Sketch-and-test: picture-centered research with p5.js assisted crowdsourcing
April 17, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Maarten W. A. Wijntjes, Mitchell van Zuijlen
arXiv ID
2004.08198
Category
cs.HC: Human-Computer Interaction
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Relating human judgements to pictures is central to a wide variety of scientific disciplines. Pictures are used to evoke and study faculties of the human mind, while human input is used to label, understand and model pictorial representations. Human input is often collected through online crowdsourcing experiments. This paper discusses the usage of crowdsourcing in two major branches of picture-centered research, human and computer vision, and identifies novel directions such as art history and design. We demonstrate that a wide variety of experiments can be conducted by using p5.js, a library originally intended to facilitate visual creation. We report five complementary experimental paradigms to illustrated the accessibility and versatility of p5.js: Change blindness, BubbleView, 3D shape perception, Composition, and Perspective reconstruction. Results reveal that literature findings can be reproduced and novel insights can easily be achieved with the p5.js library. The creative freedom of p5.js combined with low threshold access to crowdsourcing seems like a powerful combination for all picture-centred research areas: perception, design, art history, communication, and beyond.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted