scholarly journals A Study on Correcting Virtual Camera Tracking Data for Digital Compositing

2012 ◽  
Vol 17 (11) ◽  
pp. 39-46 ◽  
Author(s):  
Junsang Lee ◽  
Imgeun Lee
Keyword(s):  
Tracking Data ◽  
Camera Tracking ◽  
Virtual Camera ◽  
Digital Compositing

Validation of ball spin estimates in tennis from multi-camera tracking data

2019 ◽  
Vol 38 (3) ◽  
pp. 296-303 ◽  
Author(s):  
Olivia Cant ◽  
Stephanie Kovalchik ◽  
Rod Cross ◽  
Machar Reid
Keyword(s):  
Tracking Data ◽  
Camera Tracking

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

Attentional Bias to Untrustworthy Faces: Evidence From Eye Tracking Data

2015 ◽  
Vol 23 (9) ◽  
pp. 1508
Author(s):  
Qiandong WANG ◽  
Qinggong LI ◽  
Kaikai CHEN ◽  
Genyue FU
Keyword(s):  
Eye Tracking ◽  
Attentional Bias ◽  
Tracking Data

Max/MSP/Jitter for Music

2011 ◽  
Author(s):  
V. J Manzo
Keyword(s):  
Video Game ◽  
Music Technology ◽  
Camera Tracking ◽  
Instructional Tools ◽  
Music Therapists ◽  
Interactive Music ◽  
Pitch Tracking ◽  
Special Value ◽  
And Performance ◽  
Private Studio

In Max/MSP/Jitter for Music, expert author and music technologist V. J. Manzo provides a user-friendly introduction to a powerful programming language that can be used to write custom software for musical interaction. Through clear, step-by-step instructions illustrated with numerous examples of working systems, the book equips you with everything you need to know in order to design and complete meaningful music projects. The book also discusses ways to interact with software beyond the mouse and keyboard through use of camera tracking, pitch tracking, video game controllers, sensors, mobile devices, and more. This book will be of special value for everyone who teaches music at any level, from classroom instructors to ensemble directors to private studio instructors. Whether you want to create simple exercises for beginning performers or more complex programs for aspiring composers, this book will show you how to write customized software that can complement and even inspire your instructional objectives. No specialist foreknowledge is required to use this book to enliven your experience with music technology. Even musicians with no prior programming skills can learn to supplement their lessons with interactive instructional tools, to develop adaptive instruments to aid in composition and performance activities, and to create measurement tools with which to conduct research. This book allows you to: -Learn how to design meaningful projects for composition, performance, music therapy, instruction, and research -Understand powerful software through this accessible introduction, written for beginners -Follow along through step-by-step tutorials -Grasp the principles by downloading the extensive software examples from the companion website This book is ideal for: -Music educators at all levels looking to integrate software in instruction -Musicians interested in how software can improve their practice and performance -Music composers with an interest in designing interactive music -Music therapists looking to tailor programs to the needs of specific groups or individuals And all who are interested in music technology. Visit the companion website at www.oup.com/us/maxmspjitter

scholarly journals Mining reading patterns from eye-tracking data: method and demonstration

2019 ◽  
Vol 19 (2) ◽  
pp. 345-369 ◽  
Author(s):  
Constantina Ioannou ◽  
Indira Nurdiani ◽  
Andrea Burattin ◽  
Barbara Weber
Keyword(s):  
Eye Tracking ◽  
Tracking Data ◽  
Reading Patterns

The Accuracy Assessment of Precise Orbits Computed from Doppler Tracking Data

1997 ◽  
Vol 45 (4) ◽  
pp. 451-469
Author(s):  
George W. Davis ◽  
John C. Ries ◽  
Byron D. Tapley
Keyword(s):  
Accuracy Assessment ◽  
Tracking Data ◽  
Doppler Tracking

scholarly journals Classifying Eye-Tracking Data Using Saliency Maps

2021 ◽  
Author(s):  
Shafin Rahman ◽  
Sejuti Rahman ◽  
Omar Shahid ◽  
Md. Tahmeed Abdullah ◽  
Jubair Ahmed Sourov
Keyword(s):  
Eye Tracking ◽  
Tracking Data ◽  
Saliency Maps

Female Voice Agents in Fully Autonomous Vehicles Are Not Only More Likeable and Comfortable, But Also More Competent

2020 ◽  
Vol 64 (1) ◽  
pp. 1033-1037
Author(s):  
Jiayuan Dong ◽  
Emily Lawson ◽  
Jack Olsen ◽  
Myounghoon Jeon
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Preference Ranking ◽  
Uncanny Valley ◽  
Tracking Data ◽  
Effective Solution ◽  
User Perception ◽  
Future Studies ◽  
Underlying Mechanisms ◽  
The Voice

Driving agents can provide an effective solution to improve drivers’ trust in and to manage interactions with autonomous vehicles. Research has focused on voice-agents, while few have explored robot-agents or the comparison between the two. The present study tested two variables - voice gender and agent embodiment, using conversational scripts. Twenty participants experienced autonomous driving using the simulator for four agent conditions and filled out subjective questionnaires for their perception of each agent. Results showed that the participants perceived the voice only female agent as more likeable, more comfortable, and more competent than other conditions. Their final preference ranking also favored this agent over the others. Interestingly, eye-tracking data showed that embodied agents did not add more visual distractions than the voice only agents. The results are discussed with the traditional gender stereotype, uncanny valley, and participants’ gender. This study can contribute to the design of in-vehicle agents in the autonomous vehicles and future studies are planned to further identify the underlying mechanisms of user perception on different agents.

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