Topology representing networks
Neural Networks
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Looking at People: Sensing for Ubiquitous and Wearable Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Self-Organizing Maps
Combining Appearance and Topology for Wide Baseline Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching of medical images by self-organizing neural networks
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Object tracking with dynamic feature graph
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Graph-based Object Tracking Using Structural Pattern Recognition
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Monomodal image registration using mutual information based methods
Image and Vision Computing
Robotics and Autonomous Systems
On Self-Organizing Feature Map (SOFM) Formation by Direct Optimization Through a Genetic Algorithm
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Approximate Bayesian methods for kernel-based object tracking
Computer Vision and Image Understanding
Application of Kohonen network for automatic point correspondence in 2D medical images
Computers in Biology and Medicine
Object Detection by Keygraph Classification
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Object tracking in image sequences using point features
Pattern Recognition
International Journal of Computer Vision
Directly optimizing topology-preserving maps with evolutionary algorithms
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Fast algorithm for robust template matching with M-estimators
IEEE Transactions on Signal Processing
An Efficient Direct Approach to Visual SLAM
IEEE Transactions on Robotics
IEEE Transactions on Image Processing
Probabilistic Object Tracking With Dynamic Attributed Relational Feature Graph
IEEE Transactions on Circuits and Systems for Video Technology
Shape indexing using self-organizing maps
IEEE Transactions on Neural Networks
Evolutionary neural networks for practical applications
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Self-Organizing Map Formation with a Selectively Refractory Neighborhood
Neural Processing Letters
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper, a recently proposed evolutionary self-organizing map is extended and applied to visual tracking of objects in video sequences. The proposed approach uses a simple geometric template to track an object executing a smooth movement represented by affine transformations. The template is selected manually in the first frame and consists of a small number of keypoints and the neighborhood relations among them. The coordinates of the keypoints are used as the coordinates of the nodes of a non-regular grid defining a self-organizing map that represents the object. The weight vectors of each node in the output grid are updated by an evolutionary algorithm and used to locate the object frame by frame. Qualitative and quantitative evaluations indicate that the proposed approach present better results than those obtained by a direct method approach. Additionally, the proposed approach is evaluated under situations of partial occlusion and self-occlusion, and outliers, also presenting good results.