Recursive 3-D Road and Relative Ego-State Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Recognition by functional parts
Computer Vision and Image Understanding - Special issue of funtion-based vision
Physics-based visual understanding
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Integrated Person Tracking Using Stereo, Color, and Pattern Detection
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Gesture Modeling and Recognition Using Finite State Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Probabilistic visual learning for object detection
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Scene Recognition Based on Relationship between Human Actions and Objects
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Learning membership functions in a function-based object recognition system
Journal of Artificial Intelligence Research
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
IEEE Intelligent Systems
Foundations and Trends® in Computer Graphics and Vision
Housewives or technophiles?: understanding domestic robot owners
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Learning Functional Object-Categories from a Relational Spatio-Temporal Representation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Abstraction and generalization of 3D structure for recognition in large intra-class variation
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
An automated vision based on-line novel percept detection method for a mobile robot
Robotics and Autonomous Systems
Hi-index | 0.00 |
The essence of the signal-to-symbol problem consists of associating a symbolic description of an object (e.g., a chair) to a signal (e.g., an image) that captures the real object. Robots that interact with humans in natural environments must be able to solve this problem correctly and robustly. However, the problem of providing complete object models a priori to a robot so that it can understand its environment from any viewpoint is extremely difficult to solve. Additionally, many objects have different uses which in turn can cause ambiguities when a robot attempts to reason about the activities of a human and their interactions with those objects. In this paper, we build upon the fact that robots that co-exist with humans should have the ability of observing humans using the different objects and learn the corresponding object definitions. We contribute an object recognition algorithm, FOCUS, that is robust to the variations of signals, combines structure and function of an object, and generalizes to multiple similar objects. FOCUS, which stands for Finding Object Classification through Use and Structure, combines an activity recognizer capable of capturing how an object is used with a traditional visual structure processor. FOCUS learns structural properties (visual features) of objects by knowing first the object's affordance properties and observing humans interacting with that object with known activities. The strength of the method relies on the fact that we can define multiple aspects of an object model, i.e., structure and use, that are individually robust but insufficient to define the object, but can do when combined.