Mahdi Abbaspour Tehrani

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.

Abstract

Distortions due to perspective projection is often de- scribed under the umbrella term of foreshortening in com- puter graphics and are treated the same way. However, a large body of literature from artists, perceptual psycholo- gists and perception scientists have shown that the percep- tion 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.

Abstract

Vibrant lighting of relief maps via high-resolution dynamic projected imagery can be a powerful tool for simulation, augmented reality, and visualization, enabling several scientific, educational and entertainment applications. This is usually achieved via multiple projectors lighting the relief map, which are then sensed by multiple cameras for geometric and color calibration. However, cumbersome semi-automatic calibration techniques have limited the applicability of such systems.

In this paper we present the first fully-automatic lighting system that registers high-resolution images on arbitrarily-shaped relief maps using a network of projectors and cameras. The devices are uncalibrated and casually aligned with the only constraint that every surface point of the relief is seen by at least two of the cameras. Our method achieves precise geometric registration followed by seamless edge blending. Quick recalibration allows changes in the position and number of the devices, as well as the surface geometry. Thus, our work can enable easy deployment of spatially augmented reality environments in various scales playing a fundamental role in increasing their popularity in several applications like geospatial analysis, architectural lighting, cultural heritage restoration, theatrical lighting and visualization. It can also be applied to any immersive VR environments on non-planar surfaces like domes or cylinders.

Abstract

Abstract

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 well- known density based algorithm) derivative, named ADBSCAN. Presented experimental results show that ADBSCAN has a better approximation to DBCSAN than the DBRS (the most well-known randomized density based algorithm) in terms of performance and quality of clustering, specially for large data sets.