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Abstract

Recently, the rapid advancement of the IT industry has resulted in significant changes in audio-system configurations; particularly, the audio over internet protocol (AoIP) network-based audio-transmission technology has received favourable evaluations in this field. Applying the AoIP in a certain section of the multiple-cable zone is advantageous because the installation cost is lower than that for the existing systems, and the original sound is transmitted without any distortion. The existing AoIP-based technology, however, cannot control the audio-signal characteristics of every device and can only transmit multiple audio signals through a network. In this paper, the proposed Audio Network & Control Hierarchy Over peer-to-peer (Anchor) system enables all audio equipment to send and receive signals via a data network, and the receiving device can mix the signals of different IPs. Accordingly, it was possible to improve the system-application flexibility by simplifying the audio-system configuration. The research results confirmed that the received audio signals from different IPs were received, mixed, and output without errors. It is expected that Anchor will become a standard for audio-network protocols.
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Abstract

The task of generating fast and accurate three-dimensional (3D) models of objects or scenes through a sequence of non-calibrated images is an active field of research. The recent development in algorithm optimization has resulted in many automatic solutions that can provide an accurate 3D model from texture-full objects. Structure-from-motion (SfM) is an image-based method that uses discriminative point-based feature identifier, such as SIFT, to locate feature points in the images. This method faces difficulties when presented with the objects made of homogenous or texture-less surfaces. To reconstruct such surfaces a well-known technique is to apply an artificial variety by covering the surface with a random texture pattern prior to the image capturing process. In this work, we designed three series of image patterns which are tested based on the contrast and density ratio which increases from the first to the last pattern within the same series. The performance of the patterns is evaluated by reconstructing the surface of a texture-less object and comparing it with the true data. Using the best-found patterns from the experiments, a 3D model of a Moai statue is reconstructed. The experimental results demonstrate that the density and structure of a pattern highly affects the quality of the reconstruction.
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