Acrosser AR-B8020 Driver
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Acrosser AR-B8020 Driver
Specifically, this article will examine the processing requirements for vision-based tracking Acrosser AR-B8020 AR augmented realityalong with the ability of mobile platforms to address these requirements.
Future planned articles in the series will explore face recognition, gesture interfaces and other applications. The displayed material can be made either to hang disembodied in space or to coincide with maps, desktops, walls, or the Acrosser AR-B8020 of a typewriter.
Figure 1. Computer Acrosser AR-B8020 pioneer Ivan Sutherland first demonstrated a crude augmented reality prototype nearly 50 years ago.
Mobile electronics devices are ideal AR platforms in part because they include numerous sensors Acrosser AR-B8020 support various AR facilities. Embedded Vision Enhancements While inertia accelerometer, gyroscope and location GPS, Wi-Fi, magnetometer, barometer data can be used to identify the pose i. Various approaches to vision-based pose estimation exist, becoming more sophisticated and otherwise evolving over time. The most basic technique uses pre-defined fiducial markers as a Acrosser AR-B8020 of enabling the feature tracking system to determine device pose Reference 4.
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Figure 2 shows a basic system diagram for marker-based processing in AR. Figure 2.
This system flow details the multiple steps involved in implementing marker-based augmented reality. Since markers are easily detectable due to Acrosser AR-B8020 unique shape and color, and since they are located in a visual plane, they can assist in Acrosser AR-B8020 pose calculation. Their high contrast enables easier detection, and four known marker points allows for unambiguous calculation of the camera pose Reference 6.
Acrosser AR-B8020 Most markers are comprised of elementary patterns of black and white squares. The four known points are critical to enable not only marker decoding but also lens distortion correction Reference 7.
Figure 3 shows two marker examples from the popular ARToolKit open source tracking library used in the creation of AR applications. Acrosser AR-B8020 3. The ARToolKit open source library supports the development of fiducial markers.
While marker-based AR is a relatively basic approach for Acrosser AR-B8020 pose estimation, a review of the underlying embedded vision processing algorithms is especially worthwhile in the context of small, power-limited, mobile platforms. Such an understanding can also assist in extrapolating the requirements if more demanding pose estimation approaches are required in a given application. The basic vision processing steps for marker-based AR involve: Resources are also available to show you how to build a marker-based AR application for iOS or another operating system Reference 9.
This means that within 40 ms or lessthe system needs to capture each image, detect Acrosser AR-B8020 decode one or multiple markers within it, and render the scene augmentation.
ACROSSER introduces AR-B8020 SBC for X86 applications
For example, the iPhone 4 in the study documented in Reference 10 requires Algorithm optimization may allow for performance improvements. More advanced smartphones and tablets processors, combined Acrosser AR-B8020 additional algorithm optimization, would likely enable the sub ms latency Acrosser AR-B8020 mentioned as required for real-time performance. This approach is associated with the SLAM simultaneous localization and mapping techniques that have been developed in robotic research Reference SLAM attempts to first localize the camera in a map of the environment and then find the pose of the camera relative to that Acrosser AR-B8020.
A variety of feature trackers and feature matching algorithms exist for this purpose, each with varying computational requirements and other strengths and shortcomings.
Feature detectors can be roughly categorized based on the types of features they Acrosser AR-B8020 Cannycorners e. Harrisblobs e.
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MSER, Acrosser AR-B8020 maximally stable extremal regionsand patches. However, some detectors use multiple types of features. For example, the SUSAN smallest univalue segment assimilating nucleus approach employs both edge and corner detection.
Ultimately the selection and use of a particular feature detector has Acrosser AR-B8020 great deal to do with its performance and its suitability for a real-time embedded environment. Feature tracking and motion estimation both attempt to solve the problem of selecting features and then tracking them from frame to frame. This simplification is particularly necessary in the context of a mobile computing Acrosser AR-B8020 that runs off a diminutive battery. Feature Matching Matching is a more elaborate means of establishing feature correspondence, used in situations where there is higher expectation of relative motion and a greater Acrosser AR-B8020 of notable change in illumination, rotation, and other environment and feature characteristics.
The inter-frame matching goal is typically accomplished Acrosser AR-B8020 either template matching or descriptor matching techniques. Template matching is used for pixel-wise correlation, especially when one image is assumed to be a simple spatial translation of another, e.