Hong, T and Kingsbury, N and Furman, MD (2011) Biologically-inspired object recognition system with features from complex wavelets. Proceedings - International Conference on Image Processing, ICIP. pp. 261-264. ISSN 1522-4880Full text not available from this repository.
In this paper, a novel cortex-inspired feed-forward hierarchical object recognition system based on complex wavelets is proposed and tested. Complex wavelets contain three key properties for object representation: shift invariance, which enables the extraction of stable local features; good directional selectivity, which simplifies the determination of image orientations; and limited redundancy, which allows for efficient signal analysis using the multi-resolution decomposition offered by complex wavelets. In this paper, we propose a complete cortex-inspired object recognition system based on complex wavelets. We find that the implementation of the HMAX model for object recognition in [1, 2] is rather over-complete and includes too much redundant information and processing. We have optimized the structure of the model to make it more efficient. Specifically, we have used the Caltech 5 standard dataset to compare with Serre's model in  (which employs Gabor filter bands). Results demonstrate that the complex wavelet model achieves a speed improvement of about 4 times over the Serre model and gives comparable recognition performance. © 2011 IEEE.
|Divisions:||Div F > Signal Processing and Communications|
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|Date Deposited:||09 Dec 2016 18:22|
|Last Modified:||22 Mar 2017 23:51|