Partial Shape Recognition: A Landmark-Based Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neural network approach to robust shape classification
Pattern Recognition
Vehicle Segmentation and Classification Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning control strategies for object recognition
Symbolic visual learning
Generalizing over Aspect and Location for Rooftop Detection
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Human Shape Recognition from Snakes Using Neural Networks
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
Component-based face detection and verification
Pattern Recognition Letters
A shape-based voting algorithm for pedestrian detection and tracking
Pattern Recognition
Adaptive model-based multi-person tracking
CIS'04 Proceedings of the First international conference on Computational and Information Science
Component-based online learning for face detection and verification
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
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Detection, segmentation, and classification of specific objects are the key building blocks of a computer vision system for image analysis. This paper presents a unified model-based approach to these three tasks. It is based on using unsupervised learning to find a set of templates specific to the objects being outlined by the user. The templates are formed by averaging the shapes that belong to a particular cluster, and are used to guide a probabilistic search through the space of possible objects. The main difference from previously reported methods is the use of on-line learning, ideal for highly repetitive tasks. This results in faster and more accurate object detection, as system performance improves with continued use. Further, the information gained through clustering and user feedback is used to classify the objects for problems in which shape is relevant to the classification. The effectiveness of the resulting system is demonstrated in two applications: a medical diagnosis task using cytological images, and a vehicle recognition task.