Abstract
This paper proposes a novel active contour model for image object recognition using neural networks as a dynamic information fusion kernel. It first learns feature fusion strategies from training data by searching for an optimal fusion model at each marching step of the active contour model. A recurrent neural network is then employed to learn the fusion strategy knowledge. The learned knowledge is then applied to guide another linear neural network to fuse the features, which determine the marching procedures of an active contour model for object recognition. We test our model on both artificial and real image data sets and compare the results to those of a standard active model, with promising outcomes.
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Cai, X., Sowmya, A. (2006). Active Contour with Neural Networks-Based Information Fusion Kernel. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_36
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DOI: https://doi.org/10.1007/11893257_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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