Increasing the Robustness of Heteroassociative Morphological Memories for Practical Applications
نویسندگان
چکیده
Associative Morphological Memories are a recently proposed neural networks architecture based on the shift of the basic algebraic framework. They possess some robustness to specific noise models (erosive and dilative noise). Combining the Associative Morphological Memories with erosion/dilation scale-spaces, we achieved an increased robustness against noise. Here we report ongoing work on their application to the tasks of face localization in grayscale images and visual self-localization of a mobile robot.
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