This paper presents random dot markers (RDM) printed on transparent sheets as transparent fiducial markers. They are extremely unobstructive, and useful for developing novel user interfaces. However, the marker identification is required to be robust to observable back sides of the transparent sheets. To realize such markers, we propose a graph based framework for geometric feature based robust point matching for RDM. Instead of building one-to-one correspondences, we first build one-to-many correspondences using a 2D affinity matrix, and then globally optimize the matching assignment from the matrix. Especially, we incorporate pairwise relationship between neighboring points using local geometric descriptors into the matrix, and finally solve it with spectral matching. In the evaluation, we investigate the effectiveness of the global assignment from one-to-many correspondences, and finally show that our proposed method is enough robust to identifying overlapped markers.

We propose a texture synthesis method to enhance the trackability of a target planar object by embedding natural features into the object in the object design process. To transform an input object into an easy-to-track object in the design process, we extend an inpainting method for naturally embedding the features into the texture. First, a feature-less region in an input object is extracted based on feature distribution based segmentation. Then, the region is filled by using an inpainting method with a feature-rich region searched in an object database. By using context based region search, the inpainted region can be consistent in terms of the object context while improving the feature distribution.

Depth estimation for the lense-array type cameras is a challenging problem because of sensor noise and radiometric distortion which is a global brightness change between sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against the sensor noise and the radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial map obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. We refine an initial map by the optimization which uses the assumption that the variations of the depth values in the depth map and of the pixel values in the texture-less objects are similar. Experiments show that our method outperforms the conventional methods.

In this work, we encourage the idea of using Photogrammetric targets for object tracking in Industrial Augmented Reality (IAR). Photogrammetric targets, especially uncoded circular targets, are widely used in the industry to perform 3D surface measurements. Therefore, an AR solution based on the uncoded circular targets can improve the work flow integration by reusing existing targets and saving time. These circular targets do not have coded patterns to establish unique 2D-3D correspondences between the targets on the model and their image projections. We solve this particular problem of 2D-3D correspondence of non-coplanar circular targets from a single image. We introduce a Conic pair descriptor, which computes the Eucledian invariants from circular targets in the model space and in the image space. A three stage method is used to compare the descriptors and compute the correspondences with up to 100% precision and 89% recall rates. We are able to achieve tracking performance of 3 FPS (2560x1920 pix) to 8 FPS (640×480 pix) depending on the camera resolution and the targets present in the scene.

We propose a HOG-based hand gesture recognition system running on a mobile device. Input data is a video of hand gesture taken by a mobile device. The input data is compared with a database storing hand gesture images, which was synthesized with rotation variation. The comparison is done based on their HOG features and the gesture corresponding to the best-matched image is returned as the result. The recognition algorithm is implemented on a client-server system. The proposed system is applied to American Sign Language (ASL) alphabet recognition problem. The experimental results show that the proposed recognition algorithm improves HOG's robustness under rotation change and compare processing time with different network configurations.

The concept of augmented reality (AR) has been introduced to variety of felds in the last decade. Recent development of portable devices such as smart phone and tablet PC provides the community a lot of possible applications in AR systems. Even in the medical feld, various AR systems have recently been proposed: systems for education, pre-planning, and those in the operating room. The aim of this tutorial is to bridge the expertise between the researchers in ISMAR community and medical doctors so that researchers can contribute to the medical domain with their specialty more than one can do right now. This tutorial aims to make a bridge between researchers in augmented reality feld and medical doctors. We target an audience interested in medical augmented reality systems.

We present a medical Augmented Reality (AR) edutainment system for bone anatomy learning. This learning environment, called AR bone puzzle, is a metaphor for bone anatomy learning with AR visualization and intuitive interaction. AR bone puzzle uses its user's body as a puzzle frame and computer generated virtual bones as puzzle pieces. Users learn bone anatomy by assembling the virtual bone pieces on their body. Key features of this system are 3D AR visualization and intuitive gesture based user interaction.

Camera shake during exposure blurs the captured image. Despite several decades of studies, image deconvolution to restore a blurred image still remains an issue, particularly in blind deconvolution cases in which the actual shape of the blur is unknown. Approaches based on cepstral analysis succeeded in restoring images degraded by a uniform blur caused by a camera moving straight in a single direction. In this paper, we propose to estimate, from a single blurred image, the point spread function (PSF) caused by a normal camera undergoing a 2D curved motion, and to restore the image. To extend the traditional cepstral analysis, we derive assumptions about the PSF effects in the cepstrum domain. In a first phase, we estimate several PSF candidates from the cepstrum of a blurred image and restore the image with a fast deconvolution algorithm. In a second phase, we select the best PSF candidate by evaluating the restored images. Finally, a slower but more accurate deconvolution algorithm recovers the latent image with the chosen PSF. We validate the proposed method with synthetic and real experiments.

We propose a method for reducing out-of-focus blur caused by projector projection. In this method, we estimate the Point-Spread-Function (PSF) of the out-of-focus blur in the image projected onto the screen by comparing the screen image captured by a camera with the original image projected by the projector. According to the estimated PSF, the projected image is pre-corrected, so that the screen image can be deblurred. Experimental results show that our method can reduce out-of-focus projection blur.