Aspl{ü}nd's metric defined in the Logarithmic Image Processing (LIP) framework for colour and multivariate images
This work addresses pattern matching challenges in image processing for applications like computer vision, but it is incremental as it builds on existing methods for new data types.
The paper tackled the problem of extending Asplünd's metric, used for pattern matching and insensitive to lighting variations, from grey-scale to colour and multivariate images using the Logarithmic Image Processing (LIP) framework, resulting in a colour variant that is robust to noise.
Aspl{ü}nd's metric, which is useful for pattern matching, consists in a double-sided probing, i.e. the over-graph and the sub-graph of a function are probed jointly. It has previously been defined for grey-scale images using the Logarithmic Image Processing (LIP) framework. LIP is a non-linear model to perform operations between images while being consistent with the human visual system. Our contribution consists in extending the Aspl{ü}nd's metric to colour and multivariate images using the LIP framework. Aspl{ü}nd's metric is insensitive to lighting variations and we propose a colour variant which is robust to noise.