Change Detection in Overhead Imagery Using Neural Networks
Applied Intelligence
Spatio-Temporal Alignment of Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Combining One-Class Classifiers
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
ACM SIGGRAPH 2004 Papers
The Amsterdam Library of Object Images
International Journal of Computer Vision
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Registration of Challenging Image Pairs: Initialization, Estimation, and Decision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Sequence-to-Sequence Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Score normalization in multimodal biometric systems
Pattern Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Integrating intensity and texture differences for robust change detection
IEEE Transactions on Image Processing
Video Alignment for Change Detection
IEEE Transactions on Image Processing
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Synchronization of videos of the same scene recorded at different times is the first step in many applications related to video surveillance, remote sensing, and medical diagnosis. When a pair of corresponding frames from different videos is provided, synchronization of the rest of the frames is a relatively easy task. Unfortunately, this initial correspondence is usually not available. To avoid an exhaustive search for the initial match, most existing solutions rely either on prior information or additional hardware. It would be beneficial to have a method providing the initial match in an automated manner. In this paper, we investigate the feasibility of one--class learning for the problem of video synchronization. We propose a hybrid one--class learner that can assess a similarity score based on the visual features between two frames from different videos by combining outputs of Support Vector Machines and Replicator Neural Network. The learner first finds a small set of potentially corresponding frames. Then, the exact match is determined by minimizing the similarity error in the set. We apply proposed synchronization method to the videos of dynamic outdoor environments recorded by mobile platforms.