IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical evaluation of dissimilarity measures for color and texture
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Cognitive maps for mobile robots-an object based approach
Robotics and Autonomous Systems
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Foundations and Trends in Robotics
Image and Vision Computing
Context-based indoor object detection as an aid to blind persons accessing unfamiliar environments
Proceedings of the international conference on Multimedia
Computer vision-based door detection for accessibility of unfamiliar environments to blind persons
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs
Entrance detection of buildings using multiple cues
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
A probabilistic framework for learning kinematic models of articulated objects
Journal of Artificial Intelligence Research
Robotics and Autonomous Systems
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An important component of human-robot interaction is the capability to associate semantic concepts with encountered locations and objects. This functionality is essential for visually guided navigation as well as location and object recognition. In this paper we focus on the problem of door detection using visual information only. Doors are frequently encountered in structured man-made environments and function as transitions between different places. We adopt a probabilistic approach for door detection, by defining the likelihood of various features for generated door hypotheses. Differing from previous approaches, the proposed model captures both the shape and appearance of the door. This is learned from a few training examples, exploiting additional assumptions about the structure of indoor environments. After the learning stage, we describe a hypothesis generation process and several approaches to evaluate the likelihood of the generated hypotheses. The approach is tested on numerous examples of indoor environment. It shows a good performance provided that the door extent in the images is sufficiently large and well supported by low level feature measurements.