Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Geometric and Illumination Invariants for Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Context-based vision system for place and object recognition
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
A service-oriented middleware for building context-aware services
Journal of Network and Computer Applications
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
CSIFT: A SIFT Descriptor with Color Invariant Characteristics
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
IEEE Transactions on Information Technology in Biomedicine
Towards analytical evaluation of human machine interfaces developed in the context of smart homes
Interacting with Computers
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Services for smart home share a fundamental problem--object recognition, which is challenging because of complex background and appearance variation of object. In this paper we develop a framework of object recognition for smart home integrating SIFT (scale invariant feature transform) and context knowledge of home environment. The context knowledge includes the structure and settings of a smart home, knowledge of cameras, illumination, and location. We counteract sudden significant illumination change by trained support vector machine (SVM) and use the knowledge of home settings to define the region for multiple view registration of an object. Experiments show that the trained SVM can recognize and distinguish different illumination classes which significantly facilitate object recognition.