Academic literature on the topic 'Non-local matching'
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Journal articles on the topic "Non-local matching":
Huang, Xu, Yongjun Zhang, and Zhaoxi Yue. "IMAGE-GUIDED NON-LOCAL DENSE MATCHING WITH THREE-STEPS OPTIMIZATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 67–74. http://dx.doi.org/10.5194/isprsannals-iii-3-67-2016.
Huang, Xu, Yongjun Zhang, and Zhaoxi Yue. "IMAGE-GUIDED NON-LOCAL DENSE MATCHING WITH THREE-STEPS OPTIMIZATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 3, 2016): 67–74. http://dx.doi.org/10.5194/isprs-annals-iii-3-67-2016.
Cai, Weibo, Jintao Cheng, Juncan Deng, Yubin Zhou, Hua Xiao, Jian Zhang, and Kaiqing Luo. "Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information." Applied Sciences 12, no. 1 (December 23, 2021): 127. http://dx.doi.org/10.3390/app12010127.
ZHANG, Chao, Haitian SUN, and Takuya AKASHI. "Robust Non-Parametric Template Matching with Local Rigidity Constraints." IEICE Transactions on Information and Systems E99.D, no. 9 (2016): 2332–40. http://dx.doi.org/10.1587/transinf.2015edp7492.
Amit, Mika, Rolf Backofen, Steffen Heyne, Gad M. Landau, Mathias Mohl, Christina Otto, and Sebastian Will. "Local Exact Pattern Matching for Non-Fixed RNA Structures." IEEE/ACM Transactions on Computational Biology and Bioinformatics 11, no. 1 (January 2014): 219–30. http://dx.doi.org/10.1109/tcbb.2013.2297113.
Zhong, Ying, Xue-zhi Yang, Yi-ming Tang, Can-jun Liu, and Feng Yue. "Non-local Means Denoising Derived from Structure-adapted Block Matching." Journal of Electronics & Information Technology 35, no. 12 (February 23, 2014): 2908–15. http://dx.doi.org/10.3724/sp.j.1146.2013.00099.
Ying Luo, 罗颖, 霍冠英 Guanying Huo, 许金鑫 Jinxin Xu, and 李庆武 Qingwu Li. "Non-Local Stereo Matching Algorithm Based on Edge Constraint Iteration." Laser & Optoelectronics Progress 56, no. 15 (2019): 151501. http://dx.doi.org/10.3788/lop56.151501.
Zahari, Madiha, Rostam Affendi Hamzah, Nurulfajar Abd Manap, and Adi Irwan Herman. "Stereo matching algorithm for autonomous vehicle navigation using integrated matching cost and non-local aggregation." Bulletin of Electrical Engineering and Informatics 12, no. 1 (February 1, 2023): 328–37. http://dx.doi.org/10.11591/eei.v12i1.4122.
Li, Dongrui, Xiaofeng Huang, and Ying Yang. "Research on image denoising algorithm based on non-local block matching." International Journal of Information and Communication Technology 16, no. 3 (2020): 245. http://dx.doi.org/10.1504/ijict.2020.10027484.
Yang, Ying, Dongrui Li, and Xiaofeng Huang. "Research on image denoising algorithm based on non-local block matching." International Journal of Information and Communication Technology 16, no. 3 (2020): 245. http://dx.doi.org/10.1504/ijict.2020.106317.
Dissertations / Theses on the topic "Non-local matching":
Zhou, Dan. "Stereo Matching Based on Edge-Aware T-MST." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35538.
St-Jean, Samuel. "Acquisitions d'IRM de diffusion à haute résolution spatiale : nouvelles perspectives grâce au débruitage spatialement adaptatif et angulaire." Mémoire, Université de Sherbrooke, 2015. http://hdl.handle.net/11143/6993.
Carrillo, Hernan. "Colorisation d'images avec réseaux de neurones guidés par l'intéraction humaine." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0016.
Colorization is the process of adding colors to grayscale images. It is an important task in the image-editing and animation community. Although automatic colorization methods exist, they often produce unsatisfying results due to artifacts such as color bleeding, inconsistency, unnatural colors, and the ill-posed nature of the problem. Manual intervention is often necessary to achieve the desired outcome. Consequently, there is a growing interest in automating the colorization process while allowing artists to transfer their own style and vision to the process. In this thesis, we investigate various interaction formats by guiding colors of specific areas of an image or transferring them from a reference image or object. As part of this research, we introduce two semi-automatic colorization frameworks. First, we describe a deep learning architecture for exemplar-based image colorization that takes into account user’s reference images. Our second framework uses a diffusion model to colorize line art using user-provided color scribbles. This thesis first delves into a comprehensive overview of state-of-the-art image colorization methods, color spaces, evaluation metrics, and losses. While recent colorization methods based on deep-learning techniques are achieving the best results on this task, these methods are based on complex architectures and a high number of joint losses, which makes the reasoning behind each of these methods difficult. Here, we leverage a simple architecture in order to analyze the impact of different color spaces and several losses. Then, we propose a novel attention layer based on superpixel features to establish robust correspondences between high-resolution deep features from target and reference image pairs, called super-attention. This proposal deals with the quadratic complexity problem of the non-local calculation in the attention layer. Additionally, it helps to overcome color bleeding artifacts. We study its use in color transfer and exemplar-based colorization. We finally extend this model to specifically guide the colorization on segmented objects. Finally, we propose a diffusion probabilistic model based on implicit and explicit conditioning mechanism, to learn colorizing line art. Our approach enables the incorporation of user guidance through explicit color hints while leveraging on the prior knowledge from the trained diffusion model. We condition with an application-specific encoder that learns to extract meaningful information on user-provided scribbles. The method generates diverse and high-quality colorized images
Romanenko, Ilya. "Novel image processing algorithms and methods for improving their robustness and operational performance." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/16340.
Giraud, Remi. "Algorithmes de correspondance et superpixels pour l’analyse et le traitement d’images." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0771/document.
This thesis focuses on several aspects of image analysis and processing with non local methods. These methods are based on the redundancy of information that occurs in other images, and use matching algorithms, that are usually patch-based, to extract and transfer information from the example data. These approaches are widely used by the computer vision community, and are generally limited by the computational time of the matching algorithm, applied at the pixel scale, and by the necessity to perform preprocessing or learning steps to use large databases. To address these issues, we propose several general methods, without learning, fast, and that can be easily applied to different image analysis and processing applications on natural and medical images. We introduce a matching algorithm that enables to quickly extract patches from a large library of 3D images, that we apply to medical image segmentation. To use a presegmentation into superpixels that reduces the number of image elements, in a way that is similar to patches, we present a new superpixel neighborhood structure. This novel descriptor enables to efficiently use superpixels in non local approaches. We also introduce an accurate and regular superpixel decomposition method. We show how to evaluate this regularity in a robust manner, and that this property is necessary to obtain good superpixel-based matching performances
Lin, Zong-Han, and 林宗翰. "The Study of Integrating Block Matching 3D and Non-Local Means Image Denoising Algorithms." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5650006%22.&searchmode=basic.
國立中興大學
通訊工程研究所
107
Since digital images are often interfered by noises from various sources, image denoising is an important technique in image processing. Non-local means (NLM) algorithm achieves denoising through block matching. It compares the block of a pixel to be denoised with its neighboring blocks and uses the weighted average of all the neighboring pixels to remove noises. Later, block-matching 3D (BM3D) algorithm also uses the concept of block matching in image denoising. BM3D gathers similar blocks into a 3D group. It then uses collaborative filtering in these 3-D groups to remove noises. Collaborative filtering is accomplished in three steps: 1) 3D transformation of a group, 2) shrinkage of the transform coefficients, and 3) inverse 3D transformation. BM3D repeats the above block matching and collaborative filtering approach twice. In the first stage, hard thresholding is used in the shrinkage of the transform coefficients. In the second stage, Wiener filtering is used in the shrinkage. Therefore, there are three different denoising blocks from NLM and BM3D: a NLM denoising block, a BM3D denoising block in its first stage, and a BM3D denoising block in its second stage. By combining these three blocks in two steps, this thesis aims to find the best combination among the nine possibilities. Since the second stage of the BM3D require a reference with small noise interference, it cannot achieve acceptable denoising performance if put at the first step of the combination. Therefore, this thesis considers the other six combination by eliminating the second stage of BM3D from the first step of the combination. Experimental results demonstrate that NLM in the first step combined with the first stage of BM3D in the second step can speed up the processing by 2.98 times with only a small sacrifice in denoising performance.
Hsieh, Cheng-Hung, and 謝正宏. "A Non-Local Stereo Matching Algorithm Based on Improved Minimum Spanning Tree Structure and Occlusion Handling." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/6bn3ce.
國立臺北科技大學
電機工程系所
102
The work proposed a non-local-based stereo matching algorithm for disparity computation. The feature of non-local stereo matching method is to utilize a tree structure to propagate weighted matching cost, and thus it can achieve both the merits of accuracy and speed in global and local methods respectively. In this work, a new minimum spanning tree forming method is proposed. Our method adopts 8-connected rather than 4-connected grid to construct the minimum spanning tree, and we also exploit the stereo matching cost as the confidence value, to prioritize tree built-up order from the increased connection possibilities of 8-connected grid. Compared to the original non-local stereo matching method in literature, our proposed tree built-up method significantly improves the error rate of disparity from 5.48 to 4.85 by the Middlebury benchmark. This work also proposed a multiple left-right consistency checking method to identify occlusion points efficiently for further refining their disparity. After applying it, our error rate is further decreased from 4.85 to 4.80, while the overall complexity of ours is only 1.24 times of the original non-local method.
Books on the topic "Non-local matching":
Identification of Local Matching Fund Requirements for State-Administered Federal and Non-Federal Public Transportation Programs. Washington, D.C.: National Academies Press, 2011. http://dx.doi.org/10.17226/14530.
Horing, Norman J. Morgenstern. Retarded Green’s Functions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198791942.003.0005.
Book chapters on the topic "Non-local matching":
Clifford, Raphaël, and Benjamin Sach. "Online Approximate Matching with Non-local Distances." In Combinatorial Pattern Matching, 142–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02441-2_13.
Landau, Gad M., Avivit Levy, and Ilan Newman. "LCS Approximation via Embedding into Local Non-repetitive Strings." In Combinatorial Pattern Matching, 92–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02441-2_9.
Amit, Mika, Rolf Backofen, Steffen Heyne, Gad M. Landau, Mathias Möhl, Christina Schmiedl, and Sebastian Will. "Local Exact Pattern Matching for Non-fixed RNA Structures." In Combinatorial Pattern Matching, 306–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31265-6_25.
Sankoff, David. "Edit distance for genome comparison based on non-local operations." In Combinatorial Pattern Matching, 121–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-56024-6_10.
Nguyen, Hong Phuc, Thi Dinh Tran, and Quang Vinh Dinh. "Local Stereo Matching by Joining Shiftable Window and Non-parametric Transform." In Lecture Notes in Computer Science, 133–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35455-7_13.
Du, Jianning, Yanbing Xue, Hua Zhang, and Zan Gao. "Stereo Matching Based on Density Segmentation and Non-Local Cost Aggregation." In Advances in Multimedia Information Processing – PCM 2018, 253–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00767-6_24.
Iguernaissi, Rabah, Djamal Merad, and Pierre Drap. "People’s Re-identification Across Multiple Non-overlapping Cameras by Local Discriminative Patch Matching." In Lecture Notes in Computer Science, 190–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59876-5_22.
Chen, Geng, Yafeng Wu, Dinggang Shen, and Pew-Thian Yap. "XQ-NLM: Denoising Diffusion MRI Data via x-q Space Non-local Patch Matching." In Lecture Notes in Computer Science, 587–95. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46726-9_68.
Tsai, Chun-Jen, and Aggelos K. Katsaggelos. "Dense Disparity Estimation via Global and Local Matching." In Noblesse Workshop on Non-Linear Model Based Image Analysis, 289–94. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1597-7_45.
Xu, Jinxin, Xueling Yang, Jinbo Zuo, Jiayan Mu, and Zhiqiang Guan. "Forward-Backward Diffusion and Pruning-Based Cost Aggregation for Non-Local Stereo Matching." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221021.
Conference papers on the topic "Non-local matching":
Koster, Urs, and Aapo Hyvarinen. "Natural image statistics: Energy-based models estimated by score matching." In 2009 International Workshop on Local and Non-Local Approximation in Image Processing (LNLA 2009). IEEE, 2009. http://dx.doi.org/10.1109/lnla.2009.5278409.
Xu, Juan, Zhihui Wei, and Yubao Sun. "Non-Local Means Image Denoising with Local Geometric Structures Matching Strategy." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5301368.
Gould, S. "Multiclass pixel labeling with non-local matching constraints." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6248002.
Qingxiong Yang. "A non-local cost aggregation method for stereo matching." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247827.
Huang, Xiaoming, Guoqin Cui, and Yundong Zhang. "A fast non-local disparity refinement method for stereo matching." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025776.
Zhou, Ziheng, Samuel Chindaro, and Farzin Deravi. "Non-linear fusion of local matching scores for face verification." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813338.
Altantawy, Doaa A., Marwa Obbaya, and Sherif Kishk. "A fast non-local based stereo matching algorithm using graph cuts." In 2014 9th International Conference on Computer Engineering & Systems (ICCES). IEEE, 2014. http://dx.doi.org/10.1109/icces.2014.7030943.
Carrillo, Hernan, Michaël Clément, and Aurélie Bugeau. "Non-local Matching of Superpixel-based Deep Features for Color Transfer." In 17th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0010767900003124.
Ma, Yong, Huabing Zhou, Jun Chen, Jingshu Shi, and Zhongyuan Wang. "Non-rigid feature matching for image retrieval using global and local regularizations." In 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017. http://dx.doi.org/10.1109/icme.2017.8019441.
Zhong, Heng, Yahu Zhu, and Deqi Ming. "An efficient stereo matching method based on non-local spatial tree filter." In ICCIR 2022: 2022 2nd International Conference on Control and Intelligent Robot. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3548608.3559180.
Reports on the topic "Non-local matching":
van den Boogaard,, Vanessa, and Fabrizio Santoro. Co-Financing Community-Driven Development Through Informal Taxation: Experimental Evidence from South-Central Somalia. Institute of Development Studies (IDS), September 2021. http://dx.doi.org/10.19088/ictd.2021.016.
Bleakley, Hoyt, and Kevin Cowan. Corporate Dollar Debt and Depreciations: Much Ado about Nothing? Inter-American Development Bank, July 2005. http://dx.doi.org/10.18235/0010842.
Raei, Lamia. Exploring the Links: Youth participation and employment opportunities in Jordan. Oxfam IBIS, August 2021. http://dx.doi.org/10.21201/2021.7981.
Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.