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Relevant bibliographies by topics / Correspondence estimation

Academic literature on the topic 'Correspondence estimation'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Correspondence estimation"

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Liu, Yizhang, Shengjie Zhao, Hao Deng, and Fuqiang Ding. "Correspondence Learning via Correspondence Embedded and Channel Recalibration Network." ITM Web of Conferences 60 (2024): 00008. http://dx.doi.org/10.1051/itmconf/20246000008.

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Correspondence learning is pivotal to many computer vision-based tasks. Existing methods regard each correspondence equally along the channel dimension, which weakens the feature representation capability of the network. To alleviate this problem, we propose a Correspondence Embedded and Channel Recalibration Network, named CECR-Net, to predict the inlier probability of each correspondence and recover camera poses. The proposed CECR-Net is designed to explore the potential impact of correspondences on the channel dimension, and recalibrate the weight of each channel, so that our CECRNet can capture more exact contextual information. Experiments show that our CECR-Net is effective in outlier removal and camera pose estimation tasks on challenging public datasets.

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Huang, Zhaoyang, Xiaokun Pan, Weihong Pan, Weikang Bian, Yan Xu, Ka Chun Cheung, Guofeng Zhang, and Hongsheng Li. "NeuralMarker." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–10. http://dx.doi.org/10.1145/3550454.3555468.

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We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addressed by fitting a homography model based on sparse feature matching. However, they are only able to handle plane-like markers and the sparse features do not sufficiently utilize appearance information. In this paper, we propose a novel framework NeuralMarker, training a neural network estimating dense marker correspondences under various challenging conditions, such as marker deformation, harsh lighting, etc. Deep learning has presented an excellent performance in correspondence learning once provided with sufficient training data. However, annotating pixel-wise dense correspondence for training marker correspondence is too expensive. We observe that the challenges of marker correspondence estimation come from two individual aspects: geometry variation and appearance variation. We, therefore, design two components addressing these two challenges in NeuralMarker. First, we create a synthetic dataset FlyingMarkers containing marker-image pairs with ground truth dense correspondences. By training with FlyingMarkers, the neural network is encouraged to capture various marker motions. Second, we propose the novel Symmetric Epipolar Distance (SED) loss, which enables learning dense correspondence from posed images. Learning with the SED loss and the cross-lighting posed images collected by Structure-from-Motion (SfM), NeuralMarker is remarkably robust in harsh lighting environments and avoids synthetic image bias. Besides, we also propose a novel marker correspondence evaluation method circumstancing annotations on real marker-image pairs and create a new benchmark. We show that NeuralMarker significantly outperforms previous methods and enables new interesting applications, including Augmented Reality (AR) and video editing.

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Zhang, Shihua, and Jiayi Ma. "ConvMatch: Rethinking Network Design for Two-View Correspondence Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (June 26, 2023): 3472–79. http://dx.doi.org/10.1609/aaai.v37i3.25456.

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Multilayer perceptron (MLP) has been widely used in two-view correspondence learning for only unordered correspondences provided, and it extracts deep features from individual correspondence effectively. However, the problem of lacking context information limits its performance and hence, many extra complex blocks are designed to capture such information in the follow-up studies. In this paper, from a novel perspective, we design a correspondence learning network called ConvMatch that for the first time can leverage convolutional neural network (CNN) as the backbone to capture better context, thus avoiding the complex design of extra blocks. Specifically, with the observation that sparse motion vectors and dense motion field can be converted into each other with interpolating and sampling, we regularize the putative motion vectors by estimating dense motion field implicitly, then rectify the errors caused by outliers in local areas with CNN, and finally obtain correct motion vectors from the rectified motion field. Extensive experiments reveal that ConvMatch with a simple CNN backbone consistently outperforms state-of-the-arts including MLP-based methods for relative pose estimation and homography estimation, and shows promising generalization ability to different datasets and descriptors. Our code is publicly available at https://github.com/SuhZhang/ConvMatch.

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Fu, Mingliang, and Weijia Zhou. "DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation." Sensors 19, no. 5 (February 28, 2019): 1032. http://dx.doi.org/10.3390/s19051032.

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In recent years, estimating the 6D pose of object instances with convolutional neural network (CNN) has received considerable attention. Depending on whether intermediate cues are used, the relevant literature can be roughly divided into two broad categories: direct methods and two-stage pipelines. For the latter, intermediate cues, such as 3D object coordinates, semantic keypoints, or virtual control points instead of pose parameters are regressed by CNN in the first stage. Object pose can then be solved by correspondence constraints constructed with these intermediate cues. In this paper, we focus on the postprocessing of a two-stage pipeline and propose to combine two learning concepts for estimating object pose under challenging scenes: projection grouping on one side, and correspondence learning on the other. We firstly employ a local-patch based method to predict projection heatmaps which denote the confidence distribution of projection of 3D bounding box’s corners. A projection grouping module is then proposed to remove redundant local maxima from each layer of heatmaps. Instead of directly feeding 2D–3D correspondences to the perspective-n-point (PnP) algorithm, multiple correspondence hypotheses are sampled from local maxima and its corresponding neighborhood and ranked by a correspondence–evaluation network. Finally, correspondences with higher confidence are selected to determine object pose. Extensive experiments on three public datasets demonstrate that the proposed framework outperforms several state of the art methods.

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Dai, Luanyuan, Xin Liu, Jingtao Wang, Changcai Yang, and Riqing Chen. "Learning Two-View Correspondences and Geometry via Local Neighborhood Correlation." Entropy 23, no. 8 (August 9, 2021): 1024. http://dx.doi.org/10.3390/e23081024.

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Seeking quality feature correspondences (also known as matches) is a foundational step in computer vision. In our work, a novel and effective network with a stable local constraint, named the Local Neighborhood Correlation Network (LNCNet), is proposed to capture abundant contextual information of each correspondence in the local region, followed by calculating the essential matrix and camera pose estimation. Firstly, the k-Nearest Neighbor (KNN) algorithm is used to divide the local neighborhood roughly. Then, we calculate the local neighborhood correlation matrix (LNC) between the selected correspondence and other correspondences in the local region, which is used to filter outliers to obtain more accurate local neighborhood information. We cluster the filtered information into feature vectors containing richer neighborhood contextual information so that they can be used to more accurately determine the probability of correspondences as inliers. Extensive experiments have demonstrated that our proposed LNCNet performs better than some state-of-the-art networks to accomplish outlier rejection and camera pose estimation tasks in complex outdoor and indoor scenes.

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Redert, A., E. Hendriks, and J. Biemond. "Correspondence estimation in image pairs." IEEE Signal Processing Magazine 16, no. 3 (May 1999): 29–46. http://dx.doi.org/10.1109/79.768571.

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YAMASHINA, Hideki, Akihiro ICHIHASHI, Atushi KURODA, and Koichi IKEDA. "ESTIMATION OF COLOR RENDERING INDICES WITH CORRESPONDENCE TO PERCEIVED COLOR SHIFTS." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 78, Appendix (1994): 377–78. http://dx.doi.org/10.2150/jieij1980.78.appendix_377.

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Pons-Moll, Gerard, Jonathan Taylor, Jamie Shotton, Aaron Hertzmann, and Andrew Fitzgibbon. "Metric Regression Forests for Correspondence Estimation." International Journal of Computer Vision 113, no. 3 (April 11, 2015): 163–75. http://dx.doi.org/10.1007/s11263-015-0818-9.

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Yi, Yunai, Diya Sun, Peixin Li, Tae-Kyun Kim, Tianmin Xu, and Yuru Pei. "Unsupervised random forest for affinity estimation." Computational Visual Media 8, no. 2 (December 6, 2021): 257–72. http://dx.doi.org/10.1007/s41095-021-0241-9.

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AbstractThis paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness. The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node.The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees. A pseudo-leaf-splitting (PLS) algorithm is introduced to account for spatial relationships, which regularizes affinity measures and overcomes inconsistent leaf assign-ments. The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences. The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence. Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.

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Tang, Jiexiong, John Folkesson, and Patric Jensfelt. "Geometric Correspondence Network for Camera Motion Estimation." IEEE Robotics and Automation Letters 3, no. 2 (April 2018): 1010–17. http://dx.doi.org/10.1109/lra.2018.2794624.

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Dissertations / Theses on the topic "Correspondence estimation"

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Qi, Zhen. "Pose estimation using points to regions correspondence." Laramie, Wyo. : University of Wyoming, 2008. http://proquest.umi.com/pqdweb?did=1663060061&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Hewa, Thondilege Akila Sachinthani Pemasiri. "Multimodal Image Correspondence." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235433/1/Akila%2BHewa%2BThondilege%2BThesis%281%29.pdf.

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Multimodal images are used across many application areas including medical and surveillance. Due to the different characteristics of different imaging modalities, developing image processing algorithms for multimodal images is challenging. This thesis proposes effective solutions for the challenging problem of multimodal semantic correspondence where the connections between similar components across images from different modalities are established. The proposed methods which are based on deep learning techniques have been applied for several applications including epilepsy type classification and 3D reconstruction of human hand from visible and X-ray image. These proposed algorithms can be adapted to many other imaging modalities.

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Kazemi, Vahid. "Correspondence Estimation in Human Face and Posture Images." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-150115.

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Many computer vision tasks such as object detection, pose estimation,and alignment are directly related to the estimation of correspondences overinstances of an object class. Other tasks such as image classification andverification if not completely solved can largely benefit from correspondenceestimation. This thesis presents practical approaches for tackling the corre-spondence estimation problem with an emphasis on deformable objects.Different methods presented in this thesis greatly vary in details but theyall use a combination of generative and discriminative modeling to estimatethe correspondences from input images in an efficient manner. While themethods described in this work are generic and can be applied to any object,two classes of objects of high importance namely human body and faces arethe subjects of our experimentations.When dealing with human body, we are mostly interested in estimating asparse set of landmarks – specifically we are interested in locating the bodyjoints. We use pictorial structures to model the articulation of the body partsgeneratively and learn efficient discriminative models to localize the parts inthe image. This is a common approach explored by many previous works. Wefurther extend this hybrid approach by introducing higher order terms to dealwith the double-counting problem and provide an algorithm for solving theresulting non-convex problem efficiently. In another work we explore the areaof multi-view pose estimation where we have multiple calibrated cameras andwe are interested in determining the pose of a person in 3D by aggregating2D information. This is done efficiently by discretizing the 3D search spaceand use the 3D pictorial structures model to perform the inference.In contrast to the human body, faces have a much more rigid structureand it is relatively easy to detect the major parts of the face such as eyes,nose and mouth, but performing dense correspondence estimation on facesunder various poses and lighting conditions is still challenging. In a first workwe deal with this variation by partitioning the face into multiple parts andlearning separate regressors for each part. In another work we take a fullydiscriminative approach and learn a global regressor from image to landmarksbut to deal with insufficiency of training data we augment it by a large numberof synthetic images. While we have shown great performance on the standardface datasets for performing correspondence estimation, in many scenariosthe RGB signal gets distorted as a result of poor lighting conditions andbecomes almost unusable. This problem is addressed in another work wherewe explore use of depth signal for dense correspondence estimation. Hereagain a hybrid generative/discriminative approach is used to perform accuratecorrespondence estimation in real-time.

QC 20140919

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Bartosch, Nadine. "Correspondence-based pairwise depth estimation with parallel acceleration." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-34372.

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This report covers the implementation and evaluation of a stereo vision corre- spondence-based depth estimation algorithm on a GPU. The results and feed- back are used for a Multi-view camera system in combination with Jetson TK1 devices for parallelized image processing and the aim of this system is to esti- mate the depth of the scenery in front of it. The performance of the algorithm plays the key role. Alongside the implementation, the objective of this study is to investigate the advantages of parallel acceleration inter alia the differences to the execution on a CPU which are significant for all the function, the imposed overheads particular for a GPU application like memory transfer from the CPU to the GPU and vice versa as well as the challenges for real-time and concurrent execution. The study has been conducted with the aid of CUDA on three NVIDIA GPUs with different characteristics and with the aid of knowledge gained through extensive literature study about different depth estimation algo- rithms but also stereo vision and correspondence as well as CUDA in general. Using the full set of components of the algorithm and expecting (near) real-time execution is utopic in this setup and implementation, the slowing factors are in- ter alia the semi-global matching. Investigating alternatives shows that results for disparity maps of a certain accuracy are also achieved by local methods like the Hamming Distance alone and by a filter that refines the results. Further- more, it is demonstrated that the kernel launch configuration and the usage of GPU memory types like shared memory is crucial for GPU implementations and has an impact on the performance of the algorithm. Just concurrency proves to be a more complicated task, especially in the desired way of realization. For the future work and refinement of the algorithm it is therefore recommended to invest more time into further optimization possibilities in regards of shared memory and into integrating the algorithm into the actual pipeline.

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Kurdila, Hannah Robertshaw. "Gappy POD and Temporal Correspondence for Lizard Motion Estimation." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83603.

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With the maturity of conventional industrial robots, there has been increasing interest in designing robots that emulate realistic animal motions. This discipline requires careful and systematic investigation of a wide range of animal motions from biped, to quadruped, and even to serpentine motion of centipedes, millipedes, and snakes. Collecting optical motion capture data of such complex animal motions can be complicated for several reasons. Often there is the need to use many high-quality cameras for detailed subject tracking, and self-occlusion, loss of focus, and contrast variations challenge any imaging experiment. The problem of self-occlusion is especially pronounced for animals. In this thesis, we walk through the process of collecting motion capture data of a running lizard. In our collected raw video footage, it is difficult to make temporal correspondences using interpolation methods because of prolonged blurriness, occlusion, or the limited field of vision of our cameras. To work around this, we first make a model data set by making our best guess of the points' locations through these corruptions. Then, we randomly eclipse the data, use Gappy POD to repair the data and then see how closely it resembles the initial set, culminating in a test case where we simulate the actual corruptions we see in the raw video footage.
Master of Science

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Besse, F. O. "PatchMatch belief propagation for correspondence field estimation and its applications." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1409029/.

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Correspondence fields estimation is an important process that lies at the core of many different applications. Is it often seen as an energy minimisation problem, which is usually decomposed into the combined minimisation of two energy terms. The first is the unary energy, or data term, which reflects how well the solution agrees with the data. The second is the pairwise energy, or smoothness term, and ensures that the solution displays a certain level of smoothness, which is crucial for many applications. This thesis explores the possibility of combining two well-established algorithms for correspondence field estimation, PatchMatch and Belief Propagation, in order to benefit from the strengths of both and overcome some of their weaknesses. Belief Propagation is a common algorithm that can be used to optimise energies comprising both unary and pairwise terms. It is however computational expensive and thus not adapted to continuous spaces which are often needed in imaging applications. On the other hand, PatchMatch is a simple, yet very efficient method for optimising the unary energy of such problems on continuous and high dimensional spaces. The algorithm has two main components: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these components are related to the components of a specific form of Belief Propagation, called Particle Belief Propagation (PBP). PatchMatch however suffers from the lack of an explicit smoothness term. We show that unifying the two approaches yields a new algorithm, PMBP, which has improved performance compared to PatchMatch and is orders of magnitude faster than PBP. We apply our new optimiser to two different applications: stereo matching and optical flow. We validate the benefits of PMBP through series of experiments and show that we consistently obtain lower errors than both PatchMatch and Belief Propagation.

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Sellent, Anita [Verfasser], and Marcus [Akademischer Betreuer] Magnor. "Dense Correspondence Field Estimation from Multiple Images / Anita Sellent ; Betreuer: Marcus Magnor." Braunschweig : Technische Universität Braunschweig, 2011. http://d-nb.info/1175825697/34.

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Barsai, Gabor. "DATA REGISTRATION WITHOUT EXPLICIT CORRESPONDENCE FOR ADJUSTMENT OF CAMERA ORIENTATION PARAMETER ESTIMATION." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315855340.

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Johnson, Amanda R. "A pose estimation algorithm based on points to regions correspondence using multiple viewpoints." Laramie, Wyo. : University of Wyoming, 2008. http://proquest.umi.com/pqdweb?did=1798480891&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Linz, Christian [Verfasser], and Marcus [Akademischer Betreuer] Magnor. "Correspondence Estimation and Image Interpolation for Photo-Realistic Rendering / Christian Linz ; Betreuer: Marcus Magnor." Braunschweig : Technische Universität Braunschweig, 2011. http://d-nb.info/1175825557/34.

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Books on the topic "Correspondence estimation"

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Ahmed, S. E. (Syed Ejaz), 1957- editor of compilation, ed. Perspectives on big data analysis: Methodologies and applications : International Workshop on Perspectives on High-Dimensional Data Anlaysis II, May 30-June 1, 2012, Centre de Recherches Mathématiques, University de Montréal, Montréal, Québec, Canada. Providence, Rhode Island: American Mathematical Society, 2014.

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Office, General Accounting. Tax administration: Difficulties in accurately estimating tax examination yield : report to the chairman, Committee on the Budget, U.S. Senate. Washington, D.C: The Office, 1988.

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Office, General Accounting. Tax administration: Difficulties in accurately estimating tax examination yield : report to the chairman, Committee on the Budget, U.S. Senate. Washington, D.C: The Office, 1988.

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Estimating the Degrees of an Arma Model. Correspondence Analysis and Gaussian Ordination. Physica-Verlag, 1985.

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Social security: Need to improve unit times for estimating field office staff budgets : report to the Secretary of Health and Human Services. Washington, D.C: The Office, 1986.

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Social security: Need to improve unit times for estimating field office staff budgets : report to the Secretary of Health and Human Services. Washington, D.C: The Office, 1986.

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Book chapters on the topic "Correspondence estimation"

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Aygün, Mehmet, and Oisin Mac Aodha. "Demystifying Unsupervised Semantic Correspondence Estimation." In Lecture Notes in Computer Science, 125–42. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20056-4_8.

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Tseng, Gwojyh, and Arun K. Sood. "Motion Parameter Estimation Using Correspondence Approach." In Active Perception and Robot Vision, 43–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77225-2_3.

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Huang, Lin, Tomas Hodan, Lingni Ma, Linguang Zhang, Luan Tran, Christopher Twigg, Po-Chen Wu, Junsong Yuan, Cem Keskin, and Robert Wang. "Neural Correspondence Field for Object Pose Estimation." In Lecture Notes in Computer Science, 585–603. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20080-9_34.

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Yang, Xiaohang, Lingtong Kong, Ziyun Liang, and Jie Yang. "Homography Estimation Network Based on Dense Correspondence." In Communications in Computer and Information Science, 632–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92310-5_73.

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Cortés, Xavier, Carlos Moreno, and Francesc Serratosa. "Improving the Correspondence Establishment Based on Interactive Homography Estimation." In Computer Analysis of Images and Patterns, 457–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40246-3_57.

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Skafte, Anders, and Rune Brincker. "Estimation of Unmeasured DOF’s Using the Local Correspondence Principle." In Topics on the Dynamics of Civil Structures, Volume 1, 265–71. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2413-0_26.

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Schaffert, Roman, Markus Weiß, Jian Wang, Anja Borsdorf, and Andreas Maier. "Learning-Based Correspondence Estimation for 2-D/3-D Registration." In Informatik aktuell, 222–28. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29267-6_50.

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Hui, Tak-Wai, and Chen Change Loy. "LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation." In Computer Vision – ECCV 2020, 169–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58565-5_11.

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Li, Hongyang, Jiehong Lin, and Kui Jia. "DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation." In Lecture Notes in Computer Science, 369–85. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20077-9_22.

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McClelland, Jamie. "Estimating Internal Respiratory Motion from Respiratory Surrogate Signals Using Correspondence Models." In 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy, 187–213. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36441-9_9.

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Conference papers on the topic "Correspondence estimation"

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Tamas, Levente, and Andras Majdik. "Heterogeneous feature based correspondence estimation." In 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012). IEEE, 2012. http://dx.doi.org/10.1109/mfi.2012.6343042.

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Hietanen, Antti, Jussi Halme, Anders Glent Buch, Jyrki Latokartano, and J. K. Kamarainen. "Robustifying correspondence based 6D object pose estimation." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989091.

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Wulff, Jonas, Thomas Lotz, Thomas Stehle, Til Aach, and J. Geoffrey Chase. "Correspondence estimation from non-rigid motion information." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2011. http://dx.doi.org/10.1117/12.877896.

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Yuan, Xiaohui, Jian Zhang, Bill Buckles, and Zhaoshan Yuan. "Feature-based approach for image correspondence estimation." In Second International Conference on Image and Graphics, edited by Wei Sui. SPIE, 2002. http://dx.doi.org/10.1117/12.477214.

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Cech, Jan, Jordi Sanchez-Riera, and Radu Horaud. "Scene flow estimation by growing correspondence seeds." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995442.

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Banani, Mohamed El, Ignacio Rocco, David Novotny, Andrea Vedaldi, Natalia Neverova, Justin Johnson, and Ben Graham. "Self-supervised Correspondence Estimation via Multiview Registration." In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. http://dx.doi.org/10.1109/wacv56688.2023.00127.

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Chen, Ying, and Chunjian Hua. "Automatic Facial Feature Correspondence Based on Pose Estimation." In 2010 Second International Workshop on Education Technology and Computer Science. IEEE, 2010. http://dx.doi.org/10.1109/etcs.2010.614.

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Besse, Frederic, Carsten Rother, Andrew Fitzgibbon, and Jan Kautz. "PMBP: PatchMatch Belief Propagation for Correspondence Field Estimation." In British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.132.

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Mannan Mondal, Md Abdul, and Md Haider Ali. "On stereo correspondence estimation: a spiral search algorithm." In 2011 International Conference on Graphic and Image Processing. SPIE, 2011. http://dx.doi.org/10.1117/12.913526.

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Zhao, Shanshan, Xi Li, and Omar El Farouk Bourahla. "Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/488.

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As an important and challenging problem in computer vision, learning based optical flow estimation aims to discover the intrinsic correspondence structure between two adjacent video frames through statistical learning. Therefore, a key issue to solve in this area is how to effectively model the multi-scale correspondence structure properties in an adaptive end-to-end learning fashion. Motivated by this observation, we propose an end-to-end multi-scale correspondence structure learning (MSCSL) approach for optical flow estimation. In principle, the proposed MSCSL approach is capable of effectively capturing the multi-scale inter-image-correlation correspondence structures within a multi-level feature space from deep learning. Moreover, the proposed MSCSL approach builds a spatial Conv-GRU neural network model to adaptively model the intrinsic dependency relationships among these multi-scale correspondence structures. Finally, the above procedures for correspondence structure learning and multi-scale dependency modeling are implemented in a unified end-to-end deep learning framework. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed approach.

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