Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human-robot speech interface understanding inexplicit utterances using vision
CHI '04 Extended Abstracts on Human Factors in Computing Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Interactive Object Recognition System for a Helper Robot Using Photometric Invariance
IEICE - Transactions on Information and Systems
Interactive Object Recognition through Hypothesis Generation and Confirmation
IEICE - Transactions on Information and Systems
Integration of multiple methods for class and specific object recognition
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
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Service robots need to be able to recognize and identify objects located within complex backgrounds. Since no single method may work in every situation, several methods need to be combined. However, there are several cases when autonomous recognition methods fail. We propose several types of interactive recognition methods in those cases. Each one takes place at the failures of autonomous methods in different situations. We proposed four types of interactive methods such that robot may know the current situation and initiate the appropriate interaction with the user. Moreover we propose the grammar and sentence patterns for the instructions used by the user. We also propose an interactive learning process which can be used to learn or improve an object model through failures.