PhD project

On Probing Appearance:

Testing material-lighting interactions in an image-based canonical approach

Summary

Materials are omnipresent. Recognizing materials helps us with inferring their physical and chemical properties, for instance if they are compressible, slippery, sweet and juicy. Yet in literature, much less attention has been paid to material perception than to object perception. This dissertation presents studies on a method to systematically measure human visual perception of opaque materials and test the influence of lighting and shape on material perception.

In our studies, we applied multiple psychophysical methods such as matching, discriminating, and perceptual scaling to test the visual perception of materials for human observers.

Combining the four material modes and three lighting modes, we presented a canonical set that in combination with optical mixing supports a painterly approach in which key image features could be varied directly. With this method we were able to test and predict light-material interactions using both photographs of the real objects and computer rendered stimuli.

To conclude, our research mainly contributed to 1) the development of a novel probing method that mixes image features of the proximal stimulus in a fluent manner instead of varying the distal physical properties of the stimuli, plus a validation that it works and that it allows quantitative measurements of material perception and material-lighting interactions; 2) understanding of visual perception of opaque materials and material-light-interactions in a wide ecological variety; 3) a validated model for predicting the material dependent lighting effects for matte, specular, velvet and glittery materials; and 4) the interpretations of the material perception results in a manner relating to shape and light. Our findings can be further applied to many subjects, such as industrial design, education, e-commerce, computer graphics, and future psychophysical studies.

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For more details and reference, please use TU Delft repository.

 

Also, as an amateur footballer..

..I’m recovering from “professional” knee injuries since 2016, progressing well and now aiming to return to pitch in near future.

Fun fact 1: When people ask me to repeat my name in a phone call I always say “Fan, F-A-N, as in ‘football fan’ “. Yet in most cases they still think it is Sam.

Fun fact 2: my favourite English football club Arsenal and my hometown club Beijing Guo-An(国安) have many in common:

  • Both won quite a few domestic cup titles, not so many league titles, and none continental cup..
  • Both from capital cities and owns the best football stadium with no doubt.
  • Both rank 1st in the league table alphabetically.
  • Arsenal in English can also mean “a place where weapons and military equipments are stored”, whereas Guoan(国安) in Chinese can also be referring to the Ministry of State Security.
  • Last time I checked they both ranked 4th place in the league table…

As an early-stage researcher..

I have a multi-disciplinary background in Mechanical Engineering and Automation (B.Eng.) at Shanghai Jiao Tong University, CN, Robotics (M.Sc.) at King’s College London, UK, and Material Perception (Ph.D.) at TU Delft, NL.

In November 2013, I joined the Delft Perceptual Intelligence Lab (pi-lab.eu) as a Ph.D. candidate in the section of Human Information Communication Design (HICD) in the Faculty of Industrial Design Engineering. The Ph.D. project is within the EU FP7 Marie Curie Initial Training Network (ITN) project PRISM – Perceptual Intelligence of Illumination, Shape and Material. We also collaborated with P&G (Germany) consumer market knowledge division in a user research project to study how consumers perceive visual-tactile information for their products.

The main aim of my Ph.D. project is to measure the visual perception of materials. During my Ph.D., I developed a novel interactive probing method for purely visually and quantitatively measuring material perception and its interaction with lighting perception. For the summary of my PhD, please see here.

I am experienced in Experimental Design, Interactive Interface Design, Data Analysis, Computer Vision, 3D Modelling & Rendering, and Computer-Aided Design & Manufacturing.

In December 2019 I joined the Computational Psychology Lab as postdoc to work with Dr. Dietmar Heinke on further developing the CoRLEGO model.

Contact me via vanzh89 [at] gmail [dot] com