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The superpixel, as an important pre-processing technique, has been successfully used in many vision applications. Introduced is a fast superpixel method called iterative edge refinement (IER). The image was first initialised as regular grids, and then concentration was on unstable pixels and relabelling them iteratively so called unstable pixels, are edge pixels around the moving boundary. It is found that the unstable pixels decrease rapidly during the iterative process, which results in a high speed-up. Experimental results on the Berkeley BSDS500 dataset show that IER achieves a segmentation performance comparable with the state-of-the-art, and moreover, runs in real-time on a single Intel i3 CPU at 2.5 GHz.

DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing. Reasoning in DatalogMTL is, however, of high computational complexity, making implementation challenging and hindering its adoption in applications. In this paper, we present a novel approach for practical reasoning in DatalogMTL which combines materialisation (a.k.a. forward chaining) with automata-based techniques. We have implemented this approach in a reasoner called MeTeoR and evaluated its performance using a temporal extension of the Lehigh University Benchmark and a benchmark based on real-world meteorological data. Our experiments show that MeTeoR is a scalable system which enables reasoning over complex temporal rules and datasets involving tens of millions of temporal facts.

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