In many applications today user interaction is moving away from mouse and pens and is becoming pervasive and much more physical and tangible New emerging interaction nologies allow developing and experimenting with new teraction methods on the long way to providing intuitive man computer interaction In this paper, we aim at zing gestures to interact with an application and present the design and evaluation of our sensor d gesture on As input device we employ the W r (W te) which recently gained much attention world wide We use the Wiimote's acceleration sensor independent of the g ming console for gesture recognition The system allows the training of arbitrary gestures by users which can then be called for interacting with systems like photo browsing on a home TV The developed library exploits W r data and employs a hidden Markov model for training and gnizing user n gestures Our evaluation shows that we can already recognize gestures with a small number of ning samples In addition to the gesture recognition we also present our experiences with the W r and the plementation of the gesture recognition The system forms the basis for our ongoing work on multimodal intuitive dia browsing and are available to other researchers in the Author
Figure 1: (Left) 1024 points with constant density in a toroidal square and its spectral analysis to the right; (Center) 2048 points with the density function ρ = e (−20x 2 −20y 2 ) + 0.2 sin 2 (πx) sin 2 (πy); (Right) 4096 points with a density function extracted from a grayscale image. AbstractWe present a new general-purpose method for optimizing existing point sets. The resulting distributions possess high-quality blue noise characteristics and adapt precisely to given density functions. Our method is similar to the commonly used Lloyd's method while avoiding its drawbacks. We achieve our results by utilizing the concept of capacity, which for each point is determined by the area of its Voronoi region weighted with an underlying density function. We demand that each point has the same capacity. In combination with a dedicated optimization algorithm, this capacity constraint enforces that each point obtains equal importance in the distribution. Our method can be used as a drop-in replacement for Lloyd's method, and combines enhancement of blue noise characteristics and density function adaptation in one operation.
In this article we revisit the problem of blue noise sampling with a strong focus on the spectral properties of the sampling patterns. Starting from the observation that oscillations in the power spectrum of a sampling pattern can cause aliasing artifacts in the resulting images, we synthesize two new types of blue noise patterns: step blue noise with a power spectrum in the form of a step function and single-peak blue noise with a wide zero-region and no oscillations except for a single peak. We study the mathematical relationship of the radial power spectrum to a spatial statistic known as the radial distribution function to determine which power spectra can actually be realized and to construct the corresponding point sets. Finally, we show that both proposed sampling patterns effectively prevent structured aliasing at low sampling rates and perform well at high sampling rates.
δ X =0.009,δ X =0.469 input δ X =0.049,δ X =0.645 1/4 iteration δ X =0.079,δ X =0.756 1/2 iteration δ X =0.772,δ X =0.865 1 iteration δ X =0.814,δ X =0.905 2 iterations δ X =0.925,δ X =0.932 63 iterationsFigure 1: Farthest-point optimization of a random point set with 1024 points. Both the global minimum distance δX and the average minimum distanceδX increase rapidly using our optimization technique. After one iteration the point set is already well-distributed. AbstractEfficient sampling often relies on irregular point sets that uniformly cover the sample space. We present a flexible and simple optimization strategy for such point sets. It is based on the idea of increasing the mutual distances by successively moving each point to the "farthest point," i.e., the location that has the maximum distance from the rest of the point set. We present two iterative algorithms based on this strategy. The first is our main algorithm which distributes points in the plane. Our experimental results show that the resulting distributions have almost optimal blue noise properties and are highly suitable for image plane sampling. The second is a variant of the main algorithm that partitions any point set into equally sized subsets, each with large mutual distances; the resulting partitionings yield improved results in more general integration problems such as those occurring in physically based rendering.
Figure 1: (Left) 1024 points with constant density in a toroidal square and its spectral analysis to the right; (Center) 2048 points with the density function ρ = e (−20x 2 −20y 2 ) + 0.2 sin 2 (πx) sin 2 (πy); (Right) 4096 points with a density function extracted from a grayscale image. AbstractWe present a new general-purpose method for optimizing existing point sets. The resulting distributions possess high-quality blue noise characteristics and adapt precisely to given density functions. Our method is similar to the commonly used Lloyd's method while avoiding its drawbacks. We achieve our results by utilizing the concept of capacity, which for each point is determined by the area of its Voronoi region weighted with an underlying density function. We demand that each point has the same capacity. In combination with a dedicated optimization algorithm, this capacity constraint enforces that each point obtains equal importance in the distribution. Our method can be used as a drop-in replacement for Lloyd's method, and combines enhancement of blue noise characteristics and density function adaptation in one operation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.