similar contributions. In this paper, we propose a multi-label classification strategy to further improve SGM-Forest. Each training sample is allowed to be described by multiple labels (or zero label) if more than one (or none) scanline gives a proper prediction. We test the proposed method on stereo matching datasets, from Middlebury, ETH3D, EuroSDR image matching benchmark, and the 2019 IEEE GRSS data fusion contest. The result indicates that under the framework of SGM-Forest, the multi-label strategy outperforms the single-label scheme consistently.

scholarly journals A Novel Stereo Matching Algorithm for Digital Surface Model (DSM) Generation in Water Areas

2020 ◽  
Vol 12 (5) ◽  
pp. 870 ◽  
Author(s):  
Wenhuan Yang ◽  
Xin Li ◽  
Bo Yang ◽  
Yu Fu
Keyword(s):  
Common Ground ◽  
Stereo Matching ◽  
Final Height ◽  
Block Matching ◽  
Disparity Estimation ◽  
Surface Model ◽  
Seed Point ◽  
Practical Applications ◽  
Dense Matching ◽  
Global Matching

Image dense matching has become one of the widely used means for DSM generation due to its good performance in both accuracy and efficiency. However, for water areas, the most common ground object, accurate disparity estimation is always a challenge to excellent image dense matching methods, as represented by semi-global matching (SGM), due to the poor texture. For this reason, a great deal of manual editing is always inevitable before practical applications. The main reason for this is the lack of uniqueness of matching primitives, with fixed size and shape, used by those methods. In this paper, we propose a novel DSM generation method, namely semi-global and block matching (SGBM), to achieve accurate disparity and height estimation in water areas by adaptive block matching instead of pixel matching. First, the water blocks are extracted by seed point growth, and an adaptive block matching strategy considering geometrical deformations, called end-block matching (EBM), is adopted to achieve accurate disparity estimation. Then, the disparity of all other pixels beyond these water blocks is obtained by SGM. Last, the median value of height of all pixels within the same block is selected as the final height for this block after forward intersection. Experiments are conducted on ZiYuan-3 (ZY-3) stereo images, and the results show that DSM generated by our method in water areas has high accuracy and visual quality.

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 Edge-Sense Bidirectional Pyramid Network for Stereo Matching of VHR Remote Sensing Images

2020 ◽  
Vol 12 (24) ◽  
pp. 4025
Author(s):  
Rongshu Tao ◽  
Yuming Xiang ◽  
Hongjian You
Keyword(s):  
Remote Sensing ◽  
Stereo Matching ◽  
Tall Buildings ◽  
Disparity Estimation ◽  
Complex Structures ◽  
Learning Networks ◽  
Remote Sensing Images ◽  
Essential Step ◽  
Disparity Range ◽  
The Cost

As an essential step in 3D reconstruction, stereo matching still faces unignorable problems due to the high resolution and complex structures of remote sensing images. Especially in occluded areas of tall buildings and textureless areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we develop a novel edge-sense bidirectional pyramid stereo matching network to solve the aforementioned problems. The cost volume is constructed from negative to positive disparities since the disparity range in remote sensing images varies greatly and traditional deep learning networks only work well for positive disparities. Then, the occlusion-aware maps based on the forward-backward consistency assumption are applied to reduce the influence of the occluded area. Moreover, we design an edge-sense smoothness loss to improve the performance of textureless areas while maintaining the main structure. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two methods, DenseMapNet and PSMNet, in terms of averaged endpoint error (EPE) and the fraction of erroneous pixels (D1), and the improvements in occluded and textureless areas are significant.

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 Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Younes Chihab ◽  
Zineb Bousbaa ◽  
Marouane Chihab ◽  
Omar Bencharef ◽  
Soumia Ziti
Keyword(s):  
Random Forest ◽  
Foreign Exchange ◽  
Growth Index ◽  
Trading Strategy ◽  
Good Prediction ◽  
Currency Markets ◽  
Probit Regression ◽  
Trading Systems ◽  
Currency Market ◽  
Minimum Requirements

In the Forex market, the price of the currencies increases and decreases rapidly based on many economic and political factors such as commercial balance, the growth index, the inflation rate, and the employment indicators. Having a good strategy to buy and sell can make a profit from the above changes. A successful strategy in Forex should take into consideration the relation between benefits and risks. In this work, we propose an intraweek foreign exchange speculation strategy for currency markets based on a combination of technical indicators. This system has a two-level decision and is composed of the Probit regression model and rules discovery using Random Forest. There are two minimum requirements for a trading strategy: a rule to enter the market and a rule to exit it. Our proposed system, to enter the currency market, should validate two conditions. First, it should validate Random Forest access rules over the following week while in the second one the predicted value of the next day using Probit should be positive. To exit the currency market just one negative warning from Probit or Random Forest is enough. This system was used to develop dynamic portfolio trading systems. The profitability of the model was examined for USD/(EUR, JYN, BRP) variation within the period from January 2014 to January 2016. The proposed system allows improving the prediction accuracy. This indicates a good prediction of the behavior market and it helps to identify the good times to enter it or to leave it.

scholarly journals Robust Indoor Localization Methods Using Random Forest-Based Filter against MAC Spoofing Attack

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6756
Author(s):  
DongHyun Ko ◽  
Seok-Hwan Choi ◽  
Sungyong Ahn ◽  
Yoon-Ho Choi
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Indoor Localization ◽  
Artificial Noise ◽  
Good Prediction ◽  
Localization Method ◽  
Good Prediction Accuracy ◽  
Attack Scenario ◽  
Natural Noise

With the development of wireless networks and mobile devices, interest on indoor localization systems (ILSs) has increased. In particular, Wi-Fi-based ILSs are widely used because of the good prediction accuracy without additional hardware. However, as the prediction accuracy decreases in environments with natural noise, some studies were conducted to remove it. So far, two representative methods, i.e., the filtering-based method and deep learning-based method, have shown a significant effect in removing natural noise. However, the prediction accuracy of these methods severely decreased under artificial noise caused by adversaries. In this paper, we introduce a new media access control (MAC) spoofing attack scenario injecting artificial noise, where the prediction accuracy of Wi-Fi-based indoor localization system significantly decreases. We also propose a new deep learning-based indoor localization method using random forest(RF)-filter to provide the good prediction accuracy under the new MAC spoofing attack scenario. From the experimental results, we show that the proposed indoor localization method provides much higher prediction accuracy than the previous methods in environments with artificial noise.

scholarly journals Salivary Microbiome Variation in Early Childhood Caries of Children 3–6 Years of Age and Its Association With Iron Deficiency Anemia and Extrinsic Black Stain

2021 ◽  
Vol 11 ◽  
Author(s):  
Rui Han ◽  
Jin Yue ◽  
Haozhi Lin ◽  
Nan Du ◽  
Jinfeng Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Iron Deficiency ◽  
Iron Deficiency Anemia ◽  
Random Forest Classifier ◽  
High Abundance ◽  
Deficiency Anemia ◽  
Tooth Surface ◽  
Rrna Gene ◽  
Good Prediction ◽  
Oral Flora

ECC is a common clinical manifestation of the oral cavity in childhood and Iron deficiency-anemia (IDA) is a high-risk factor but extrinsic black stain on the tooth surface is a protective factor for caries. There is limited information about oral microecological change in early children who suffer from ECC with IDA and extrinsic black stain (BS). This study enrolled 136 children aged 3-6 years. Dental caries and teeth BS were examined. Saliva was collected for 16S rRNA gene and fingertip blood were for Hemoglobin test. There are 93 ECC including 13 with IDA (IDA ECC) and 80 without IDA (NIDA ECC) and 43 caries free (CF) including 17 with BS (BSCF) and 26 without BS (NBS CF). Statistical analysis of microbiota data showed differences of the oral flora in different groups. The oral flora of the IDA ECC group had a high diversity, while the BSCF group had a low diversity. The bacterial genera Bacillus, Moraxella, and Rhodococcus were enriched in the IDA ECC while Neisseria was enriched in the NIDA ECC. Neisseria only exhibited high abundance in the BSCF, and the remaining genera exhibited high abundance in the NBSCF. Interestingly, the BSCF had the same trend as the NIDA ECC, and the opposite trend was observed with IDA ECC. We established random forest classifier using these biomarkers to predict disease outcomes. The random forest classifier achieved the best accuracy in predicting the outcome of caries, anemia and black stain using seven, one and eight biomarkers, respectively; and the accuracies of the classifiers were 93.35%, 94.62% and 95.23%, respectively. Our selected biomarkers can achieve good prediction, suggesting their potential clinical implications.

scholarly journals SEMI – GLOBAL MERGING OF DIGITAL SURFACE MODELS FROM MULTIPLE STEREOPAIRS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 267-271
Author(s):  
S. Pang ◽  
X. Hu ◽  
M. Zhang ◽  
L. Ye
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Experimental Results ◽  
Surface Model ◽  
Digital Surface Model ◽  
Global Optimization Algorithm ◽  
Smoothness Constraint ◽  
Dense Matching ◽  
Surface Models ◽  
Global Matching

The semi-global optimization algorithm, which approximates a global 2D smoothness constraint by combining several 1D constraints, has been widely used in the field of image dense matching for digital surface model (DSM) generation. However, due to occlusion, shadow and textureless area of the matching images, some inconsistency may exist in the overlapping areas of different DSMs. To address this problem, based on the DSMs generated by semi-global matching from multiple stereopairs, a novel semi-global merging algorithm is proposed to generate a reliable and consistent DSM in this paper. Two datasets, each covering 1&amp;thinsp;km<sup>2</sup>, are used to validate the proposed method. Experimental results show that the optimal DSM after merging can effectively eliminate the inconsistency and reduce redundancy in the overlapping areas.

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