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Articles de revues sur le sujet « Stereo »

Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 25 mai 2024

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1

Miyazaki, Daisuke, et Kazuya Uegomori. « Example-Based Multispectral Photometric Stereo for Multi-Colored Surfaces ». Journal of Imaging 8, no 4 (11 avril 2022) : 107. http://dx.doi.org/10.3390/jimaging8040107.

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A photometric stereo needs three images taken under three different light directions lit one by one, while a color photometric stereo needs only one image taken under three different lights lit at the same time with different light directions and different colors. As a result, a color photometric stereo can obtain the surface normal of a dynamically moving object from a single image. However, the conventional color photometric stereo cannot estimate a multicolored object due to the colored illumination. This paper uses an example-based photometric stereo to solve the problem of the color photometric stereo. The example-based photometric stereo searches the surface normal from the database of the images of known shapes. Color photometric stereos suffer from mathematical difficulty, and they add many assumptions and constraints; however, the example-based photometric stereo is free from such mathematical problems. The process of our method is pixelwise; thus, the estimated surface normal is not oversmoothed, unlike existing methods that use smoothness constraints. To demonstrate the effectiveness of this study, a measurement device that can realize the multispectral photometric stereo method with sixteen colors is employed instead of the classic color photometric stereo method with three colors.
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2

Li, Yinhao, Han Bao, Zheng Ge, Jinrong Yang, Jianjian Sun et Zeming Li. « BEVStereo : Enhancing Depth Estimation in Multi-View 3D Object Detection with Temporal Stereo ». Proceedings of the AAAI Conference on Artificial Intelligence 37, no 2 (26 juin 2023) : 1486–94. http://dx.doi.org/10.1609/aaai.v37i2.25234.

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Restricted by the ability of depth perception, all Multi-view 3D object detection methods fall into the bottleneck of depth accuracy. By constructing temporal stereo, depth estimation is quite reliable in indoor scenarios. However, there are two difficulties in directly integrating temporal stereo into outdoor multi-view 3D object detectors: 1) The construction of temporal stereos for all views results in high computing costs. 2) Unable to adapt to challenging outdoor scenarios. In this study, we propose an effective method for creating temporal stereo by dynamically determining the center and range of the temporal stereo. The most confident center is found using the EM algorithm. Numerous experiments on nuScenes have shown the BEVStereo's ability to deal with complex outdoor scenarios that other stereo-based methods are unable to handle. For the first time, a stereo-based approach shows superiority in scenarios like a static ego vehicle and moving objects. BEVStereo achieves the new state-of-the-art in the camera-only track of nuScenes dataset while maintaining memory efficiency. Codes have been released.
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3

Woei Teo, Chun. « STEREO ENCODING DEVICE, STEREO DECODING DEVICE, AND STEREO ENCODING METHOD ». Journal of the Acoustical Society of America 134, no 6 (2013) : 4582. http://dx.doi.org/10.1121/1.4836712.

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McKee, Suzanne P., Preeti Verghese et Bart Farell. « Stereo sensitivity depends on stereo matching ». Journal of Vision 5, no 10 (23 novembre 2005) : 3. http://dx.doi.org/10.1167/5.10.3.

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Agui, Takeshi, Tomoharu Nagao, Ryuji Yamazaki et Masayuki Nakajima. « Stereo clipping for binocular stereo images. » Journal of the Institute of Television Engineers of Japan 45, no 1 (1991) : 94–100. http://dx.doi.org/10.3169/itej1978.45.94.

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Takeuchi, Gakuto, Manabu Onuma, Wataru Saito, Takaaki Hara, Eriko Ikari, Norio Usui et Toru Utsumi. « Evaluation and Comparison of Stereo Tests- JACO Stereo Test, TNO Stereo Test, and Stereo Fly Test ». JAPANESE ORTHOPTIC JOURNAL 48 (2018) : 65–71. http://dx.doi.org/10.4263/jorthoptic.048f105.

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7

Chen, Ai Hua, Cheng Hui Gao et Bing Wei He. « Image Stereo Correspondence Method for Stereo Vision ». Applied Mechanics and Materials 475-476 (décembre 2013) : 337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.337.

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Image stereo correspondence is the core technology of stereo vision. It has been widely studied and applied in the fields such as 3D reconstruction, vision measurement and target recognition. According to characteristics and application of stereo matching technology, the image stereo correspondence methods can be classified into three categories: local stereo correspondence, global stereo correspondence and semi-global stereo correspondence. Some image stereo correspondence solutions and problems are emphatically analyzed. Finally some future research issues on image stereo correspondence are highlighted.
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8

Thernisien, A., A. Vourlidas et R. A. Howard. « CME reconstruction : Pre-STEREO and STEREO era ». Journal of Atmospheric and Solar-Terrestrial Physics 73, no 10 (juin 2011) : 1156–65. http://dx.doi.org/10.1016/j.jastp.2010.10.019.

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9

Liu, J., S. Ji, C. Zhang et Z. Qin. « EVALUATION OF DEEP LEARNING BASED STEREO MATCHING METHODS : FROM GROUND TO AERIAL IMAGES ». ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (30 mai 2018) : 593–97. http://dx.doi.org/10.5194/isprs-archives-xlii-2-593-2018.

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Dense stereo matching has been extensively studied in photogrammetry and computer vision. In this paper we evaluate the application of deep learning based stereo methods, which were raised from 2016 and rapidly spread, on aerial stereos other than ground images that are commonly used in computer vision community. Two popular methods are evaluated. One learns matching cost with a convolutional neural network (known as MC-CNN); the other produces a disparity map in an end-to-end manner by utilizing both geometry and context (known as GC-net). First, we evaluate the performance of the deep learning based methods for aerial stereo images by a direct model reuse. The models pre-trained on KITTI 2012, KITTI 2015 and Driving datasets separately, are directly applied to three aerial datasets. We also give the results of direct training on target aerial datasets. Second, the deep learning based methods are compared to the classic stereo matching method, Semi-Global Matching(SGM), and a photogrammetric software, SURE, on the same aerial datasets. Third, transfer learning strategy is introduced to aerial image matching based on the assumption of a few target samples available for model fine tuning. It experimentally proved that the conventional methods and the deep learning based methods performed similarly, and the latter had greater potential to be explored.
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10

Hong, Phuc Nguyen, et Chang Wook Ahn. « Stereo Matching Methods for Imperfectly Rectified Stereo Images ». Symmetry 11, no 4 (19 avril 2019) : 570. http://dx.doi.org/10.3390/sym11040570.

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Stereo matching has been under development for decades and is an important process for many applications. Difficulties in stereo matching include textureless regions, occlusion, illumination variation, the fattening effect, and discontinuity. These challenges are effectively solved in recently developed stereo matching algorithms. A new imperfect rectification problem has recently been encountered in stereo matching, and the problem results from the high resolution of stereo images. State-of-the-art stereo matching algorithms fail to exactly reconstruct the depth information using stereo images with imperfect rectification, as the imperfectly rectified image problems are not explicitly taken into account. In this paper, we solve the imperfect rectification problems, and propose matching stereo matching methods that based on absolute differences, square differences, normalized cross correlation, zero-mean normalized cross correlation, and rank and census transforms. Finally, we conduct experiments to evaluate these stereo matching methods using the Middlebury datasets. The experimental results show the proposed stereo matching methods can reduce error rate significantly for stereo images with imperfect rectification.
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11

Wolski, Krzysztof, Fangcheng Zhong, Karol Myszkowski et Rafał K. Mantiuk. « Dark stereo ». ACM Transactions on Graphics 41, no 4 (juillet 2022) : 1–12. http://dx.doi.org/10.1145/3528223.3530136.

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It is often desirable or unavoidable to display Virtual Reality (VR) or stereoscopic content at low brightness. For example, a dimmer display reduces the flicker artefacts that are introduced by low-persistence VR headsets. It also saves power, prolongs battery life, and reduces the cost of a display or projection system. Additionally, stereo movies are usually displayed at relatively low luminance due to polarization filters or other optical elements necessary to separate two views. However, the binocular depth cues become less reliable at low luminance. In this paper, we propose a model of stereo constancy that predicts the precision of binocular depth cues for a given contrast and luminance. We use the model to design a novel contrast enhancement algorithm that compensates for the deteriorated depth perception to deliver good-quality stereoscopic images even for displays of very low brightness.
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12

Runk, Peter T. B. « Stereo Viewing ». Science 236, no 4802 (8 mai 1987) : 657. http://dx.doi.org/10.1126/science.236.4802.657.a.

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13

Klayman, Arnold I. « Stereo synthesizer ». Journal of the Acoustical Society of America 93, no 6 (juin 1993) : 3543–44. http://dx.doi.org/10.1121/1.405334.

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14

Morichev, I. E., et I. O. Starobogatov. « Stereo viewer ». Journal of Optical Technology 71, no 11 (1 novembre 2004) : 799. http://dx.doi.org/10.1364/jot.71.000799.

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15

Zhou, Tinghui, Richard Tucker, John Flynn, Graham Fyffe et Noah Snavely. « Stereo magnification ». ACM Transactions on Graphics 37, no 4 (10 août 2018) : 1–12. http://dx.doi.org/10.1145/3197517.3201323.

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16

Stedman, Geoff, et Mary Cole. « Stereo visions ». Physics World 7, no 12 (décembre 1994) : 18. http://dx.doi.org/10.1088/2058-7058/7/12/19.

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17

Firebaugh, William H. « Stereo microphone ». Journal of the Acoustical Society of America 91, no 2 (février 1992) : 1202. http://dx.doi.org/10.1121/1.402517.

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18

Andre, Eugene M., Allan G. Modzikowski et Seth R. Banks. « Stereo headphone ». Journal of the Acoustical Society of America 89, no 5 (mai 1991) : 2490. http://dx.doi.org/10.1121/1.400889.

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19

Dell, Howard. « Stereo sound ». IEE Review 36, no 7 (1990) : 254. http://dx.doi.org/10.1049/ir:19900101.

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20

Meares, D. J. « Stereo sound ». IEE Review 36, no 8 (1990) : 305. http://dx.doi.org/10.1049/ir:19900128.

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21

Nakamura, K., T. Ooe, T. Matsushita, K. Nakagawa, K. Aoki, K. Mamada, M. Ono et T. Kurokawa. « Stereo Fluoroscopy ». Journal of Spinal Disorders 10, no 2 (avril 1997) : 102???105. http://dx.doi.org/10.1097/00002517-199704000-00002.

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22

Papiernik, Raymond S. « Stereo headphone ». Journal of the Acoustical Society of America 86, no 4 (octobre 1989) : 1638. http://dx.doi.org/10.1121/1.398590.

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23

MILLMAN, BARRY M. « Stereo problem ». Nature 338, no 6216 (avril 1989) : 547. http://dx.doi.org/10.1038/338547b0.

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24

MAX, NELSON. « Stereo-image ». Nature 339, no 6220 (mai 1989) : 105. http://dx.doi.org/10.1038/339105c0.

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25

COULSON, ANDREW. « Stereo suggestion ». Nature 341, no 6239 (septembre 1989) : 192. http://dx.doi.org/10.1038/341192a0.

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26

Unterman, Nathan A. « Stereo stamps ». Physics Teacher 32, no 3 (mars 1994) : 133. http://dx.doi.org/10.1119/1.2343935.

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27

Eades, Alwyn. « Stereo-Spurious ». Microscopy Today 26, no 6 (novembre 2018) : 46–47. http://dx.doi.org/10.1017/s1551929518001086.

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28

Stewart, Ian. « Stereo Zoom ». Microscopy Today 7, no 7 (septembre 1999) : 44. http://dx.doi.org/10.1017/s1551929500064889.

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29

Hasinoff, Samuel W., et Kiriakos N. Kutulakos. « Confocal Stereo ». International Journal of Computer Vision 81, no 1 (10 septembre 2008) : 82–104. http://dx.doi.org/10.1007/s11263-008-0164-2.

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30

Pickering, Michael. « Personal Stereo ». European Journal of Communication 33, no 1 (février 2018) : 102–4. http://dx.doi.org/10.1177/0267323117753748.

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31

RUNK, P. T. B. « Stereo Viewing ». Science 236, no 4802 (8 mai 1987) : 657. http://dx.doi.org/10.1126/science.236.4802.657.

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32

Marshall-Linnemeier, Lynn. « Stereo Propaganda ». Southern Cultures 17, no 2 (2011) : 55–60. http://dx.doi.org/10.1353/scu.2011.0036.

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33

Xiong, Weihua, et Brian Funt. « Stereo retinex ». Image and Vision Computing 27, no 1-2 (janvier 2009) : 178–88. http://dx.doi.org/10.1016/j.imavis.2007.11.012.

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34

LIU, Xiao-yong, Da-hai LI, Qiong-hua WANG, Xi LIU et Xiao-ping QI. « Adaptive stereo matching algorithms for color stereo images ». Journal of Computer Applications 31, no 1 (22 mars 2011) : 163–66. http://dx.doi.org/10.3724/sp.j.1087.2011.00163.

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35

Pui-Kuen Ho et R. Chung. « Stereo-motion with stereo and motion in complement ». IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no 2 (février 2000) : 221–26. http://dx.doi.org/10.1109/tpami.2000.825761.

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36

Reu, Phillip. « Stereo-rig Design : Stereo-Angle Selection - Part 4 ». Experimental Techniques 37, no 2 (mars 2013) : 1–2. http://dx.doi.org/10.1111/ext.12006.

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37

Pui-Kuen Ho et R. Chung. « Stereo-motion with stereo and motion in complement ». IEEE Transactions on Pattern Analysis and Machine Intelligence 22, no 2 (2000) : 215–20. http://dx.doi.org/10.1109/34.825760.

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38

Kilpua, E. K. J., L. K. Jian, Y. Li, J. G. Luhmann et C. T. Russell. « Multipoint ICME encounters : Pre-STEREO and STEREO observations ». Journal of Atmospheric and Solar-Terrestrial Physics 73, no 10 (juin 2011) : 1228–41. http://dx.doi.org/10.1016/j.jastp.2010.10.012.

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39

DING, Zhi-Ping, et Yoshiaki NOMURA. « Stereo Matching with Used Motion Trinocular Stereo Vision. » Transactions of the Japan Society of Mechanical Engineers Series C 64, no 618 (1998) : 546–52. http://dx.doi.org/10.1299/kikaic.64.546.

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40

Starks, Michael. « Stereoscopic Imaging Technology : A Review of Patents and the Literature ». International Journal of Virtual Reality 1, no 2 (1 janvier 1995) : 2–24. http://dx.doi.org/10.20870/ijvr.1995.1.2.2602.

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This article contains comments on the nature of stereo perception as it relates to stereo video displays as well as a discussion of interfaces for stereo graphics and the interaction of stereo video and stereo graphics. A number of areas of interest are briefly reviewed and accompanied by extensive citations from the patent and technical literature. These include single camera (70 references) and dual camera (100 references) stereoscopy, compatible 3D recording and transmission (57 references), head mounted displays (85 references), field sequential stereo (285 references), and autostereoscopic systems including lenticular (64 references), parallax barrier (22 references), stereoptiplexer (17 references), integral imaging (24 references), direction selective mirrors, lenses or screens (26 references), volumetric displays (133 references), holovision (13 references), stereoendoscopy (14 references), and stereosculpting (15 references). Also discussed are interfaces for stereo graphics and the interaction of stereo video and stereo graphics.
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41

Patil, S., et Q. Guo. « STELLAR : A LARGE SATELLITE STEREO DATASET FOR DIGITAL SURFACE MODEL GENERATION ». International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (14 juin 2023) : 433–40. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-433-2023.

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Abstract. Stellar is a large, satellite stereo dataset. It contains rectified stereo pairs of the terrain captured by the satellite image sensors and corresponding true disparity maps and semantic segmentation. Unlike stereo vision in autonomous driving and mobile imaging, a satellite stereo pair is not captured simultaneously. Thus, the same object in a satellite stereo pair is more likely to have a varied visual appearance. Stellar provides flexible access to such stereo pairs to train methods to be robust to such appearance variation. We use publicly available data sources, and invented several techniques to perform data registration, rectification, and semantic segmentation on the data to build Stellar. In our preliminary experiment, we fine-tuned two deep-learning stereo methods on Stellar. The result demonstrates that most of the time, these methods generate denser and more accurate disparity maps for satellite stereo by fine-tuning on Stellar, compared to without fine-tuning on satellite stereo datasets, or fine-tuning on previous, smaller satellite stereo datasets. Stellar is available to download at https://github.com/guo-research-group/Stellar.
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42

Xu, Zheng, Masanori Idesawa et Qin Wang. « Mask Scale Adjustment (MSA) in Stereo Matching ». Journal of Robotics and Mechatronics 20, no 1 (20 février 2008) : 159–70. http://dx.doi.org/10.20965/jrm.2008.p0159.

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We proposed mask scale adjustment method for stereo matching inspired by volume perception in human visual perception. Stereo matching is required to obtain 3-dimensional (3D) information from stereo pair images, and many sophisticated approaches have been developed to precisely obtain correspondence in stereo pairs. We found a forgotten problem not solved by these methods inherent in stereo matching and originating in the scale difference between stereo pair images. We propose mask scale adjustment for improving matching performance, especially in adjacent areas of binocularly unpaired parts, on objects with curved surfaces. Implementing our proposal into conventional stereo matching would yield more precise 3D information on objects and realize of volume perception.
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43

Baek, Seung-Hae, Pathum Rathnayaka et Soon-Yong Park. « Calibration of a Stereo Radiation Detection Camera Using Planar Homography ». Journal of Sensors 2016 (2016) : 1–11. http://dx.doi.org/10.1155/2016/8928096.

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This paper proposes a calibration technique of a stereo gamma detection camera. Calibration of the internal and external parameters of a stereo vision camera is a well-known research problem in the computer vision society. However, few or no stereo calibration has been investigated in the radiation measurement research. Since no visual information can be obtained from a stereo radiation camera, it is impossible to use a general stereo calibration algorithm directly. In this paper, we develop a hybrid-type stereo system which is equipped with both radiation and vision cameras. To calibrate the stereo radiation cameras, stereo images of a calibration pattern captured from the vision cameras are transformed in the view of the radiation cameras. The homography transformation is calibrated based on the geometric relationship between visual and radiation camera coordinates. The accuracy of the stereo parameters of the radiation camera is analyzed by distance measurements to both visual light and gamma sources. The experimental results show that the measurement error is about 3%.
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44

Zhang, Yaru, Jiantao Liu, Tong Zhang et Zhibiao Zhao. « Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution ». Applied Sciences 13, no 12 (18 juin 2023) : 7265. http://dx.doi.org/10.3390/app13127265.

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In the process of stereo super-resolution reconstruction, in addition to the richness of the extracted feature information directly affecting the texture details of the reconstructed image, the texture details of the corresponding pixels between stereo image pairs also have an important impact on the reconstruction accuracy in the process of network learning. Therefore, aiming at the information interaction and stereo consistency of stereo image pairs, a cross-view attention interaction fusion stereo super-resolution algorithm is proposed. Firstly, based on parallax attention mechanism and triple attention mechanism, an attention stereo fusion module is constructed. The attention stereo fusion module is inserted between different levels of two single image super-resolution network branches, and the attention weight is calculated through the cross dimensional interaction of the three branches. It makes full use of the ability of single image super-resolution network to extract single view information and further maintaining the stereo consistency between stereo image pairs. Then, an enhanced cross-view interaction strategy including three fusion methods is proposed. Specifically, the vertical sparse fusion method is used to integrate the interior view information of different levels in the two single image super-resolution sub branches, the horizontal dense fusion method is used to connect the adjacent attention stereo fusion modules and the constraint between stereo image consistency is further strengthened in combination with the feature fusion method. Finally, the experimental results on Flickr 1024, Middlebury and KITTI benchmark datasets show that the proposed algorithm is superior to the existing stereo image super-resolution methods in quantitative measurement and qualitative visual quality while maintaining the stereo consistency of image pairs.
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45

KUMAR, S. SRINIVAS, et B. N. CHATTERJI. « STEREO MATCHING ALGORITHMS BASED ON FUZZY APPROACH ». International Journal of Pattern Recognition and Artificial Intelligence 16, no 07 (novembre 2002) : 883–99. http://dx.doi.org/10.1142/s0218001402002040.

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Stereo matching is the central problem of stereovision paradigm. Area-based techniques provide the dense disparity maps and hence they are preferred for stereo correspondence. Normalized cross correlation (NCC), sum of squared differences (SSD) and sum of absolute differences (SAD) are the linear correlation measures generally used in the area-based techniques for stereo matching. In this paper, similarity measure for stereo matching based on fuzzy relations is used to establish the correspondence in the presence of intensity variations in stereo images. The strength of relationship of fuzzified data of two windows in the left image and the right image of stereo image pair is determined by considering the appropriate fuzzy aggregation operators. However, these measures fail to establish correspondence of the pixels in the stereo images in the presence of occluded pixels in the corresponding windows. Another stereo matching algorithm based on fuzzy relations of fuzzy data is used for stereo matching in such regions of images. This algorithm is based on weighted normalized cross correlation (WNCC) of the intensity data in the left and the right windows of stereo image pair. The properties of the similarity measures used in these algorithms are also discussed. Experiments with various real stereo images prove the superiority of these algorithms over normalized cross correlation (NCC) under nonideal conditions.
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46

Ługowski, A. M. « AM stereo and FM stereo detection : an implicit approach ». Electronics Letters 22, no 19 (1986) : 973. http://dx.doi.org/10.1049/el:19860665.

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47

Kitagawa, Hiroshi. « Stereo system and stereo method for electronic acoustical system ». Journal of the Acoustical Society of America 104, no 3 (1998) : 1150. http://dx.doi.org/10.1121/1.424277.

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48

Pribanic, Tomislav, Nenad Obradovic et Joaquim Salvi. « Stereo computation combining structured light and passive stereo matching ». Optics Communications 285, no 6 (mars 2012) : 1017–22. http://dx.doi.org/10.1016/j.optcom.2011.10.045.

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49

Gadia, Davide, Gianfranco Garipoli, Cristian Bonanomi, Luigi Albani et Alessandro Rizzi. « Assessing stereo blindness and stereo acuity on digital displays ». Displays 35, no 4 (octobre 2014) : 206–12. http://dx.doi.org/10.1016/j.displa.2014.05.010.

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50

Ying, Xinyi, Yingqian Wang, Longguang Wang, Weidong Sheng, Wei An et Yulan Guo. « A Stereo Attention Module for Stereo Image Super-Resolution ». IEEE Signal Processing Letters 27 (2020) : 496–500. http://dx.doi.org/10.1109/lsp.2020.2973813.

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