In a spatially augmented reality system, multiple projectors are tiled on a complex shaped surface to create a seamless display on it. This has several applications in visualization, gaming, education and entertainment. The main challenges in creating seamless and undistorted imagery on such complex shaped surfaces are geometric registration and color correction. Prior methods that provide solutions for the spatial color variation in multi-projector displays assume rectangular overlap regions across the projectors that is possible only on flat surfaces with extremely constrained projector placement. In this article, we present a novel and fully automated method for removing color variations in a multi-projector display on arbitrary shaped smooth surfaces using a general color gamut morphing algorithm that can handle any arbitrarily shaped overlap between the projectors and assures imperceptible color variations across the display surface.

In this article we present a completely automated and scalable multi-projector registration system that allows multiple uncalibrated projectors and cameras on arbitrary shape surfaces. Our method estimates the parameters of multiple uncalibrated tiled or superimposed projectors, the extrinsic parameters of the observing cameras (with known intrinsic parameters), the shape of the illuminated 3D geometry and geometrically registers the projectors on it. This is achieved without using any fiducials, even if part of the surface is visible to only one camera. The method uses a completely automatic approach for cross-correlation and cross-validation of the device parameters and the surface geometry resulting in an accurate registration on the arbitrary unknown geometry that does not need an accurate prior calibration of each of the uncalibrated devices using physical patterns or fiducials. Estimating projector parameters allows for quick recalibration of the system in the face of projector movements, by re-estimating only the parameters of the moved projector and not the entire system. Thus, our work can enable easy deployment of spatially augmented reality environments of different sizes (from small table top objects to large immersive environments), different shapes (inside-looking-out or outside-looking in), and different configurations (tiled or superimposed) using the same proposed method.

Calibrating the intrinsic properties of a camera is one of the fundamental tasks required for a variety of computer vision and image processing tasks. The precise measurement of focal length, location of the principal point as well as distortion parameters of the lens is crucial, for example, for 3D reconstruction [27]. Although a variety of methods exist to achieve this goal, they are often cumbersome to carry out, require substantial manual interaction, expert knowledge, and a significant operating volume. We propose a novel calibration method based on the usage of directionally encoded light rays for estimating the intrinsic parameters. It enables a fully automatic calibration with a small device mounted close to the front lens element and still enables an accuracy comparable to standard methods even when the lens is focused up to infinity. Our method overcomes the mentioned limitations since it guarantees an accurate calibration without any human intervention while requiring only a limited amount of space. Besides that, the approach also allows to estimate the distance of the focal plane as well as the size of the aperture. We demonstrate the advantages of the proposed method by evaluating several camera/lens configurations using prototypical devices.

Distortions due to perspective projection is often described under the umbrella term of foreshortening in computer graphics and are treated the same way. However, a large body of literature from artists, perceptual psychologists and perception scientists have shown that the perception of these distortions is different in different situations. While the distortions themselves depend on both the depth and the orientation of the object with respect to the camera image plane, the perception of these distortions depends on other depth cues present in the image. In the absence of any depth cue or prior knowledge about the objects in the scene, the visual system finds it hard to correct the foreshortening automatically and such images need user input and external algorithmic distortion correction.In this paper, we claim that the shape distortion is more perceptible than area distortion, and quantify such perceived foreshortening as the non-uniformity across the image, of the ratio e of the differential areas of an object in the scene and its projection. We also categorize foreshortening into uniform and non-uniform foreshortening. Uniform foreshortening is perceived by our visual system as a distortion, even if e is uniform across the image, only when comparative objects of known sizes are present in the image. Non-uniform foreshortening is perceived when there is no other depth cue in the scene that can help the brain to correct for the distortion. We present a unified solution to correct these distortions in one or more non-occluded foreground objects by applying object-specific segmentation and affine transformation of the segmented camera image plane. Our method also ensures that the background undergoes minimal distortion and preserves background features during this process. This is achieved efficiently by solving Laplace's equations with Dirichlet boundary conditions, assisted by a simple and intuitive user interface.

In this demo we will demonstrate integration of general content delivery from a windows desktop to a multi-projector display of arbitrary, shape, size and resolution automatically calibrated using our calibration methods. We have developed these sophisticated completely automatic geometric and color registration techniques in our lab for deploying seamless multi-projector displays on popular non-planar surfaces (e.g. cylinders, domes, truncated domes). This work has gotten significant attention in both VR and Visualization venues in the past 5 years and this will be the first time such calibration will be integrated with content delivery.

The use of wide angle lens on commodity cameras are becoming increasingly popular. However, this leads to image distortions due to the large difference in relative orientation of the different foreground objects with respect to the camera's image plane. More importantly, these distortions are different in different parts of the image since it depends entirely on the position and orientation of the foreground object with respect to the image plane. Such distortions often manifest themselves as objects in the foreground appearing fatter than they are supposed to be or parts thereof (e.g. hands or head of people) looking disproportionately larger than the rest of them. Though there are earlier works addressing other common distortions in cameras like radial distortions, little attention has been given to this problem. In this paper, we present an effective method to remove such distortions in foreground objects minimally distorting the background. This is achieved efficiently using a mesh-based pixel displacement technique assisted by a simple and intuitive user interface design.

Clustering is the problem of finding relations in a data set in an supervised manner. These relations can be extracted using the density of a data set, where density of a data point is defined as the number of data points around it. To find the number of data points around another point, region queries are adopted. Region queries are the most expensive construct in density based algorithm, so it should be optimized to enhance the performance of density based clustering algorithms specially on large data sets. Finding the optimum set of region queries to cover all the data points has been proven to be NP-complete. This optimum set is called the skeletal points of a data set. In this paper, we proposed a generic algorithms which fires region queries at most 6 times the optimum number of region queries (has 6 as approximation factor). Also, we have extend this generic algorithm to create a DBSCAN (the most wellknown density based algorithm) derivative, named ADBSCAN. Presented experimental results show that ADBSCAN has a better approximation to DBSCAN than the DBRS-H (the most well-known randomized density based algorithm) in terms of performance and quality of clustering, specially for large data sets.