In order to investigate factors necessary for reproducing actual star images in a planetarium, for this article, the authors conducted a psychophysical experiment using projection stimuli generated by changing three parameters of the stars: color, luminance, and size. A reference projection pattern was designed to be faithful to the actual starry sky perceptually (rather than physically) by an experienced group with abundant astronomical observation experience. A reproduction system was constructed to project ten types of star image patterns to a planetarium dome using different parameters. Then, evaluation experiments with twenty observers were conducted. The results of the experiment indicate that the intensity of the stars was sensitive to the fidelity of the reproduction, and in either case of change (whether the star was bright or dark compared to the reference pattern), the result was a loss of fidelity. In addition, although the fidelity was improved when the size of the projected star was small, for stars that were projected larger than the reference pattern, the result was remarkably negative. As for differences in color, the evaluation results suggested that the tolerance to loss of fidelity was wide.
Abstraction in art often reflects human perception—areas of an artwork that hold the observer’s gaze longest will generally be more detailed, while peripheral areas are abstracted, just as they are mentally abstracted by humans’ physiological visual process. The authors’ artistic abstraction tool, Salience Stylize, uses Deep Learning to predict the areas in an image that the observer’s gaze will be drawn to, which informs the system about which areas to keep the most detail in and which to abstract most. The planar abstraction is done by a Random Forest Regressor, splitting the image into large planes and adding more detailed planes as it progresses, just as an artist starts with tonally limited masses and iterates to add fine details, then completed with our stroke engine. The authors evaluated the aesthetic appeal and effectiveness of the detail placement in the artwork produced by Salience Stylize through two user studies with 30 subjects.
This study focuses on real-time pedestrian detection using thermal images taken at night because a number of pedestrian–vehicle crashes occur from late at night to early dawn. However, the thermal energy between a pedestrian and the road differs depending on the season. We therefore propose the use of adaptive Boolean-map-based saliency (ABMS) to boost the pedestrian from the background based on the particular season. For pedestrian recognition, we use the convolutional neural network based pedestrian detection algorithm, you only look once (YOLO), which differs from conventional classifier-based methods. Unlike the original version, we combine YOLO with a saliency feature map constructed using ABMS as a hardwired kernel based on prior knowledge that a pedestrian has higher saliency than the background. The proposed algorithm was successfully applied to the thermal image dataset captured by moving vehicles, and its performance was shown to be better than that of other related state-of-the-art methods.
We introduce a web-based visual comparison approach for the systematic exploration of dynamic activation networks across biological datasets. Understanding the dynamics of such networks in the context of demographic factors like age is a fundamental problem in computational systems biology and neuroscience. We design visual encodings for the dynamic and community characteristics of these temporal networks. Our multi-scale approach blends nested mosaic matrices that capture temporal characteristics of the data, spatial views of the network data, Kiviat diagrams and mirror glyphs that detail the temporal behavior and community assignment of specific nodes. A top design specifically targeted at pairwise visual comparison further supports the comparative analysis of multiple dataset activations. We demonstrate the effectiveness of this approach through a case study on mouse brain network data. Domain expert feedback indicates this approach can help identify trends and anomalies in the data.
Visually induced motion sickness (VIMS) is evoked by conflicting motion sensory signals within the brain. Use of the simulator sickness questionnaire (SSQ) or postural stability measures to quantify one’s VIMS experience only measures the changes between pre- and post-experiment. The motion sickness susceptibility questionnaire (MSSQ) is widely used to measure individual’s sensitivity to motion sickness, but its applicability to VIMS has not been proven. We are introducing a novel VIMS susceptibility measure by combining measures of the subject’s “sensitivity” and “endurance” to VIMS. The proposed VIMS susceptibility measure was tested for various VIMS inducing conditions, and demonstrated its effectiveness by conducting both between-subjects and within-subject comparisons for different VIMS conditions.
Display systems suitable for virtual reality applications can prove useful for a variety of domains. The emergence of low-cost head-mounted displays reinvigorated the area of virtual reality significantly. However, there are still applications where full-scale CAVE-type display systems are better suited. Moreover, the cost of most CAVE-type display systems is typically rather high, thereby making it difficult to justify in a research setting.
This article aims at providing a design of less costly display technology combined with inexpensive input devices that implements a virtual environment paradigm suitable for such full-scale visualization and simulation tasks. The focus is on cost-effective display technology that does not break a researchers budget. The software framework utilizing these displays combines different visualization and graphics packages to create an easy-to-use software environment that can run readily on this display.
A user study was performed to evaluate the display technology and its usefulness for virtual reality tasks using an accepted measure: presence. It was found that the display technology is capable of delivering a virtual environment in which the user feels fully immersed.
In this article, a real-time object tracking method based on perceptual hashing called MHash is put forward. In order to enhance the robustness of noise and the apparent change, hash down-sampling is employed to ignore the image aspect ratio and gain low frequency information. Solving the problem of partial illumination change and occlusion, fragment-hash tracking is proposed. Overcoming the limitation of global illumination change, the mean-compared method is used. Also, integral image is applied to accelerate the calculation of the mean and confidence map to filter matching noise for the maximum inhibition. Experimental results show that MHash algorithm has very strong robustness especially for partial occlusion and illumination change, and achieves accurate tracking in a variety of sequences. The average tracking speed reaches 52.12 fps that shows high speed.
To pursue the spatial consistency in the process of colorization, unpleasing transferred color may be produced due to over-smoothing. To solve this problem, the authors propose a colorization approach based on an adaptive weighted average method to assign color from a reference color image to a target grayscale image. In this approach, support vector machine and improved simple linear iterative clustering are combined to obtain class probability distribution and classification labels with high local spatial consistency. According to the classification results, the color candidate in reference image is sought by space features matching with the corresponding class labels. Thereafter, an adaptive weighted average filter based on the class probability is applied to compute the chrominance value by averaging the neighborhood of the candidate pixel, and then the color is assigned to the corresponding pixel in grayscale image. The adaptive weight filter can ensure spatial coherency and avoid over-smoothing simultaneously. Experimental results demonstrate the superiority of our method compared with the existing methods.