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Relevant bibliographies by topics / Correspondence estimation

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Journal articles

Academic literature on the topic 'Correspondence estimation'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Correspondence estimation"

1

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 ca

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2

Huang, Zhaoyang, Xiaokun Pan, Weihong Pan, et al. "NeuralMarker." ACM Transactions on Graphics 41, no. 6 (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 learn

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3

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 (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,

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4

Fu, Mingliang, and Weijia Zhou. "DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation." Sensors 19, no. 5 (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

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5

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 (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 c

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6

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

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7

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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8

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 (2015): 163–75. http://dx.doi.org/10.1007/s11263-015-0818-9.

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9

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 (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) algorit

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10

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

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