The human visual system is capable of adapting across a very wide dynamic range of luminance levels; values up to 14 log units have been reported. However, when the bright and dark areas of a scene are presented simultaneously to an observer, the bright stimulus produces significant glare in the visual system and prevents full adaptation to the dark areas, impairing the visual capability to discriminate details in the dark areas and limiting simultaneous dynamic range. Therefore, this simultaneous dynamic range will be much smaller, due to such impairment, than the successive dynamic range measurement across various levels of steady-state adaptation. Previous indirect derivations of simultaneous dynamic range have suggested between 2 and 3.5 log units. Most recently, Kunkel and Reinhard reported a value of 3.7 log units as an estimation of simultaneous dynamic range, but it was not measured directly. In this study, simultaneous dynamic range was measured directly through a psychophysical experiment. It was found that the simultaneous dynamic range is a bright-stimulus-luminance dependent value. A maximum simultaneous dynamic range was found to be approximately 3.3 log units. Based on the experimental data, a descriptive log-linear model and a nonlinear model were proposed to predict the simultaneous dynamic range as a function of stimulus size with bright-stimulus luminance-level dependent parameters. Furthermore, the effect of spatial frequency in the adapting pattern on the simultaneous dynamic range was explored. A log parabola function, representing a traditional Contrast Sensitivity Function (CSF), fitted the simultaneous dynamic range data well.
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.
We analyzed the impact of common stereoscopic three-dimensional (S3D) depth distortion on S3D optic flow in virtual reality environments. The depth distortion is introduced by mismatches between the image acquisition and display parameters. The results show that such S3D distortions induce large S3D optic flow distortions and may even induce partial/full optic flow reversal within a certain depth range, depending on the viewer’s moving speed and the magnitude of S3D distortion. We hypothesize that the S3D optic flow distortion may be a source of intra-sensory conflict that could be a source of visually induced motion sickness.
This paper addresses the problem of face recognition using a graphical representation to identify structure that is common to pairs of images. Matching graphs are constructed where nodes correspond to local brightness gradient directions and edges are dependent on the relative orientation of the nodes. Similarity is determined from the size of maximal matching cliques in pattern pairs. The method uses a single reference face image to obtain recognition without a training stage. Results on samples from MegaFace obtain a 100% correct recognition result.