Alireza Sadri

TL;DR: It is shown that large hyperedges are better from both a theoretical and an empirical standpoint, and a novel guided sampling strategy is proposed, based on the concept of random cluster models, that can generate large pure hyperedge size that significantly improve grouping accuracy without exponential increases in sampling costs.

TL;DR: A multi-agent system composed of several cost effective embedded Arduino systems as agents and a Raspberry-Pi® as a core agent that can reflect the non-linear material value flow without modelling the process or using RFID tags for dynamic value stream mapping (DVSM).

TL;DR: A new robust model fitting method is proposed to efficiently segment multistructure data even when they are heavily contaminated by outliers and mutual information theory is applied to fuse the model hypotheses of the same model instance.

TL;DR: In this paper, the authors proposed an effective sampling method to obtain a highly accurate approximation of the full graph required to solve multi-structural model fitting problems in computer vision, which is based on the observation that the usefulness of a graph for segmentation improves as the distribution of hypotheses (used to build the graph) approaches the distribution for the given data.

TL;DR: In this paper, the authors proposed an effective sampling method for obtaining a highly accurate approximation of the full graph, which is required to solve multi-structural model fitting problems in computer vision.