PDF | This project was created to provide a spectacular and interactive way of Keywords — Microsoft Kinect, augmented reality, geography. technologies: two kinect systems for motion capture, depth map and real In this context we have developed an Augmented Reality (AR) application that. If you know C/C++ programming, then this book will give you the ability to develop augmented reality applications with Microsoft's Kinect. By the.
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Abstract: In this paper an Augmented Reality system for teaching key developmental abilities for individuals with. ASD is described. The system has been. reality based system using Kinect to build a virtual augmented mirror. Where there would ..  [online] webtiekittcenve.cf~battiato/CVision/Kinect. pdf. Theory: Augmented reality is a process by which data from the real world (for example video and The Kinect is a very useful tool for working with augmented.
It was found that this filter reduced jittering to the point where it was imperceptible. Finding objects for augmentation After determining the camera pose, the next step is to find planar objects, to be augmented, in this work, by synthetic column models.
Small rectangles and rectangles having inappropriate aspect ratios for columns were rejected. Figure 4 Rectangular features marked in black located within an image.
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The rectangles detected using the method described above tend to disappear and re-appear from frame to frame due to the changes in lightning conditions. From 12 , it can be seen that a Gaussian probability distribution is used for the update of the particle confidences. After the re-sampling update is performed, the particle with the largest weight is selected both for augmentation and propagation into the next state.
Open image in new window Figure 5 Tracking selected rectangles. Small circles show the estimates of their centres whereas the large circle is the particle with largest weight.
Open image in new window Augmenting participants This section describes the use of the skeleton tracking features of Kinect to augment a participant with clothes from ancient times.
Chapter 3: Rendering the Player. Chapter 4: Skeletal Motion and Face Tracking. Chapter 5: Designing a Touchable User Interface. Chapter 6: Implementing the Scene and Gameplay. Authors Rui Wang. Crystal CG , in charge of the new media interactive application design and development. He also wrote the book OpenSceneGraph 3.
In his spare time he also writes novels and is a guitar lover. Read More. Read More Reviews. Recommended for You. Leap Motion Development Essentials. Software Defined Networking with OpenFlow. Scratch 2.
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Suitability of the Kinect Sensor and Leap Motion Controller—A Literature Review
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Starting Accessible to readers adopting the topic, these titles get you into the tool or technology so that you can become an effective user.
Progressing Building on core skills you already have, these titles share solutions and expertise so you become a highly productive power user. The color camera acquires images with x pixels in 30 frames per second fps.
The infrared transmitter projects a pattern of IR dots into the physical space, and the depth information of this scene is then reconstructed by the infrared camera. According to Khoshelham, et al. By fusing the anatomical information of the preoperative images on the images captured by the attached camera, the AR visualization can thus be displayed on the HMD screens.
Figure 1 illustrates a flowchart of the proposed system. In general, the proposed system is divided into two stages: registration stage and real-time AR stage. Figure 1: An overall flowchart of the proposed system. In the registration stage, marker-free imageto- patient registration is performed. Facial data of the patient is reconstructed from the preoperative CT images, and we utilized an EICP to align the preoperative CT model with the facial data of the patient in physical space, which is extracted by the Kinect depth camera.
In the real-time AR display stage, firstly an initialization step is executed. There are two coordinate systems used here; one is the coordinate system of preoperative CT image CMI and the other is the coordinate system of the Kinect, which is considered as the world coordinate system CW, i.
The imageto- patient registration aligns the preoperative CT images to the position of the patient, and the transformation for alignment is denoted as WMIT. Figure 2: Spatial relationships among the involved coordinate systems for the image-to-patient registration. Since the color sensor of the Kinect and its depth sensor were calibrated in advanced, each pixel x, y in the color image is assigned a depth value D x, y obtained from the depth image [ 30 ].
It is assumed that the color camera of Kinect follows the pin-hole camera model [ 31 ].
K is the matrix representing the intrinsic parameters of camera, which comprises the focal lengths fx,fy and the principle point cx,cy. E is called extrinsic parameters including a rotation matrix R and a translation vector T. Here the 3-D coordinate system of Kinect color camera is considered as the world coordinate system Cw, and thus Eq. The set of point cloud represents the facial data of the patient in physical space.
Meanwhile, the color information of the same ROI, called facial template Iface, is also stored for the next step, i. In order to align these two surface data, we have designed an EICPalgorithm to accomplish the surface registration task.
In each iteration of ICP, firstly every point fi in F finds their closest point qj in Q, and a cost function C is evaluated based on the distance of each corresponding pair fi, qj.
In the EICP algorithm, we modified the cost function C of ICP by adding a weighting function to the distance of all closest corresponding pair fi, qj to deal with the problem of outliers, as shown in Eq.
The way that ICP reaches a local minimum acts as a gradient descent approach.
In each iteration of ICP, the cost function is evaluated at the current solution and then move along the direction of gradient to the local minimum. When the registration goes to convergence, we can get a transformation solution T which projects a set of points onto another set of points, and the total distance between these two point sets is the smallest. When the facial data of patient and the preoperative CT surface data have been registered, the transformation between the CT coordinate system and the real world coordinate system is thus estimated.
The flowchart is shown in Figure 3. Once the corresponding relationship is established, the 3-D coordinate of the SIFT features can thus be assigned to the corresponding features on the image of the AR camera.
In each iteration of ICP, the cost function is evaluated at the current solution and then move along the direction of gradient to the local minimum.
The results obtained with the framework are presented on 3 Resizing the stream. There are two coordinate systems used here; one is the coordinate system of preoperative CT image CMI and the other is the coordinate system of the Kinect, which is considered as the world coordinate system CW, i.
There are several ways to perform this; the following method was found to produce the best results.
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