A large dataset that contains the eye movements of N=216 programmers of different experience levels captured during two code comprehension tasks is presented. Data are grouped in terms of programming expertise (from none to high) and other demographic descriptors. Data were collected through an international collaborative effort that involved eleven research teams across eight countries on four continents. The same eye tracking apparatus and software was used for the data collection. The Eye Movements in Programming (EMIP) dataset is freely available for download. The varied metadata in the EMIP dataset provides fertile ground for the analysis of gaze behavior and may be used to make novel insights about code comprehension.

Gradient based dark pupil tracking [Timm and Barth 2011] is a simple and robust algorithm for pupil center estimation. The algorithm's time complexity of O(n4) can be tackled by applying a two-stage process (coarse center estimation followed by a windowed refinement), as well as by optimizing and parallelizing code using cache-friendly data structures, vector-extensions of modern CPU's and GPU acceleration. We could achieve a substantial speed up compared to a non-optimized implementation: 12x using vector extensions and 65x using a GPU. Further, the two-stage process combined with parameter optimization using differential evolution considerably increased the accuracy of the algorithm. We evaluated our implementation using the "Labelled pupils the wild" data set. The percentage of frames with a pixel error below 15px increased from 28% to 72%, surpassing algorithmically more complex algorithms like ExCuse (64%) and catching up with recent algorithms like PuRe (87%).

Brain-Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) can be used as hand-free control device. To utilize this control method in a real life scenario, we created a system in which a smart home is controlled by BCI. Six devices in the smart home environment could be controlled with the BCI system: The entrance door, the wardrobe, the kitchens' worktop and drawers, the light system of all the rooms and a guide light. In the presented paper, the visual stimuli for the BCI were placed at multiple screens in the smart home (placed at different locations such as the kitchen and the living room). The processing was done on one computer, located in the living room. The placement of the visual stimuli corresponded to the actuators that were controlled, e.g. the kitchen drawers were linked to the stimuli displayed in the kitchen. An online experiment was conducted where participants went through a scenario consisting of thirteen SSVEP-BCI selections in total. Eight healthy participants took part in the experiments. For BCI signal acquisition, a mobile EEG amplifier was used. Participants walked freely around the rooms during the experiment. An average accuracy of 81 % was achieved, which suggests that the SSVEP-system is suitable to control the external devices in the smart home, and that the system can be expanded to involve more actuators.

We implemented a system, called the VICON-EyeTracking Visualizer, that combines mobile eye tracking data with motion capture data to calculate and visualize the 3D gaze vector within the motion capture co-ordinate system. To ensure that both devices were temporally synchronized we used previously developed software by us. By placing reflective markers on objects in the scene, their positions are known and by spatially synchronizing both the eye tracker and the motion capture system allows us to automatically compute how many times and where fixations occur, thus overcoming the time consuming and error-prone disadvantages of the traditional manual annotation process. We evaluated our approach by comparing its outcome for a simple looking task and a more complex grasping task against the average results produced by the manual annotation process. Preliminary data reveals that the program only differed from the average manual annotation results by approximately 3 percent in the looking task with regard to the number of fixations and cumulative fixation duration on each point in the scene. In case of the more complex grasping task the results depend on the object size: for larger objects there was good agreement (less than 16 percent (or 950ms)), but this degraded for smaller objects, where there are more saccades towards object boundaries. The advantages of our approach are easy user calibration, the ability to have unrestricted body movements (due to the mobile eye-tracking system), and that it can be used with any wearable eye tracker and marker based motion tracking system. Extending existing approaches, our system is also able to monitor fixations on moving objects. The automatic analysis of gaze and movement data in complex 3D scenes can be applied to a variety of research domains, i. e., Human Computer Interaction, Virtual Reality or grasping and gesture research.

In this work, we propose a model of a content-based image retrieval system by using the new idea of combining a color segmentation with relationship trees and a corresponding tree-matching method. We retain the hierarchical relationship of the regions in an image during segmentation. Using the information of the relationships and features of the regions, we can represent the desired objects in images more accurately. In retrieval, we compare not only region features but also region relationships.