Non-parametric local transforms for computing visual correspondence
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
The spatial semantic hierarchy
Artificial Intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Bootstrap learning for place recognition
Eighteenth national conference on Artificial intelligence
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robotics and Autonomous Systems
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Curious George: An attentive semantic robot
Robotics and Autonomous Systems
Semantic place classification of indoor environments with mobile robots using boosting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
From Sensors to Human Spatial Concepts: An Annotated Data Set
IEEE Transactions on Robotics
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Testing image segmentation for topological SLAM with omnidirectional images
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Unsupervised scene classification based on context of features for a mobile robot
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
An extended-HCT semantic description for visual place recognition
International Journal of Robotics Research
Histogram of Oriented Uniform Patterns for robust place recognition and categorization
International Journal of Robotics Research
A model for the qualitative description of images based on visual and spatial features
Computer Vision and Image Understanding
PLISS: labeling places using online changepoint detection
Autonomous Robots
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Object templates for visual place categorization
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Robotics and Autonomous Systems
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In this paper we describe the problem of Visual Place Categorization (VPC) for mobile robotics, which involves predicting the semantic category of a place from image measurements acquired from an autonomous platform. For example, a robot in an unfamiliar home environment should be able to recognize the functionality of the rooms it visits, such as kitchen, living room, etc. We describe an approach to VPC based on sequential processing of images acquired with a conventional video camera. We identify two key challenges: Dealing with non-characteristic views and integrating restricted-FOV imagery into a holistic prediction. We present a solution to VPC based upon a recently-developed visual feature known as CENTRIST (CENsus TRansform hISTogram). We describe a new dataset for VPC which we have recently collected and are making publicly available. We believe this is the first significant, realistic dataset for the VPC problem. It contains the interiors of six different homes with ground truth labels. We use this dataset to validate our solution approach, achieving promising results.