A consensus-based elastic matching algorithm for mapping recall fixations onto encoding fixations in the looking-at-nothing paradigm.

We present an algorithmic method for aligning recall fixations with encoding fixations, to be used in looking-at-nothing paradigms that either record recall eye movements during silence or want to speed up data analysis with recordings of recall data during speech. The algorithm utilizes a novel consensus-based elastic matching algorithm to estimate which encoding fixations correspond to later recall fixations. This is not a scanpath comparison method, as fixation sequence order is ignored and only position configurations are used. The algorithm has three internal parameters and is reasonable stable over a wide range of parameter values. We then evaluate the performance of our algorithm by investigating whether the recalled objects identified by the algorithm correspond with independent assessments of what objects in the image are marked as subjectively important. Our results show that the mapped recall fixations align well with important regions of the images. This result is exemplified in four groups of use cases: to investigate the roles of low-level visual features, faces, signs and text, and people of different sizes, in recall of encoded scenes. The plots from these examples corroborate the finding that the algorithm aligns recall fixations with the most likely important regions in the images. Examples also illustrate how the algorithm can differentiate between image objects that have been fixated during silent recall vs those objects that have not been visually attended, even though they were fixated during encoding.

PolyCover: Shape Approximating With Discrete Surface Orientation.

We consider the problem of approximating given shapes so that the surface normals are restricted to a prescribed discrete set. Such shape approximations are commonly required in the context of manufacturing shapes. We provide an algorithm that first computes maximal interior polytopes and, then, selects a subset of offsets from the interior polytopes that cover the shape. This provides prescribed Hausdorff error approximations that use only a small number of primitives.

Computational discrimination between natural images based on gaze during mental imagery.

When retrieving image from memory, humans usually move their eyes spontaneously as if the image were in front of them. Such eye movements correlate strongly with the spatial layout of the recalled image content and function as memory cues facilitating the retrieval procedure. However, how close the correlation is between imagery eye movements and the eye movements while looking at the original image is unclear so far. In this work we first quantify the similarity of eye movements between recalling an image and encoding the same image, followed by the investigation on whether comparing such pairs of eye movements can be used for computational image retrieval. Our results show that computational image retrieval based on eye movements during spontaneous imagery is feasible. Furthermore, we show that such a retrieval approach can be generalized to unseen images.

The mean point of vergence is biased under projection.

The point of interest in three-dimensional space in eye tracking is often computed based on intersecting the lines of sight with geometry, or finding the point closest to the two lines of sight. We first start by theoretical analysis with synthetic simulations. We show that the mean point of vergence is generally biased for centrally symmetric errors and that the bias depends on the horizontal vs. vertical noise distribution of the tracked eye positions. Our analysis continues with an evaluation on real experimental data. The estimated mean vergence points seem to contain different errors among individuals but they generally show the same bias towards the observer. And it tends to be larger with an increased viewing distance. We also provided a recipe to minimize the bias, which applies to general computations of gaze estimation under projection. These findings not only have implications for choosing the calibration method in eye tracking experiments and interpreting the observed eye movements data; but also suggest to us that we shall consider the mathematical models of calibration as part of the experiment.

Measuring the Visual Salience of 3D Printed Objects.

To investigate human viewing behavior on physical realizations of 3D objects, the authors use an eye tracker with scene camera and fiducial markers on 3D objects to gather fixations on the presented stimuli. They use this data to validate assumptions regarding visual saliency that so far have experimentally only been analyzed for flat stimuli. They provide a way to compare fixation sequences from different subjects and developed a model for generating test sequences of fixations unrelated to the stimuli. Their results suggest that human observers agree in their fixations for the same object under similar viewing conditions. They also developed a simple procedure to validate computational models for visual saliency of 3D objects and found that popular models of mesh saliency based on center surround patterns fail to predict fixations.

3D-printing spatially varying BRDFs.

A new method fabricates custom surface reflectance and spatially varying bidirectional reflectance distribution functions (svBRDFs). Researchers optimize a microgeometry for a range of normal distribution functions and simulate the resulting surface's effective reflectance. Using the simulation's results, they reproduce an input svBRDF's appearance by distributing the microgeometry on the printed material's surface. This method lets people print svBRDFs on planar samples with current 3D printing technology, even with a limited set of printing materials. It extends naturally to printing svBRDFs on arbitrary shapes.