The differential geometry of images (one of numerous applications but certainly an important one) involves singly isotropic space, rather than Euclidean space, the reason being that image intensity is not commensurate with the dimensions of the image plane. The Procrustes root mean square nonuniformity measure in such spaces immediately leads to a principled definition of curvedness, shape index, and orientation of local second-order structure. However, it is categorically different from the conventional curvature-based measures. One obtains a natural Euclidean metric of shape space that is readily extended to the cubic and quartic orders of approximation. In this setting, it is simple to derive the marginal probability densities for curvedness, shape index, and orientation for isotropic and anisotropic Gaussian random fields. For slight anisotropies, the marginals are much closer to those empirically found in natural images than the conventional formalisms suggest. The main articulation fits the edge and ridge structure imposed by the linear and quadric orders. The cubic and quartic structures contribute in a natural manner to these edge and ridge structures, whereas their higher-order saddle structures contribute little to the variance and can generally be ignored in applications.

Color pickers are widely used in all kinds of display applications. They vary greatly in their utility, depending on user expertise. We focus on nonprofessional, occasional users. Such users may spend from a few seconds up to a few minutes to select a color. Yet, typically they reach final accuracy within the initial 20s. Additional effort leads to random walks in the neighborhood of the target. We explore the efficaciousness of five generic color pickers, analyzing the results in terms of generic user interface properties. There is a major dichotomy between three-slider interfaces, and those that offer some form of 2D selectivity. The accuracy in rgb coordinates is about one-tenth to one-twentieth of the full scale (often 0--255 in r , g , and b ), whereas a little over 100 hues are resolved. The most efficient color picker, which is presently rarely used in popular applications, is much more efficient than the worst one. We speculate that this derives from a closer match to the user’s internal representation of color space. The study results in explicit recommendations for the implementation of user-friendly and efficient color tools.

``Pictorial space” is the mental structure that appears to be the scaffold for the visual awareness when looking “into” (as opposed to “at'') a picture. Its structure differs from the “visual space” that is the scaffold for the visual awareness when looking into the scene in front of the observer. The structure of pictorial space has been probed empirically and explored theoretically. Here we propose a framework that allows one to handle cases that have been encountered empirically, but thus far have not been explored in a formal, geometrical setting. The framework allows one to handle many idiosyncrasies of human visual observers, as well as to characterize the (frequent) individual differences in a principled manner. This opens the door to a principled formalism of the structure (e.g., quality) of the pictorial spaces evoked by various methods of presentation, as required for applications.

Human perception of sign language can serve as inspiration for the improvement of automatic recognition systems. Experiments with human signers show that sign language signs contain redundancy over time. In this article, experiments are conducted to investigate whether comparable redundancies also exist for an automatic sign language recognition system. Such redundancies could be exploited, for example, by reserving more processing resources for the more informative phases of a sign, or by discarding uninformative phases. In the experiments, an automatic system is trained and tested on isolated fragments of sign language signs. The stimuli used were similar to those of the human signer experiments, allowing us to compare the results. The experiments show that redundancy over time exists for the automatic recognizer. The central phase of a sign is the most informative phase, and the first half of a sign is sufficient to achieve a recognition performance similar to that of the entire sign. These findings concur with the results of the human signer studies. However, there are differences as well, most notably the fact that human signers score better on the early phases of a sign than the automatic system. The results can be used to improve the automatic recognizer, by using only the most informative phases of a sign as input.

In this study we compare human and machine acceptability judgments for extreme variations in sign productions. We gathered acceptability judgments of 26 signers and scores of three different automatic gesture recognition (AGR) algorithms that could potentially be used for automatic acceptability judgments, in which case the correlation between human ratings and AGR scores may serve as an dasiaacceptability performancepsila measure. We found high human-human correlations, high AGR-AGR correlations, but low human-AGR correlations. Furthermore, in a comparison between acceptability and classification performance of the different AGR methods, classification performance was found to be an unreliable predictor of acceptability performance.

This study explores issues of consistency and inspectability in usability test data analysis processes and reports. Problem reports resulting from usability tests performed by three professional usability labs in three different countries are compared. Each of the labs conducted a usability test on the same product, applying an agreed test protocol that was collaboratively developed by the labs. Each lab first analyzed their own findings as they always do in their regular professional practice. A few weeks later, they again analyzed their findings, but then everyone applied the same method (SlimDEVAN: a simplified version of DEVAN, a method developed for facilitating comparison of findings from usability tests in an academic setting). It was found that levels of agreement between labs did not improve when they all used SlimDEVAN, suggesting that there was inherent subjectivity in their analyses. It was found that consistency of single analyst teams varied considerably and that a method like SlimDEVAN can help in making the analysis process and findings more inspectable. Inspectability is helpful in comparative studies based on identified usability problems because it allows for tracing back findings to original observations as well as for laying bare the subjective parts of the data analysis.

Surfaces play an important role in visual perception. They are perceived as «(perceptual) reliefs», that are surfaces in 2 + 1D perceptual space, that is the product space of the 2D visual field and the 1D «depth dimension». It is in many respects irrelevant whether the observer views a true 3D scene or a flat (2D) picture of a scene. In both cases, the percepts are reliefs in 2 + 1D perceptual space. In the latter case, one speaks of «pictorial relief». We discuss how perceptual reliefs can be measured and which aspects of these reliefs are especially robust against day-to-day intraobserver variations, changes of viewing conditions and interobserver differences. It turns out that only aspects of the partial depth order (based on depth precedence in infinitesimal regions) are stable. Thus, features of the relief are invariants of general «relief preserving transformations» that may actually scramble depth values at different locations. This is evident from the fact that human observers can only judge depth precedence with some degree of certainty for points that are on a single slope. We discuss the formal structure of these relief invariants. Important ones are the Morse critical points and the ridges and courses of the relief

A method that treats linear neighborhood operators within a unified framework that enables linear combinations, concatenations, resolution changes, or rotations of operators to be treated in a canonical manner is presented. Various families of operators with special kinds of symmetries (such as translation, rotation, magnification) are explicitly constructed in 1-D, 2-D, and 3-D. A concept of 'order' is defined, and finite orthonormal bases of functions closely connected with the operators of various orders are constructed. Linear transformations between the various representations are considered. The method is based on two fundamental assumptions: a decrease of resolution should not introduce spurious detail, and the local operators should be self-similar under changes of resolution. These assumptions merely sum up the even more general need for homogeneity isotropy, scale invariance, and separability of independent dimensions of front-end processing in the absence of a priori information.>

In contradistinction with technical pattern recognizers, humans are very apt at the detection of regions of coherent movements in changing images. They detect coherent patches in spatiotemporal white noise at threshold signal-to-noise ratios of one hundredth! Possible physiological mechanisms that sustain this ability are discussed in terms of simple mechanistic models, and the results of psychophysical experiments are presented. These results are compatible with two different mechanistic interpretations. The main result is that the human movement detectors are completely unlike the speedometer in an automobile: they are tuned, and a whole ensemble of mechanisms, tuned to different velocities, reside at any location in the visual field. Thus you may easily see two velocity vectors simultaneously at a given place. Segregation occurs when different detectors are stimulated at each side of a border. The spatiotemporal parameters that characterize these units are found: these limit the resolution in time and space, whereas the sensitivity depends on the number of units that participate in a detection. This number may range between a few (perhaps one) to a thousand or more. Apparently, resolution can be traded against noise immunity. It is argued that technical systems, developed on a similar basis, might be useful as preprocessors of sequences of images in order to detect features of interest (coherent regions) and to suggest a first rough image segmentation.