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Computer Vision and Image Understanding
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ARTag, a Fiducial Marker System Using Digital Techniques
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ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Strategies for Object Manipulation using Foveal and Peripheral Vision
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Efficient Discriminant Viewpoint Selection for Active Bayesian Recognition
International Journal of Computer Vision
Fourier tags: Smoothly degradable fiducial markers for use in human-robot interaction
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention
International Journal of Robotics Research
Peripheral-foveal vision for real-time object recognition and tracking in video
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Comparison of local image descriptors for full 6 degree-of-freedom pose estimation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Monocular 3D scene modeling and inference: understanding multi-object traffic scenes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Learning methods for generic object recognition with invariance to pose and lighting
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
FAW for multi-exposure fusion features
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Semantic structure from motion: a novel framework for joint object recognition and 3d reconstruction
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
3D object detection with multiple kinects
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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This paper presents a novel approach for labeling objects based on multiple spatially-registered images of a scene. We argue that such a multi-view labeling approach is a better fit for applications such as robotics and surveillance than traditional object recognition where only a single image of each scene is available. To encourage further study in the area, we have collected a data set of well-registered imagery for many indoor scenes and have made this data publicly available. Our multiview labeling approach is capable of improving the results of a wide variety of image-based classifiers, and we demonstrate this by producing scene labelings based on the output of both the Deformable Parts Model of [1] as well as a method for recognizing object contours which is similar to chamfer matching. Our experimental results show that labeling objects based on multiple viewpoints leads to a significant improvement in performance when compared with single image labeling.