Peñaranda

•Download as PPT, PDF•

0 likes•230 views

tsanchezro

This paper presents a set of qualitative and quantitative scores designed to assess performance of any eye movement classification algorithm. The scores are designed to provide a foundation for the eye tracking researchers to communicate about the performance validity of various eye movement classification algorithms. The paper concentrates on the five algorithms in particular: Velocity Threshold Identification (I-VT), Dispersion Threshold Identification (I-DT), Minimum Spanning Tree Identification (MST), Hidden Markov Model Identification (IHMM) and Kalman Filter Identification (I-KF). The paper presents an evaluation of the classification performance of each algorithm in the case when values of the input parameters are varied. Advantages provided by the new scores are discussed. Discussion on what is the "best" classification algorithm is provided for several applications. General recommendations for the selection of the input parameters for each algorithm are provided.

Vietnam paisatgesMANUEL RIVERA

רישום שמות מתחםhaimkarel

Double routing

proser tech

Exchange server

proser tech

M. Holovaty, Концепции автоматизированного тестирования

Alex

מכרות הגפרית בבאריhaimkarel

Goldberg Scanpath Clustering And Aggregation

Kalle

Eye tracking specialists often need to understand and represent aggregate scanning strategies, but methods to identify similar scanpaths and aggregate multiple scanpaths have been elusive. A new method is proposed here to identify scanning strategies by aggregating groups of matching scanpaths automatically. A dataset of scanpaths is first converted to sequences of viewed area names, which are then represented in a dotplot. Matching sequences in the dotplot are found with linear regressions, and then used to cluster the scanpaths hierarchically. Aggregate scanning strategies are generated for each cluster and presented in an interactive dendrogram. While the clustering and aggregation method works in a bottom-up fashion, based on pair-wise matches, a top-down extension is also described, in which a scanning strategy is first input by cursor gesture, then matched against the dataset. The ability to discover both bottom-up and top-down strategy matches provides a powerful tool for scanpath analysis, and for understanding group scanning strategies.

LD_March2010_forweb

Latino Decisions

Personal Learning Networksamspector93

Tisa social and mobile securityPrathan Phongthiproek

Tales of Symphonia InformaticaAngelfeathers

Doing Business With Aboriginal PeopleLee_Ahenakew

Social Networking Security Workshop

Prathan Phongthiproek