Efficient high-resolution template matching with vector quantized nearest neighbour fields
Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pi...
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| Vydané v: | Pattern recognition Ročník 151; s. 110386 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.07.2024
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN-based methods have been shown to perform better in occlusions, appearance changes, and non-rigid transformations; however, they scale poorly with high-resolution data and high feature dimensions. We present an NN-based method that efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with k features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low-resolution data, and our method outperforms previous methods at higher resolution.
•Existing methods scale poorly with high-resolution images and high-dimensional features.•Vector quantization in the template features is used to reduce nearest neighbour computations.•Filtering is introduced in the nearest neighbour space to encode spatial information.•State-of-the-art results are generated in runtime and performance for high-resolution datasets. |
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| AbstractList | Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN-based methods have been shown to perform better in occlusions, appearance changes, and non-rigid transformations; however, they scale poorly with high-resolution data and high feature dimensions. We present an NN-based method that efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with k features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low-resolution data, and our method outperforms previous methods at higher resolution.
•Existing methods scale poorly with high-resolution images and high-dimensional features.•Vector quantization in the template features is used to reduce nearest neighbour computations.•Filtering is introduced in the nearest neighbour space to encode spatial information.•State-of-the-art results are generated in runtime and performance for high-resolution datasets. Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN-based methods have been shown to perform better in occlusions, appearance changes, and non-rigid transformations; however, they scale poorly with high-resolution data and high feature dimensions. We present an NN-based method that efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with k features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low-resolution data, and our method outperforms previous methods at higher resolution. |
| ArticleNumber | 110386 |
| Author | Sintorn, Ida-Maria Gupta, Ankit |
| Author_xml | – sequence: 1 givenname: Ankit orcidid: 0000-0002-9961-1041 surname: Gupta fullname: Gupta, Ankit email: ankit.gupta@it.uu.se – sequence: 2 givenname: Ida-Maria surname: Sintorn fullname: Sintorn, Ida-Maria email: ida.sintorn@it.uu.se |
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| Cites_doi | 10.7554/eLife.68946 10.1016/j.measurement.2021.109506 10.1109/TGRS.2019.2924684 10.3390/s22176658 10.1109/CVPR.2018.00188 10.1007/978-3-030-01249-6_25 10.1049/el.2016.1331 10.1109/CVPR.2013.312 10.1109/CVPR.2018.00759 10.1186/s12859-020-3363-7 10.1109/CVPR.2013.302 10.1016/j.patcog.2020.107337 10.1109/CVPR.2016.90 10.1109/CVPR.2019.01182 10.1145/2766983 10.1016/j.neucom.2015.03.024 10.1109/CVPR.2015.7299160 10.1109/CVPR.2015.7298813 10.1109/ACCESS.2019.2901943 10.1109/TPAMI.2011.106 10.1109/CVPR.2018.00283 10.1109/TIM.2021.3134326 10.1016/j.patcog.2019.107029 10.1016/j.is.2019.02.006 10.1109/TPAMI.2012.120 10.1145/1531326.1531330 10.1049/ipr2.12521 10.1049/ipr2.12716 10.1109/CVPR.2013.316 10.1016/j.measurement.2020.108362 10.1109/TIP.2019.2893743 10.1109/CVPR.2017.144 10.1109/JBHI.2022.3177602 |
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| Keywords | Vector quantized nearest neighbour field (VQ-NNF) Template matching Object detection High-resolution template matching |
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| References | Wang, Wang, Zhou, Liu, Dai, Du, Wahab (b3) 2021; 169 Lucas, Himes, Xue, Grant, Mahamid, Grigorieff (b1) 2021; 10 Paszke, Gross, Massa, Lerer, Bradbury, Chanan, Killeen, Lin, Gimelshein, Antiga, Desmaison, Kopf, Yang, DeVito, Raison, Tejani, Chilamkurthy, Steiner, Fang, Bai, Chintala (b37) 2019 Sun, Mao, Hong, Xu, Gui (b5) 2019; 7 Lan, Wu, Li (b9) 2021; 71 Zhou, Lu, Lv, Di, Zhao, Zhang (b10) 2015; 165 Aumüller, Bernhardsson, Faithfull (b39) 2020; 87 J. Cheng, Y. Wu, W. AbdAlmageed, P. Natarajan, QATM: Quality-aware template matching for deep learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11553–11562. Y. Wei, H. Xiao, H. Shi, Z. Jie, J. Feng, T.S. Huang, Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7268–7277. Jamriška, Fišer, Asente, Lu, Shechtman, Sỳkora (b28) 2015; 34 S. Korman, D. Reichman, G. Tsur, S. Avidan, Fast-match: Fast affine template matching, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2331–2338. Gupta, Sabirsh, Wählby, Sintorn (b7) 2022; 26 Barnes, Shechtman, Finkelstein, Goldman (b13) 2009; 28 Yang, Huang, Wang, Song, Yin (b22) 2019; 28 Achanta, Shaji, Smith, Lucchi, Fua, Süsstrunk (b23) 2012; 34 K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. T. Dekel, S. Oron, M. Rubinstein, S. Avidan, W.T. Freeman, Best-buddies similarity for robust template matching, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2021–2029. Z. Chen, H. Jin, Z. Lin, S. Cohen, Y. Wu, Large displacement optical flow from nearest neighbor fields, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2443–2450. Xu, Zhang, Brownjohn (b4) 2021; 179 Moudgil, Gandhi (b36) 2018 Zhang, Zhang, Akashi (b31) 2022; 16 Gao, Spratling (b25) 2022; 22 Y. Wu, J. Lim, M.-H. Yang, Online object tracking: A benchmark, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2411–2418. Zhao, He, Lei, Zhu (b27) 2023; 17 Viola, Jones (b32) 2001; Vol. 1 Khan, Smith (b16) 2005 Zhang, Yang, Jia (b30) 2020; 70 Lai, Lei, Deng, Yan, Ruan, Jinyun (b29) 2020; 98 Ye, Bruzzone, Shan, Bovolo, Zhu (b6) 2019; 57 S. Korman, M. Milam, S. Soatto, OATM: Occlusion aware template matching by consensus set maximization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2675–2683. R. Kat, R. Jevnisek, S. Avidan, Matching pixels using co-occurrence statistics, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1751–1759. I. Talmi, R. Mechrez, L. Zelnik-Manor, Template matching with deformable diversity similarity, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 175–183. Jia, Cao, Song, Tang, Zhu (b19) 2016; 52 Spratling (b24) 2020; 104 Thomas, Gehrig (b2) 2020; 21 N. Ben-Zrihem, L. Zelnik-Manor, Approximate nearest neighbor fields in video, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5233–5242. Ouyang, Tombari, Mattoccia, Di Stefano, Cham (b17) 2011; 34 L. Talker, Y. Moses, I. Shimshoni, Efficient sliding window computation for NN-based template matching, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 404–418. Yu, Koltun (b33) 2015 Zhang (10.1016/j.patcog.2024.110386_b30) 2020; 70 Moudgil (10.1016/j.patcog.2024.110386_b36) 2018 Lan (10.1016/j.patcog.2024.110386_b9) 2021; 71 Achanta (10.1016/j.patcog.2024.110386_b23) 2012; 34 Paszke (10.1016/j.patcog.2024.110386_b37) 2019 10.1016/j.patcog.2024.110386_b20 Gao (10.1016/j.patcog.2024.110386_b25) 2022; 22 10.1016/j.patcog.2024.110386_b8 10.1016/j.patcog.2024.110386_b21 Jamriška (10.1016/j.patcog.2024.110386_b28) 2015; 34 Thomas (10.1016/j.patcog.2024.110386_b2) 2020; 21 Yang (10.1016/j.patcog.2024.110386_b22) 2019; 28 10.1016/j.patcog.2024.110386_b15 Zhou (10.1016/j.patcog.2024.110386_b10) 2015; 165 10.1016/j.patcog.2024.110386_b38 10.1016/j.patcog.2024.110386_b18 Aumüller (10.1016/j.patcog.2024.110386_b39) 2020; 87 Lai (10.1016/j.patcog.2024.110386_b29) 2020; 98 Khan (10.1016/j.patcog.2024.110386_b16) 2005 Lucas (10.1016/j.patcog.2024.110386_b1) 2021; 10 10.1016/j.patcog.2024.110386_b11 10.1016/j.patcog.2024.110386_b12 Ouyang (10.1016/j.patcog.2024.110386_b17) 2011; 34 10.1016/j.patcog.2024.110386_b34 Wang (10.1016/j.patcog.2024.110386_b3) 2021; 169 10.1016/j.patcog.2024.110386_b35 10.1016/j.patcog.2024.110386_b14 Sun (10.1016/j.patcog.2024.110386_b5) 2019; 7 Zhao (10.1016/j.patcog.2024.110386_b27) 2023; 17 Ye (10.1016/j.patcog.2024.110386_b6) 2019; 57 Gupta (10.1016/j.patcog.2024.110386_b7) 2022; 26 Zhang (10.1016/j.patcog.2024.110386_b31) 2022; 16 Xu (10.1016/j.patcog.2024.110386_b4) 2021; 179 Spratling (10.1016/j.patcog.2024.110386_b24) 2020; 104 10.1016/j.patcog.2024.110386_b26 Barnes (10.1016/j.patcog.2024.110386_b13) 2009; 28 Jia (10.1016/j.patcog.2024.110386_b19) 2016; 52 Yu (10.1016/j.patcog.2024.110386_b33) 2015 Viola (10.1016/j.patcog.2024.110386_b32) 2001; Vol. 1 |
| References_xml | – volume: 57 start-page: 9059 year: 2019 end-page: 9070 ident: b6 article-title: Fast and robust matching for multimodal remote sensing image registration publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 71 start-page: 1 year: 2021 end-page: 13 ident: b9 article-title: GAD: A global-aware diversity-based template matching method publication-title: IEEE Trans. Instrum. Meas. – volume: 52 start-page: 1220 year: 2016 end-page: 1221 ident: b19 article-title: Colour FAST (CFAST) match: fast affine template matching for colour images publication-title: Electron. Lett. – volume: 34 start-page: 127 year: 2011 end-page: 143 ident: b17 article-title: Performance evaluation of full search equivalent pattern matching algorithms publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 169 year: 2021 ident: b3 article-title: Automatic laser profile recognition and fast tracking for structured light measurement using deep learning and template matching publication-title: Measurement – volume: Vol. 1 start-page: I year: 2001 ident: b32 article-title: Rapid object detection using a boosted cascade of simple features publication-title: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 – reference: K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778. – reference: Y. Wu, J. Lim, M.-H. Yang, Online object tracking: A benchmark, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2411–2418. – volume: 21 start-page: 1 year: 2020 end-page: 8 ident: b2 article-title: Multi-template matching: a versatile tool for object-localization in microscopy images publication-title: BMC Bioinform. – volume: 104 year: 2020 ident: b24 article-title: Explaining away results in accurate and tolerant template matching publication-title: Pattern Recognit. – volume: 22 start-page: 6658 year: 2022 ident: b25 article-title: Shape–texture debiased training for robust template matching publication-title: Sensors – volume: 16 start-page: 2738 year: 2022 end-page: 2751 ident: b31 article-title: DS-SRI: Diversity similarity measure against scaling, rotation, and illumination change for robust template matching publication-title: IET Image Process. – volume: 98 year: 2020 ident: b29 article-title: Fast and robust template matching with majority neighbour similarity and annulus projection transformation publication-title: Pattern Recognit. – year: 2015 ident: b33 article-title: Multi-scale context aggregation by dilated convolutions – reference: Y. Wei, H. Xiao, H. Shi, Z. Jie, J. Feng, T.S. Huang, Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7268–7277. – volume: 34 start-page: 1 year: 2015 end-page: 10 ident: b28 article-title: LazyFluids: Appearance transfer for fluid animations publication-title: ACM Trans. Graph. – reference: T. Dekel, S. Oron, M. Rubinstein, S. Avidan, W.T. Freeman, Best-buddies similarity for robust template matching, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2021–2029. – reference: L. Talker, Y. Moses, I. Shimshoni, Efficient sliding window computation for NN-based template matching, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 404–418. – volume: 7 start-page: 28392 year: 2019 end-page: 28401 ident: b5 article-title: Template matching-based method for intelligent invoice information identification publication-title: IEEE Access – volume: 28 start-page: 24 year: 2009 ident: b13 article-title: PatchMatch: A randomized correspondence algorithm for structural image editing publication-title: ACM Trans. Graph. – start-page: 673 year: 2005 end-page: 688 ident: b16 article-title: 5.3 - Fundamentals of vector quantization publication-title: Handbook of Image and Video Processing – volume: 165 start-page: 350 year: 2015 end-page: 360 ident: b10 article-title: Abrupt motion tracking via nearest neighbor field driven stochastic sampling publication-title: Neurocomputing – reference: J. Cheng, Y. Wu, W. AbdAlmageed, P. Natarajan, QATM: Quality-aware template matching for deep learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11553–11562. – volume: 17 start-page: 1346 year: 2023 end-page: 1354 ident: b27 article-title: Template matching via bipartite graph and graph attention mechanism publication-title: IET Image Process. – start-page: 8024 year: 2019 end-page: 8035 ident: b37 article-title: Pytorch: An imperative style, high-performance deep learning library publication-title: Advances in Neural Information Processing Systems 32 – volume: 10 year: 2021 ident: b1 article-title: Locating macromolecular assemblies in cells by 2D template matching with cisTEM publication-title: Elife – reference: N. Ben-Zrihem, L. Zelnik-Manor, Approximate nearest neighbor fields in video, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5233–5242. – volume: 70 start-page: 1 year: 2020 end-page: 9 ident: b30 article-title: Scale-adaptive NN-based similarity for robust template matching publication-title: IEEE Trans. Instrum. Meas. – start-page: 629 year: 2018 end-page: 645 ident: b36 article-title: Long-term visual object tracking benchmark publication-title: Asian Conference on Computer Vision – volume: 179 year: 2021 ident: b4 article-title: An accurate and distraction-free vision-based structural displacement measurement method integrating siamese network based tracker and correlation-based template matching publication-title: Measurement – reference: I. Talmi, R. Mechrez, L. Zelnik-Manor, Template matching with deformable diversity similarity, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 175–183. – reference: Z. Chen, H. Jin, Z. Lin, S. Cohen, Y. Wu, Large displacement optical flow from nearest neighbor fields, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2443–2450. – volume: 26 start-page: 4079 year: 2022 end-page: 4089 ident: b7 article-title: Simsearch: A human-in-the-loop learning framework for fast detection of regions of interest in microscopy images publication-title: IEEE J. Biomed. Health Inform. – reference: R. Kat, R. Jevnisek, S. Avidan, Matching pixels using co-occurrence statistics, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1751–1759. – reference: S. Korman, D. Reichman, G. Tsur, S. Avidan, Fast-match: Fast affine template matching, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2331–2338. – volume: 34 start-page: 2274 year: 2012 end-page: 2282 ident: b23 article-title: SLIC superpixels compared to state-of-the-art superpixel methods publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: S. Korman, M. Milam, S. Soatto, OATM: Occlusion aware template matching by consensus set maximization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2675–2683. – volume: 28 start-page: 3061 year: 2019 end-page: 3074 ident: b22 article-title: Robust semantic template matching using a superpixel region binary descriptor publication-title: IEEE Trans. Image Process. – volume: 87 year: 2020 ident: b39 article-title: ANN-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms publication-title: Inf. Syst. – volume: 10 year: 2021 ident: 10.1016/j.patcog.2024.110386_b1 article-title: Locating macromolecular assemblies in cells by 2D template matching with cisTEM publication-title: Elife doi: 10.7554/eLife.68946 – volume: 179 year: 2021 ident: 10.1016/j.patcog.2024.110386_b4 article-title: An accurate and distraction-free vision-based structural displacement measurement method integrating siamese network based tracker and correlation-based template matching publication-title: Measurement doi: 10.1016/j.measurement.2021.109506 – volume: 57 start-page: 9059 issue: 11 year: 2019 ident: 10.1016/j.patcog.2024.110386_b6 article-title: Fast and robust matching for multimodal remote sensing image registration publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2924684 – volume: 22 start-page: 6658 issue: 17 year: 2022 ident: 10.1016/j.patcog.2024.110386_b25 article-title: Shape–texture debiased training for robust template matching publication-title: Sensors doi: 10.3390/s22176658 – ident: 10.1016/j.patcog.2024.110386_b20 doi: 10.1109/CVPR.2018.00188 – start-page: 673 year: 2005 ident: 10.1016/j.patcog.2024.110386_b16 article-title: 5.3 - Fundamentals of vector quantization – volume: Vol. 1 start-page: I year: 2001 ident: 10.1016/j.patcog.2024.110386_b32 article-title: Rapid object detection using a boosted cascade of simple features – ident: 10.1016/j.patcog.2024.110386_b8 doi: 10.1007/978-3-030-01249-6_25 – volume: 52 start-page: 1220 issue: 14 year: 2016 ident: 10.1016/j.patcog.2024.110386_b19 article-title: Colour FAST (CFAST) match: fast affine template matching for colour images publication-title: Electron. Lett. doi: 10.1049/el.2016.1331 – ident: 10.1016/j.patcog.2024.110386_b35 doi: 10.1109/CVPR.2013.312 – volume: 70 start-page: 1 year: 2020 ident: 10.1016/j.patcog.2024.110386_b30 article-title: Scale-adaptive NN-based similarity for robust template matching publication-title: IEEE Trans. Instrum. Meas. – ident: 10.1016/j.patcog.2024.110386_b34 doi: 10.1109/CVPR.2018.00759 – volume: 21 start-page: 1 issue: 1 year: 2020 ident: 10.1016/j.patcog.2024.110386_b2 article-title: Multi-template matching: a versatile tool for object-localization in microscopy images publication-title: BMC Bioinform. doi: 10.1186/s12859-020-3363-7 – ident: 10.1016/j.patcog.2024.110386_b18 doi: 10.1109/CVPR.2013.302 – volume: 104 year: 2020 ident: 10.1016/j.patcog.2024.110386_b24 article-title: Explaining away results in accurate and tolerant template matching publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2020.107337 – ident: 10.1016/j.patcog.2024.110386_b38 doi: 10.1109/CVPR.2016.90 – ident: 10.1016/j.patcog.2024.110386_b26 doi: 10.1109/CVPR.2019.01182 – volume: 34 start-page: 1 issue: 4 year: 2015 ident: 10.1016/j.patcog.2024.110386_b28 article-title: LazyFluids: Appearance transfer for fluid animations publication-title: ACM Trans. Graph. doi: 10.1145/2766983 – year: 2015 ident: 10.1016/j.patcog.2024.110386_b33 – volume: 165 start-page: 350 year: 2015 ident: 10.1016/j.patcog.2024.110386_b10 article-title: Abrupt motion tracking via nearest neighbor field driven stochastic sampling publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.03.024 – ident: 10.1016/j.patcog.2024.110386_b11 doi: 10.1109/CVPR.2015.7299160 – start-page: 629 year: 2018 ident: 10.1016/j.patcog.2024.110386_b36 article-title: Long-term visual object tracking benchmark – ident: 10.1016/j.patcog.2024.110386_b14 doi: 10.1109/CVPR.2015.7298813 – volume: 7 start-page: 28392 year: 2019 ident: 10.1016/j.patcog.2024.110386_b5 article-title: Template matching-based method for intelligent invoice information identification publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2901943 – volume: 34 start-page: 127 issue: 1 year: 2011 ident: 10.1016/j.patcog.2024.110386_b17 article-title: Performance evaluation of full search equivalent pattern matching algorithms publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2011.106 – ident: 10.1016/j.patcog.2024.110386_b21 doi: 10.1109/CVPR.2018.00283 – volume: 71 start-page: 1 year: 2021 ident: 10.1016/j.patcog.2024.110386_b9 article-title: GAD: A global-aware diversity-based template matching method publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2021.3134326 – volume: 98 year: 2020 ident: 10.1016/j.patcog.2024.110386_b29 article-title: Fast and robust template matching with majority neighbour similarity and annulus projection transformation publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.107029 – volume: 87 year: 2020 ident: 10.1016/j.patcog.2024.110386_b39 article-title: ANN-benchmarks: A benchmarking tool for approximate nearest neighbor algorithms publication-title: Inf. Syst. doi: 10.1016/j.is.2019.02.006 – volume: 34 start-page: 2274 issue: 11 year: 2012 ident: 10.1016/j.patcog.2024.110386_b23 article-title: SLIC superpixels compared to state-of-the-art superpixel methods publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2012.120 – volume: 28 start-page: 24 issue: 3 year: 2009 ident: 10.1016/j.patcog.2024.110386_b13 article-title: PatchMatch: A randomized correspondence algorithm for structural image editing publication-title: ACM Trans. Graph. doi: 10.1145/1531326.1531330 – volume: 16 start-page: 2738 issue: 10 year: 2022 ident: 10.1016/j.patcog.2024.110386_b31 article-title: DS-SRI: Diversity similarity measure against scaling, rotation, and illumination change for robust template matching publication-title: IET Image Process. doi: 10.1049/ipr2.12521 – volume: 17 start-page: 1346 issue: 5 year: 2023 ident: 10.1016/j.patcog.2024.110386_b27 article-title: Template matching via bipartite graph and graph attention mechanism publication-title: IET Image Process. doi: 10.1049/ipr2.12716 – ident: 10.1016/j.patcog.2024.110386_b12 doi: 10.1109/CVPR.2013.316 – volume: 169 year: 2021 ident: 10.1016/j.patcog.2024.110386_b3 article-title: Automatic laser profile recognition and fast tracking for structured light measurement using deep learning and template matching publication-title: Measurement doi: 10.1016/j.measurement.2020.108362 – volume: 28 start-page: 3061 issue: 6 year: 2019 ident: 10.1016/j.patcog.2024.110386_b22 article-title: Robust semantic template matching using a superpixel region binary descriptor publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2019.2893743 – ident: 10.1016/j.patcog.2024.110386_b15 doi: 10.1109/CVPR.2017.144 – volume: 26 start-page: 4079 issue: 8 year: 2022 ident: 10.1016/j.patcog.2024.110386_b7 article-title: Simsearch: A human-in-the-loop learning framework for fast detection of regions of interest in microscopy images publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2022.3177602 – start-page: 8024 year: 2019 ident: 10.1016/j.patcog.2024.110386_b37 article-title: Pytorch: An imperative style, high-performance deep learning library |
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| Title | Efficient high-resolution template matching with vector quantized nearest neighbour fields |
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