Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126544
Ethan Rublee, V. Rabaud, K. Konolige, G. Bradski
Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone.
{"title":"ORB: An efficient alternative to SIFT or SURF","authors":"Ethan Rublee, V. Rabaud, K. Konolige, G. Bradski","doi":"10.1109/ICCV.2011.6126544","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126544","url":null,"abstract":"Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"25 1","pages":"2564-2571"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87290872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126298
Zhihu Chen, Kwan-Yee Kenneth Wong, Y. Matsushita, Xiaolong Zhu, Miaomiao Liu
In this paper, we introduce a novel method for depth acquisition based on refraction of light. A scene is captured twice by a fixed perspective camera, with the first image captured directly by the camera and the second by placing a transparent medium between the scene and the camera. A depth map of the scene is then recovered from the displacements of scene points in the images. Unlike other existing depth from refraction methods, our method does not require the knowledge of the pose and refractive index of the transparent medium, but can recover them directly from the input images. We hence call our method self-calibrating depth from refraction. Experimental results on both synthetic and real-world data are presented, which demonstrate the effectiveness of the proposed method.
{"title":"Self-calibrating depth from refraction","authors":"Zhihu Chen, Kwan-Yee Kenneth Wong, Y. Matsushita, Xiaolong Zhu, Miaomiao Liu","doi":"10.1109/ICCV.2011.6126298","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126298","url":null,"abstract":"In this paper, we introduce a novel method for depth acquisition based on refraction of light. A scene is captured twice by a fixed perspective camera, with the first image captured directly by the camera and the second by placing a transparent medium between the scene and the camera. A depth map of the scene is then recovered from the displacements of scene points in the images. Unlike other existing depth from refraction methods, our method does not require the knowledge of the pose and refractive index of the transparent medium, but can recover them directly from the input images. We hence call our method self-calibrating depth from refraction. Experimental results on both synthetic and real-world data are presented, which demonstrate the effectiveness of the proposed method.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"2 1","pages":"635-642"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73112717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126337
Marcel Brückner, Joachim Denzler
Many new applications are enabled by combining a multi-camera system with a Time-of-Flight (ToF) camera, which is able to simultaneously record intensity and depth images. Classical approaches for self-calibration of a multi-camera system fail to calibrate such a system due to the very different image modalities. In addition, the typical environments of multi-camera systems are man-made and consist primary of only low textured objects. However, at the same time they satisfy the Manhattan-world assumption. We formulate the multi-modal sensor network calibration as a Maximum a Posteriori (MAP) problem and solve it by minimizing the corresponding energy function. First we estimate two separate 3D reconstructions of the environment: one using the pan-tilt unit mounted ToF camera and one using the multi-camera system. We exploit the Manhattan-world assumption and estimate multiple initial calibration hypotheses by registering the three dominant orientations of planes. These hypotheses are used as prior knowledge of a subsequent MAP estimation aiming to align edges that are parallel to these dominant directions. To our knowledge, this is the first self-calibration approach that is able to calibrate a ToF camera with a multi-camera system. Quantitative experiments on real data demonstrate the high accuracy of our approach.
{"title":"Exploiting the Manhattan-world assumption for extrinsic self-calibration of multi-modal sensor networks","authors":"Marcel Brückner, Joachim Denzler","doi":"10.1109/ICCV.2011.6126337","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126337","url":null,"abstract":"Many new applications are enabled by combining a multi-camera system with a Time-of-Flight (ToF) camera, which is able to simultaneously record intensity and depth images. Classical approaches for self-calibration of a multi-camera system fail to calibrate such a system due to the very different image modalities. In addition, the typical environments of multi-camera systems are man-made and consist primary of only low textured objects. However, at the same time they satisfy the Manhattan-world assumption. We formulate the multi-modal sensor network calibration as a Maximum a Posteriori (MAP) problem and solve it by minimizing the corresponding energy function. First we estimate two separate 3D reconstructions of the environment: one using the pan-tilt unit mounted ToF camera and one using the multi-camera system. We exploit the Manhattan-world assumption and estimate multiple initial calibration hypotheses by registering the three dominant orientations of planes. These hypotheses are used as prior knowledge of a subsequent MAP estimation aiming to align edges that are parallel to these dominant directions. To our knowledge, this is the first self-calibration approach that is able to calibrate a ToF camera with a multi-camera system. Quantitative experiments on real data demonstrate the high accuracy of our approach.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"191 1","pages":"945-950"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73749958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126260
Joseph Tighe, S. Lazebnik
This paper presents a framework for image parsing with multiple label sets. For example, we may want to simultaneously label every image region according to its basic-level object category (car, building, road, tree, etc.), superordinate category (animal, vehicle, manmade object, natural object, etc.), geometric orientation (horizontal, vertical, etc.), and material (metal, glass, wood, etc.). Some object regions may also be given part names (a car can have wheels, doors, windshield, etc.). We compute co-occurrence statistics between different label types of the same region to capture relationships such as “roads are horizontal,” “cars are made of metal,” “cars have wheels” but “horses have legs,” and so on. By incorporating these constraints into a Markov Random Field inference framework and jointly solving for all the label sets, we are able to improve the classification accuracy for all the label sets at once, achieving a richer form of image understanding.
{"title":"Understanding scenes on many levels","authors":"Joseph Tighe, S. Lazebnik","doi":"10.1109/ICCV.2011.6126260","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126260","url":null,"abstract":"This paper presents a framework for image parsing with multiple label sets. For example, we may want to simultaneously label every image region according to its basic-level object category (car, building, road, tree, etc.), superordinate category (animal, vehicle, manmade object, natural object, etc.), geometric orientation (horizontal, vertical, etc.), and material (metal, glass, wood, etc.). Some object regions may also be given part names (a car can have wheels, doors, windshield, etc.). We compute co-occurrence statistics between different label types of the same region to capture relationships such as “roads are horizontal,” “cars are made of metal,” “cars have wheels” but “horses have legs,” and so on. By incorporating these constraints into a Markov Random Field inference framework and jointly solving for all the label sets, we are able to improve the classification accuracy for all the label sets at once, achieving a richer form of image understanding.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"54 1","pages":"335-342"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73515628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126325
Eduard Serradell, Adriana Romero, R. Leta, C. Gatta, F. Moreno-Noguer
We present a novel approach to simultaneously reconstruct the 3D structure of a non-rigid coronary tree and estimate point correspondences between an input X-ray image and a reference 3D shape. At the core of our approach lies an optimization scheme that iteratively fits a generative 3D model of increasing complexity and guides the matching process. As a result, and in contrast to existing approaches that assume rigidity or quasi-rigidity of the structure, our method is able to retrieve large non-linear deformations even when the input data is corrupted by the presence of noise and partial occlusions. We extensively evaluate our approach under synthetic and real data and demonstrate a remarkable improvement compared to state-of-the-art.
{"title":"Simultaneous correspondence and non-rigid 3D reconstruction of the coronary tree from single X-ray images","authors":"Eduard Serradell, Adriana Romero, R. Leta, C. Gatta, F. Moreno-Noguer","doi":"10.1109/ICCV.2011.6126325","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126325","url":null,"abstract":"We present a novel approach to simultaneously reconstruct the 3D structure of a non-rigid coronary tree and estimate point correspondences between an input X-ray image and a reference 3D shape. At the core of our approach lies an optimization scheme that iteratively fits a generative 3D model of increasing complexity and guides the matching process. As a result, and in contrast to existing approaches that assume rigidity or quasi-rigidity of the structure, our method is able to retrieve large non-linear deformations even when the input data is corrupted by the presence of noise and partial occlusions. We extensively evaluate our approach under synthetic and real data and demonstrate a remarkable improvement compared to state-of-the-art.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"9 1","pages":"850-857"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74275456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126365
Kihwan Kim, Dongryeol Lee, Irfan Essa
Recognition of motions and activities of objects in videos requires effective representations for analysis and matching of motion trajectories. In this paper, we introduce a new representation specifically aimed at matching motion trajectories. We model a trajectory as a continuous dense flow field from a sparse set of vector sequences using Gaussian Process Regression. Furthermore, we introduce a random sampling strategy for learning stable classes of motions from limited data. Our representation allows for incrementally predicting possible paths and detecting anomalous events from online trajectories. This representation also supports matching of complex motions with acceleration changes and pauses or stops within a trajectory. We use the proposed approach for classifying and predicting motion trajectories in traffic monitoring domains and test on several data sets. We show that our approach works well on various types of complete and incomplete trajectories from a variety of video data sets with different frame rates.
{"title":"Gaussian process regression flow for analysis of motion trajectories","authors":"Kihwan Kim, Dongryeol Lee, Irfan Essa","doi":"10.1109/ICCV.2011.6126365","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126365","url":null,"abstract":"Recognition of motions and activities of objects in videos requires effective representations for analysis and matching of motion trajectories. In this paper, we introduce a new representation specifically aimed at matching motion trajectories. We model a trajectory as a continuous dense flow field from a sparse set of vector sequences using Gaussian Process Regression. Furthermore, we introduce a random sampling strategy for learning stable classes of motions from limited data. Our representation allows for incrementally predicting possible paths and detecting anomalous events from online trajectories. This representation also supports matching of complex motions with acceleration changes and pauses or stops within a trajectory. We use the proposed approach for classifying and predicting motion trajectories in traffic monitoring domains and test on several data sets. We show that our approach works well on various types of complete and incomplete trajectories from a variety of video data sets with different frame rates.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"36 1","pages":"1164-1171"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74557672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126506
F. Nie, Hua Wang, Heng Huang, C. Ding
In this paper, we propose a novel ℓ1-norm graph model to perform unsupervised and semi-supervised learning methods. Instead of minimizing the ℓ2-norm of spectral embedding as traditional graph based learning methods, our new graph learning model minimizes the ℓ1-norm of spectral embedding with well motivation. The sparsity produced by the ℓ1-norm minimization results in the solutions with much clearer cluster structures, which are suitable for both image clustering and classification tasks. We introduce a new efficient iterative algorithm to solve the ℓ1-norm of spectral embedding minimization problem, and prove the convergence of the algorithm. More specifically, our algorithm adaptively re-weight the original weights of graph to discover clearer cluster structure. Experimental results on both toy data and real image data sets show the effectiveness and advantages of our proposed method.
{"title":"Unsupervised and semi-supervised learning via ℓ1-norm graph","authors":"F. Nie, Hua Wang, Heng Huang, C. Ding","doi":"10.1109/ICCV.2011.6126506","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126506","url":null,"abstract":"In this paper, we propose a novel ℓ1-norm graph model to perform unsupervised and semi-supervised learning methods. Instead of minimizing the ℓ2-norm of spectral embedding as traditional graph based learning methods, our new graph learning model minimizes the ℓ1-norm of spectral embedding with well motivation. The sparsity produced by the ℓ1-norm minimization results in the solutions with much clearer cluster structures, which are suitable for both image clustering and classification tasks. We introduce a new efficient iterative algorithm to solve the ℓ1-norm of spectral embedding minimization problem, and prove the convergence of the algorithm. More specifically, our algorithm adaptively re-weight the original weights of graph to discover clearer cluster structure. Experimental results on both toy data and real image data sets show the effectiveness and advantages of our proposed method.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"14 1","pages":"2268-2273"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74597016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126495
Robert Anderson, B. Stenger, R. Cipolla
We present a multispectral photometric stereo method for capturing geometry of deforming surfaces. A novel photometric calibration technique allows calibration of scenes containing multiple piecewise constant chromaticities. This method estimates per-pixel photometric properties, then uses a RANSAC-based approach to estimate the dominant chromaticities in the scene. A likelihood term is developed linking surface normal, image intensity and photometric properties, which allows estimating the number of chromaticities present in a scene to be framed as a model estimation problem. The Bayesian Information Criterion is applied to automatically estimate the number of chromaticities present during calibration. A two-camera stereo system provides low resolution geometry, allowing the likelihood term to be used in segmenting new images into regions of constant chromaticity. This segmentation is carried out in a Markov Random Field framework and allows the correct photometric properties to be used at each pixel to estimate a dense normal map. Results are shown on several challenging real-world sequences, demonstrating state-of-the-art results using only two cameras and three light sources. Quantitative evaluation is provided against synthetic ground truth data.
{"title":"Color photometric stereo for multicolored surfaces","authors":"Robert Anderson, B. Stenger, R. Cipolla","doi":"10.1109/ICCV.2011.6126495","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126495","url":null,"abstract":"We present a multispectral photometric stereo method for capturing geometry of deforming surfaces. A novel photometric calibration technique allows calibration of scenes containing multiple piecewise constant chromaticities. This method estimates per-pixel photometric properties, then uses a RANSAC-based approach to estimate the dominant chromaticities in the scene. A likelihood term is developed linking surface normal, image intensity and photometric properties, which allows estimating the number of chromaticities present in a scene to be framed as a model estimation problem. The Bayesian Information Criterion is applied to automatically estimate the number of chromaticities present during calibration. A two-camera stereo system provides low resolution geometry, allowing the likelihood term to be used in segmenting new images into regions of constant chromaticity. This segmentation is carried out in a Markov Random Field framework and allows the correct photometric properties to be used at each pixel to estimate a dense normal map. Results are shown on several challenging real-world sequences, demonstrating state-of-the-art results using only two cameras and three light sources. Quantitative evaluation is provided against synthetic ground truth data.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"27 1","pages":"2182-2189"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74741606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126290
Dian Gong, G. Medioni
We address the problem of learning view-invariant 3D models of human motion from motion capture data, in order to recognize human actions from a monocular video sequence with arbitrary viewpoint. We propose a Spatio-Temporal Manifold (STM) model to analyze non-linear multivariate time series with latent spatial structure and apply it to recognize actions in the joint-trajectories space. Based on STM, a novel alignment algorithm Dynamic Manifold Warping (DMW) and a robust motion similarity metric are proposed for human action sequences, both in 2D and 3D. DMW extends previous works on spatio-temporal alignment by incorporating manifold learning. We evaluate and compare the approach to state-of-the-art methods on motion capture data and realistic videos. Experimental results demonstrate the effectiveness of our approach, which yields visually appealing alignment results, produces higher action recognition accuracy, and can recognize actions from arbitrary views with partial occlusion.
{"title":"Dynamic Manifold Warping for view invariant action recognition","authors":"Dian Gong, G. Medioni","doi":"10.1109/ICCV.2011.6126290","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126290","url":null,"abstract":"We address the problem of learning view-invariant 3D models of human motion from motion capture data, in order to recognize human actions from a monocular video sequence with arbitrary viewpoint. We propose a Spatio-Temporal Manifold (STM) model to analyze non-linear multivariate time series with latent spatial structure and apply it to recognize actions in the joint-trajectories space. Based on STM, a novel alignment algorithm Dynamic Manifold Warping (DMW) and a robust motion similarity metric are proposed for human action sequences, both in 2D and 3D. DMW extends previous works on spatio-temporal alignment by incorporating manifold learning. We evaluate and compare the approach to state-of-the-art methods on motion capture data and realistic videos. Experimental results demonstrate the effectiveness of our approach, which yields visually appealing alignment results, produces higher action recognition accuracy, and can recognize actions from arbitrary views with partial occlusion.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"183 1","pages":"571-578"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77376154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-06DOI: 10.1109/ICCV.2011.6126552
Dennis Park, Deva Ramanan
We describe a method for generating N-best configurations from part-based models, ensuring that they do not overlap according to some user-provided definition of overlap. We extend previous N-best algorithms from the speech community to incorporate non-maximal suppression cues, such that pixel-shifted copies of a single configuration are not returned. We use approximate algorithms that perform nearly identical to their exact counterparts, but are orders of magnitude faster. Our approach outperforms standard methods for generating multiple object configurations in an image. We use our method to generate multiple pose hypotheses for the problem of human pose estimation from video sequences. We present quantitative results that demonstrate that our framework significantly improves the accuracy of a state-of-the-art pose estimation algorithm.
{"title":"N-best maximal decoders for part models","authors":"Dennis Park, Deva Ramanan","doi":"10.1109/ICCV.2011.6126552","DOIUrl":"https://doi.org/10.1109/ICCV.2011.6126552","url":null,"abstract":"We describe a method for generating N-best configurations from part-based models, ensuring that they do not overlap according to some user-provided definition of overlap. We extend previous N-best algorithms from the speech community to incorporate non-maximal suppression cues, such that pixel-shifted copies of a single configuration are not returned. We use approximate algorithms that perform nearly identical to their exact counterparts, but are orders of magnitude faster. Our approach outperforms standard methods for generating multiple object configurations in an image. We use our method to generate multiple pose hypotheses for the problem of human pose estimation from video sequences. We present quantitative results that demonstrate that our framework significantly improves the accuracy of a state-of-the-art pose estimation algorithm.","PeriodicalId":6391,"journal":{"name":"2011 International Conference on Computer Vision","volume":"15 1","pages":"2627-2634"},"PeriodicalIF":0.0,"publicationDate":"2011-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76691504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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