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:

This paper presents an algorithm for computing optical flow, shape, motion, lighting, and albedo from an image sequence of a rigidly-moving Lambertian object under distant illumination. The problem is formulated in a manner that subsumes structure from motion, multi-view stereo, and photo-metric stereo as special cases. The algorithm utilizes both spatial and temporal intensity variation as cues: the former constrains flow and the latter constrains surface orientation; combining both cues enables dense reconstruction of both textured and texture-less surfaces. The algorithm works by iteratively estimating affine camera parameters, illumination, shape, and albedo in an alternating fashion. Results are demonstrated on videos of hand-held objects moving in front of a fixed light and camera.
[["latter", "spatial and temporal intensity variation", "HYPONYM-OF"], ["distant illumination", "rigidly-moving Lambertian object", "FEATURE-OF"], ["former", "spatial and temporal intensity variation", "HYPONYM-OF"], ["spatial and temporal intensity variation", "dense reconstruction of both textured and texture-less surfaces", "USED-FOR"], ["algorithm", "computing optical flow, shape, motion, lighting, and albedo", "USED-FOR"], ["former", "flow", "USED-FOR"], ["latter", "surface orientation", "USED-FOR"], ["estimating affine camera parameters, illumination, shape, and albedo", "algorithm", "USED-FOR"], ["former", "latter", "CONJUNCTION"], ["spatial and temporal intensity variation", "algorithm", "USED-FOR"], ["image sequence", "algorithm", "USED-FOR"], ["multi-view stereo", "photo-metric stereo", "CONJUNCTION"], ["rigidly-moving Lambertian object", "image sequence", "FEATURE-OF"], ["motion", "multi-view stereo", "CONJUNCTION"]]