Learning in graphical models
Computer and Robot Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
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This paper proposes a real-time object recognition using the relational dependency among the objects that is represented by the graphical model. When we recognize the objects, it is effective to use the relational dependency in which several different objects co-exist each other. The relational dependency has been modeled by the transition matrix in the graphical model. The transition matrix precisely represents the conditional probability of object's existence at time t, given the existence of others at time t-1. We use a very fast cascaded adaboost detector in order to detect all object candidates in the image. Then, the existence probability of the object from a given object candidate is estimated by a logistic regression using the softmax function. The estimated existence probability is updated by the trained transition matrix to reflect the relational dependency of the objects. The object's existence is determined by the threshold level. Experiment results validate that the proposed method is a very fast and effective way of recognizing the objects in terms of high recognition rate and low false alarm rate.