The pattern recognition and computer vision communities often employ robust methods for model fitting. In particular, high breakdown-point methods such as least median of squares (LMedS) and least trimmed squares (LTS) have often been used in situations where the data are contaminated with outliers. However, though the breakdown point of these methods can be as high as 50% (they can be robust t...

The data presented in this article are related to the research article entitled "Probabilistic representation in syllogistic reasoning: A theory to integrate mental models and heuristics" (M. Hattori, 2016) [1]. This article presents predicted data by three signature probabilistic models of syllogistic reasoning and model fitting results for each of a total of 12 experiments (N=404) in the lite...

We report current work on methods for robust fitting of ellipsoids to the shape of the human head in three-dimensional models built from laser scanner acquisitions. A starting ellipsoid is obtained from Principal Component Analysis from mesh vertices; those regions far from the surface of the ellipsoid are penalized (outlier rejection and/or damping). A first method consists in re-calculating i...

In this paper, the distributional properties and power rates of the Lz, Eci2z, and Eci4z statistics when they are used as item fit statistics were explored. The results were compared to t-transformation of Outfit and Infit mean square. Four sample sizes were selected: 100, 250, 500, and 1000 examinees. The abilities were uniform and normal with mean 0 and standard deviation 1, and uniform and n...

Marker-less motion capture systems usually rely on a 3D skin and skeleton model of the observed person. We present a system that is able to fit a template MPEG4 body model to a person from multiple views taken with a single camera. The person is observed in 6 different postures. Based on contour differences between model and person, a global nonlinear optimization method estimates the scale val...

In large-scale stochastic simulations, analysis with sufficient accuracy is often extremely time consuming. The complexity of the analysis is exacerbated with increasing dimensionality of the parameter space and sudden abruptness in the topology of the input-output response surface. This paper addresses computational issues in fitting and generating error measures of simulation metamodels, demo...

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This paper introduces a novel method to estimate the parameters of a linear dissipative wave model from noisy observations. We focus on the case of constant coefficients and an unknown localized source. These constraints are motivated by applications in computational neuroscience and in particular separation of sources in the visual cortex in optical imaging modality. The proposed method takes ...

In this paper, we propose a novel hypergraph based method (called HF) to fit and segment multi-structural data. The proposed HF formulates the geometric model fitting problem as a hypergraph partition problem based on a novel hypergraph model. In the hypergraph model, vertices represent data points and hyperedges denote model hypotheses. The hypergraph, with large and “data-determined” degrees ...