Identifying Fixations in Gaze Data via Inner Density and Optimization-Reference-Cited by-同舟云学术

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Identifying Fixations in Gaze Data via Inner Density and Optimization

Published:2019-07 Issue:3 Volume:31 Page:459-476
ISSN:1091-9856
Container-title:INFORMS Journal on Computing
language:en
Short-container-title:INFORMS Journal on Computing

Author:

Trapp Andrew C.¹²^ORCID,Liu Wen³,Djamasbi Soussan¹

Affiliation:

1. Robert A. Foisie Business School, Worcester Polytechnic Institute, Worcester, Massachusetts 01609;

2. Data Science Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609

3. Data Science Program, Worcester Polytechnic Institute, Worcester, Massachusetts 01609;

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

General Engineering

Reference42 articles.

1. One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms

2. OR Forum—An Algorithmic Approach to Linear Regression

3. Finding Segmentations of Sequences

4. Blascheck T, Kurzhals K, Raschke M, Burch M, Weiskopf D, Ertl T (2014) State-of-the-art of visualization for eye tracking data. Borgo R, Maciejewski R, Viola I, eds. Eurographics Conf. Visualization (Eurographics Association, Swansea, UK).

5. Fixation identification: The optimum threshold for a dispersion algorithm

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1. Innovation in Shared Virtual Spaces for Mental Health Therapy;Communications in Computer and Information Science;2022

2. Outlier-Aware, density-Based gaze fixation identification;Omega;2021-07

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