CVPR
Collection
Accepted papers for CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition), one dataset per year. • 14 items • Updated
paper_id uint32 0 470 | title stringlengths 14 142 | authors listlengths 1 17 | cvf_url stringlengths 89 125 | pdf_url stringlengths 90 126 | supp_url stringclasses 0
values | arxiv_id stringclasses 16
values | arxiv_id_source stringclasses 2
values | bibtex large_stringlengths 248 462 | abstract large_stringlengths 392 1.89k | embedding listlengths 768 768 |
|---|---|---|---|---|---|---|---|---|---|---|
0 | Deformable Spatial Pyramid Matching for Fast Dense Correspondences | [
"Jaechul Kim",
"Ce Liu",
"Fei Sha",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Kim_Deformable_Spatial_Pyramid_2013_CVPR_paper.pdf | null | null | null | @InProceedings{Kim_2013_ICCV_Workshops,author = {Kim, Jaechul and Liu, Ce and Sha, Fei and Grauman, Kristen},title = {Deformable Spatial Pyramid Matching for Fast Dense Correspondences},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | We introduce a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences. Dense matching methods typically enforce both appearance agreement between matched pixels as well as geometric smoothness between neighboring pixels. Whereas the prevailing approaches operate at the pixel ... | [
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1 | A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles | [
"Dror Sholomon",
"Omid David",
"Nathan S. Netanyahu"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Sholomon_A_Genetic_Algorithm-Based_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Sholomon_A_Genetic_Algorithm-Based_2013_CVPR_paper.pdf | null | 1711.06769 | title_snapshot | @InProceedings{Sholomon_2013_ICCV_Workshops,author = {Sholomon, Dror and David, Omid and Netanyahu, Nathan S.},title = {A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | In this paper we propose the first effective automated, genetic algorithm (GA)-based jigsaw puzzle solver. We introduce a novel procedure of merging two "parent" solutions to an improved "child" solution by detecting, extracting, and combining correctly assembled puzzle segments. The solver proposed exhibits state-of-t... | [
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2 | Exploring Compositional High Order Pattern Potentials for Structured Output Learning | [
"Yujia Li",
"Daniel Tarlow",
"Richard Zemel"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Li_Exploring_Compositional_High_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Li_Exploring_Compositional_High_2013_CVPR_paper.pdf | null | null | null | @InProceedings{Li_2013_ICCV_Workshops,author = {Li, Yujia and Tarlow, Daniel and Zemel, Richard},title = {Exploring Compositional High Order Pattern Potentials for Structured Output Learning},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | When modeling structured outputs such as image segmentations, prediction can be improved by accurately modeling structure present in the labels. A key challenge is developing tractable models that are able to capture complex high level structure like shape. In this work, we study the learning of a general class of patt... | [
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3 | Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration | [
"Rui Shi",
"Wei Zeng",
"Zhengyu Su",
"Hanna Damasio",
"Zhonglin Lu",
"Yalin Wang",
"Shing-Tung Yau",
"Xianfeng Gu"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Shi_Hyperbolic_Harmonic_Mapping_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Shi_Hyperbolic_Harmonic_Mapping_2013_CVPR_paper.pdf | null | null | null | @InProceedings{Shi_2013_ICCV_Workshops,author = {Shi, Rui and Zeng, Wei and Su, Zhengyu and Damasio, Hanna and Lu, Zhonglin and Wang, Yalin and Yau, Shing-Tung and Gu, Xianfeng},title = {Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration},booktitle = {Proceedings of the IEEE Conference on Computer V... | Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture. Although... | [
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4 | Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video | [
"Ravi Garg",
"Anastasios Roussos",
"Lourdes Agapito"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Garg_Dense_Variational_Reconstruction_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Garg_Dense_Variational_Reconstruction_2013_CVPR_paper.pdf | null | null | null | @InProceedings{Garg_2013_ICCV_Workshops,author = {Garg, Ravi and Roussos, Anastasios and Agapito, Lourdes},title = {Dense Variational Reconstruction of Non-rigid Surfaces from Monocular Video},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}... | This paper offers the first variational approach to the problem of dense 3D reconstruction of non-rigid surfaces from a monocular video sequence. We formulate nonrigid structure from motion ( NRS f M ) as a global variational energy minimization problem to estimate dense low-rank smooth 3D shapes for every frame along ... | [
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5 | Fusing Depth from Defocus and Stereo with Coded Apertures | [
"Yuichi Takeda",
"Shinsaku Hiura",
"Kosuke Sato"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Takeda_Fusing_Depth_from_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Takeda_Fusing_Depth_from_2013_CVPR_paper.pdf | null | null | null | @InProceedings{Takeda_2013_ICCV_Workshops,author = {Takeda, Yuichi and Hiura, Shinsaku and Sato, Kosuke},title = {Fusing Depth from Defocus and Stereo with Coded Apertures},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | In this paper we propose a novel depth measurement method by fusing depth from defocus (DFD) and stereo. One of the problems of passive stereo method is the difficulty of finding correct correspondence between images when an object has a repetitive pattern or edges parallel to the epipolar line. On the other hand, the ... | [
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6 | A Non-parametric Framework for Document Bleed-through Removal | [
"Roisin Rowley-Brooke",
"Francois Pitie",
"Anil Kokaram"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Rowley-Brooke_A_Non-parametric_Framework_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Rowley-Brooke_A_Non-parametric_Framework_2013_CVPR_paper.pdf | null | null | null | @InProceedings{Rowley-Brooke_2013_ICCV_Workshops,author = {Rowley-Brooke, Roisin and Pitie, Francois and Kokaram, Anil},title = {A Non-parametric Framework for Document Bleed-through Removal},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | This paper presents recent work on a new framework for non-blind document bleed-through removal. The framework includes image preprocessing to remove local intensity variations, pixel region classification based on a segmentation of the joint recto-verso intensity histogram and connected component analysis on the subse... | [
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7 | A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems | [
"J. Kappes",
"B. Andres",
"F. Hamprecht",
"C. Schnorr",
"S. Nowozin",
"D. Batra",
"S. Kim",
"B. Kausler",
"J. Lellmann",
"N. Komodakis",
"C. Rother"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Kappes_A_Comparative_Study_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Kappes_A_Comparative_Study_2013_CVPR_paper.pdf | null | null | null | @InProceedings{Kappes_2013_ICCV_Workshops,author = {Kappes, J. and Andres, B. and Hamprecht, F. and Schnorr, C. and Nowozin, S. and Batra, D. and Kim, S. and Kausler, B. and Lellmann, J. and Komodakis, N. and Rother, C.},title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Proble... | Seven years ago, Szeliski et al. published an influential study on energy minimization methods for Markov random fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenominal success of ... | [
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8 | Submodular Salient Region Detection | [
"Zhuolin Jiang",
"Larry S. Davis"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Jiang_Submodular_Salient_Region_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Jiang_Submodular_Salient_Region_2013_CVPR_paper.pdf | null | null | null | @InProceedings{Jiang_2013_ICCV_Workshops,author = {Jiang, Zhuolin and Davis, Larry S.},title = {Submodular Salient Region Detection},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | The problem of salient region detection is formulated as the well-studied facility location problem from operations research. High-level priors are combined with low-level features to detect salient regions. Salient region detection is achieved by maximizing a submodular objective function, which maximizes the total si... | [
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9 | Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera | [
"Lu Xia",
"J.K. Aggarwal"
] | https://openaccess.thecvf.com/content_cvpr_2013/html/Xia_Spatio-temporal_Depth_Cuboid_2013_CVPR_paper.html | https://openaccess.thecvf.com/content_cvpr_2013/papers/Xia_Spatio-temporal_Depth_Cuboid_2013_CVPR_paper.pdf | null | null | null | @InProceedings{Xia_2013_ICCV_Workshops,author = {Xia, Lu and Aggarwal, J.K.},title = {Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2013}} | Local spatio-temporal interest points (STIPs) and the resulting features from RGB videos have been proven successful at activity recognition that can handle cluttered backgrounds and partial occlusions. In this paper, we propose its counterpart in depth video and show its efficacy on activity recognition. We present a ... | [
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