Below is an abstract from a computer science research paper. Given this abstract, your task is to extract all unique relationships between entities of the following types:

- "USED-FOR": For instance, "Our method models user proficiency" -> ["method", "user proficiency", "USED-FOR"].
- "EVALUATE-FOR": For instance, "We evaluate our method using F1-measure" -> ["F1-measure", "method", "EVALUATE-FOR"].
- "FEATURE-OF": For instance, "prior knowledge of the model" -> ["prior knowledge", "model", "FEATURE-OF"].
- "HYPONYM-OF": For instance, "TUIT is a software library" -> ["TUIT", "software library", "HYPONYM-OF"].
- "PART-OF": For instance, "We incorporate NLU module into the system." -> ["NLU module", "system", "PART-OF"].
- "COMPARE": For instance, "Unlike the quantitative prior, the qualitative prior is often ignored" -> ["quantitative prior", "qualitative prior", "COMPARE"].
- "CONJUNCTION": For instance: "obtained from human expert or knowledge base" -> ["human expert", "knowledge base", "CONJUNCTION"].

Format your output as a json array. Each entry in the array should express a single relation, formatted as ["<Entity_A>", "<Entity_B>", "<RELATION_A_B>"]. An example output might look like:

[["neural networks", "classification", "USED-FOR"], ["neuron", "neural network", "PART-OF"]].

If no relations are found, please return an empty array [].

Abstract:

The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modelling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of the ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1-D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.
[["one-dimensional (1-D) discrete Fourier transform (DFT)", "rotation-invariant features", "USED-FOR"], ["approximate least squares estimate (ALSE) method", "parameters of the ACGMRF model", "USED-FOR"], ["singularity problem", "least squares estimate (LSE) method", "FEATURE-OF"], ["rotation-invariant features", "SAR (synthetic aperture radar", "USED-FOR"], ["anisotropic circular Gaussian MRF (ACGMRF) model", "retrieving rotation-invariant texture features", "USED-FOR"], ["Markov random field (MRF) models", "features", "USED-FOR"], ["parameters of the ACGMRF model", "rotation-invariant features", "USED-FOR"], ["anisotropic circular Gaussian MRF (ACGMRF) model", "modelling rotated image textures", "USED-FOR"], ["modelling rotated image textures", "retrieving rotation-invariant texture features", "CONJUNCTION"]]