The Scale Invariant Feature Transform (SIFT) extracts relevant features from images and video frames. The extracted features are robust against luminance variations, geometrical transformations and image resolution. Due to its performances, the SIFT algorithm is of great importance in fields such as object recognition, content retrieval from image databases, robotic navigation and gesture recognition. Main drawback of the SIFT algorithm is the high computational complexity. This paper presents the development of a hardware-filtering accelerator for the implementation of SIFT-based visual search. The accelerator works in the frequency domain, operating on a block-by-block basis. This enables to work faithfully to the original Scale-Space theory, which employs non-separable Laplacian of Gaussian (LoG) filters. The targeted throughput is of ~20 fps, making the coprocessor suitable for real time processing.

Towards a frequency domain processor for real-time SIFT-based filtering

NAPOLI, ETTORE;
2016

Abstract

The Scale Invariant Feature Transform (SIFT) extracts relevant features from images and video frames. The extracted features are robust against luminance variations, geometrical transformations and image resolution. Due to its performances, the SIFT algorithm is of great importance in fields such as object recognition, content retrieval from image databases, robotic navigation and gesture recognition. Main drawback of the SIFT algorithm is the high computational complexity. This paper presents the development of a hardware-filtering accelerator for the implementation of SIFT-based visual search. The accelerator works in the frequency domain, operating on a block-by-block basis. This enables to work faithfully to the original Scale-Space theory, which employs non-separable Laplacian of Gaussian (LoG) filters. The targeted throughput is of ~20 fps, making the coprocessor suitable for real time processing.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4772629
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