Visual Fixation for 3D Video Stabilization

Visual fixation is employed by humans and some animals to keep a specific 3D location at the center of the visual gaze. Inspired by this phenomenon in nature, this paper explores the idea to transfer this mechanism to the context of video stabilization for a handheld video camera. A novel approach is presented that stabilizes a video by fixating on automatically extracted 3D target points. This approach is different from existing automatic solutions that stabilize the video by smoothing. To determine the 3D target points, the recorded scene is analyzed with a stateof- the-art structure-from-motion algorithm, which estimates camera motion and reconstructs a 3D point cloud of the static scene objects. Special algorithms are presented that search either virtual or real 3D target points, which back-project close to the center of the image for as long a period of time as possible. The stabilization algorithm then transforms the original images of the sequence so that these 3D target points are kept exactly in the center of the image, which, in case of real 3D target points, produces a perfectly stable result at the image center. Furthermore, different methods of additional user interaction are investigated. It is shown that the stabilization process can easily be controlled and that it can be combined with state-of-theart tracking techniques in order to obtain a powerful image stabilization tool. The approach is evaluated on a variety of videos taken with a hand-held camera in natural scenes.

Real-time depth camera tracking with CAD models and ICP

In recent years, depth cameras have been widely utilized in camera tracking for augmented and mixed reality. Many of the studies focus on the methods that generate the reference model simultaneously with the tracking and allow operation in unprepared environments. However, methods that rely on predefined CAD models have their advantages. In such methods, the measurement errors are not accumulated to the model, they are tolerant to inaccurate initialization, and the tracking is always performed directly in reference model's coordinate system. In this paper, we present a method for tracking a depth camera with existing CAD models and the Iterative Closest Point (ICP) algorithm. In our approach, we render the CAD model using the latest pose estimate and construct a point cloud from the corresponding depth map. We construct another point cloud from currently captured depth frame, and find the incremental change in the camera pose by aligning the point clouds. We utilize a GPGPU-based implementation of the ICP which efficiently uses all the depth data in the process. The method runs in real-time, it is robust for outliers, and it does not require any preprocessing of the CAD models. We evaluated the approach using the Kinect depth sensor, and compared the results to a 2D edge-based method, to a depth-based SLAM method, and to the ground truth. The results show that the approach is more stable compared to the edge-based method and it suffers less from drift compared to the depth-based SLAM.

Global Standards: Recent Developments betweend the Poles of Privacy and Cloud Computing

The development of the Internet has made it possible to transfer data ‘around the globe at the click of a mouse’. Especially fresh business models such as cloud computing, the newest driver to illustrate the speed and breadth of the online environment, allow this data to be processed across national borders on a routine basis. A number of factors cause the Internet to blur the lines between public and private space: Firstly, globalization and the outsourcing of economic actors entrain an ever-growing exchange of personal data. Secondly, the security pressure in the name of the legitimate fight against terrorism opens the access to a significant amount of data for an increasing number of public authorities.And finally,the tools of the digital society accompany everyone at each stage of life by leaving permanent individual and borderless traces in both space and time. Therefore, calls from both the public and private sectors for an international legal framework for privacy and data protection have become louder. Companies such as Google and Facebook have also come under continuous pressure from governments and citizens to reform the use of data. Thus, Google was not alone in calling for the creation of ‘global privacystandards’. Efforts are underway to review established privacy foundation documents. There are similar efforts to look at standards in global approaches to privacy and data protection. The last remarkable steps were the Montreux Declaration, in which the privacycommissioners appealed to the United Nations ‘to prepare a binding legal instrument which clearly sets out in detail the rights to data protection and privacy as enforceable human rights’. This appeal was repeated in 2008 at the 30thinternational conference held in Strasbourg, at the 31stconference 2009 in Madrid and in 2010 at the 32ndconference in Jerusalem. In a globalized world, free data flow has become an everyday need. Thus, the aim of global harmonization should be that it doesn’t make any difference for data users or data subjects whether data processing takes place in one or in several countries. Concern has been expressed that data users might seek to avoid privacy controls by moving their operations to countries which have lower standards in their privacy laws or no such laws at all. To control that risk, some countries have implemented special controls into their domestic law. Again, such controls may interfere with the need for free international data flow. A formula has to be found to make sure that privacy at the international level does not prejudice this principle.

Missing Links in the Proposed EU Data Protection Regulation and Cloud Computing Scenarios: A Brief Overview

Applying location-focused data protection law within the context of a location-agnostic cloud computing framework is fraught with difficulties. While the Proposed EU Data Protection Regulation has introduced a lot of changes to the current data protection framework, the complexities of data processing in the cloud involve various layers and intermediaries of actors that have not been properly addressed. This leaves some gaps in the regulation when analyzed in cloud scenarios. This paper gives a brief overview of the relevant provisions of the regulation that will have an impact on cloud transactions and addresses the missing links. It is hoped that these loopholes will be reconsidered before the final version of the law is passed in order to avoid unintended consequences.