Hybrid Inertial and Vision Tracking for Augmented Reality Registration
VR '99 Proceedings of the IEEE Virtual Reality
Fusion of Vision and Gyro Tracking for Robust Augmented Reality Registration
VR '01 Proceedings of the Virtual Reality 2001 Conference (VR'01)
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The Ubiquitous Camera: An In-Depth Study of Camera Phone Use
IEEE Pervasive Computing
Putting Objects in Perspective
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Recovering Surface Layout from an Image
International Journal of Computer Vision
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Inertial-aided KLT feature tracking for a moving camera
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Superparsing: scalable nonparametric image parsing with superpixels
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Gyro-aided feature tracking for a moving camera: fusion, auto-calibration and GPU implementation
International Journal of Robotics Research
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Inertial sensor-aligned visual feature descriptors
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We present a novel approach that allows anyone to quickly teach their smartphone how to understand the visual world around them. We achieve this visual scene understanding by leveraging a camera-phone's inertial sensors to lead to both a faster and more accurate automatic labeling of the regions of an image into semantic classes (e.g. sky, tree, building). We focus on letting a user train our system from scratch while out in the real world by annotating image regions in situ as training images are captured on a mobile device, making it possible to recognize new environments and new semantic classes on the fly. We show that our approach outperforms existing methods, while at the same time performing data collection, annotation, feature extraction, and image segment classification all on the same mobile device.