Tim’s Vermeer is a recent documentary feature film following engineer and self-described non-artist Tim Jenison’ extensive efforts to “paint a Vermeer” by means of a novel optical telescope and mirror-comparator procedure. His efforts were inspired by the controversial claim that some Western painters as early as 1420 secretly built optical devices and traced passages in projected images during the execution of some of their works, thereby achieving a novel and compelling “optical look.” The authors examine the proposed telescope optics in historical perspective, the particular visual evidence adduced in support of the comparator hypothesis, and the difficulty and efficacy of the mirror-comparator procedure as revealed by an independent artist/copyist’s attempts to replicate the procedure. Specifically, the authors find that the luminance gradient along the rear wall in the duplicate painting is far from being rare, difficult, or even “impossible” to achieve as proponents claimed; in fact, such gradients appear in numerous Old Master paintings that show no ancillary evidence of having been executed with optics. There is indeed a slight bowing of a single contour in the Vermeer original, which one would normally expect to be straight; however, the optical explanation for this bowing implies that numerous other lines would be similarly bowed, but in fact all are straight. The proposed method does not explain some ofthe most compelling “optical” evidence in Vermeer’s works suchas the small disk-shaped highlights, which appear like the blur spots that arise in an out-of-focus projected image. Likewise, the comparator-based explanations for the presence of pinprick holes at central vanishing points and the presence of underdrawings and pentimenti in several of Vermeer’s works have more plausible non-optical explanations. Finally, an independent experimentalattempt to replicate the procedure fails overall to provide support for the telescope claim. In light of these considerations and evidence, the authors conclude that it is extremely unlikely that Vermeer used the proposed mirror-comparator procedure.
We attempted to predict an individual’s preferences for movie posters using a machine-learning algorithm based on the posters’ graphic elements. We transformed perceptually essential graphic elements into features for machine learning computation. Fifteen university students participated in a survey designed to assess their movie poster designs (Nposter = 619). Based on the movie posters’ feature information and participants’ judgments, we modeled individual algorithms using an XGBoost classifier. We achieved prediction accuracies for these individual models that ranged between 44.70 and 71.70%, while the repeated human judgments ranged between 61.90 and 87.50%. We discussed technical challenges to advance prediction algorithm and summarized reflections on using machine learning-driven algorithms in creative work.