Publications

You can also find my articles on my Google Scholar profile.

Investigating face pareidolia using DeepGaze: Bridging human and artificial perception [In Preparation]

Published in [In Preparation], 2024

This study employs DeepGaze models to investigate face pareidolia, revealing their superior ability to detect face-like patterns over standard models and highlighting challenges in explaining gaze prediction complexity. Findings underscore the importance of dataset diversity and reveal nuances in modeling individual versus collective gaze patterns in understanding human visual perception.

Recommended citation: Gupta, P., & Dobs, K. (2024). "Investigating face pareidolia using deepgaze: Bridging human and artificial perception [In Preparation]."

Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition [Under Review]

Published in PLoS computational biology [Under Review], 2023

Using deep convolutional neural networks (CNNs) and magnetoencephalography (MEG), this study investigates the neural basis of face pareidolia, showing that initial misidentification of faces in inanimate objects is a byproduct of the brain’s optimization for face and object recognition. The research reveals that while early stages of processing mistake pareidolia for real faces, this error is corrected in later stages through specialized face recognition optimization.

Recommended citation: Gupta, P., & Dobs, K. (2023). "Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition [Under Review]."

CNNs reveal the computational implausibility of the expertise hypothesis

Published in Iscience, 2023

This study challenges the expertise hypothesis suggesting face-specific brain mechanisms are domain-general, showing neural networks optimized for generic object categorization outperform those for face recognition in expert object discrimination. It highlights the computational implausibility of domain-general mechanisms being as effective as face-specific ones in specialized tasks.

Recommended citation: Kanwisher, N., Gupta, P., & Dobs, K. (2023). "CNNs reveal the computational implausibility of the expertise hypothesis." Iscience, 26(2).

Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement

Published in IFAC-PapersOnLine, 2022

This paper presents a digital and big data analytics approach to quantify metal cutting tool wear, employing a pipeline of deep learning for processing images and a rule-based method for measuring wear along the cutting edge. The automated system enables inline tool wear detection and measurement within CNC machining applications.

Recommended citation: Holst, C., Yavuz, T. B., Gupta, P., Ganser, P., & Bergs, T. (2022). "Deep learning and rule-based image processing pipeline for automated metal cutting tool wear detection and measurement." IFAC-PapersOnLine, 55(2), 534–539.

Digital image processing with deep learning for automated cutting tool wear detection

Published in Procedia Manufacturing, 2020

This study explores the application of deep learning in digital image processing for the detection of wear on cutting tools, with a focus on detection. Measurement is considered in the next paper.

Recommended citation: Bergs, T., Holst, C., Gupta, P., & Augspurger, T. (2020). "Digital image processing with deep learning for automated cutting tool wear detection." JProcedia Manufacturing, 48, 947–958.