Nina Runge

No more Autobahn!: Scenic Route Generation Using Googles Street View

Navigation systems allow drivers to find the shortest or fastest path between two or multiple locations mostly using time or distance as input parameters. Various researchers extended traditional route planning approaches by taking into account the users preferences, such as enjoying a coastal view or alpine landscapes during a drive. Current approaches mainly rely on volunteered geographic information (VGI), such as point of interest (POI) data from OpenStreetMap, or social media data, such as geotagged photos from Flickr, to generate scenic routes. While these approaches use proximity, distribution or other spatial relationships of the data sets, they do not take into account the actual view on specific route segments. In this paper, we propose Autobahn: a system for generating scenic routes using Google Street View images to classify route segments based on their visual characteristics enhancing the driving experience. We show that this vision-based approach can complement other approaches for scenic route planning and introduce a personalized scenic route by aligning the characteristics of the route to the preferences of the user.

Keep an eye on your photos: automatic image tagging on mobile devices

In this paper we present how to tag images automatically based on the image and sensor data from a mobile device. We developed a system that computes low-level tags using the image itself and meta data. Based on these tags and previous user tags we learn high-level tags. With a client-server-implementation we source out computational expensive algorithms to recommend the tags as fast as possible. We show what are the best feature extraction methods in combination with a machine learning technique to recommend good tags.

Tag your emotions: a novel mobile user interface for annotating images with emotions

People tend to collect more and more data, this is especially true for images on mobile devices. Tagging images is a good way to sort such collections. While automatic tagging systems are often focused on the content, such as objects or persons in the image, manual annotations are very important to describe the context of an image. Often especially emotions are important, e.g., when a person reflects a situation, shows images from a very personal collection to others, or when using images to illustrate presentations. Unfortunately, manual annotation is often very boring and users are not very motivated to do so. While there are many approaches to motivate people to annotate data in a conventional way, none of them has focused on emotions. In this poster abstract, we present EmoWheel; an innovative interface to annotate images with emotional tags. We conducted a user study with 18 participants. Results show that the EmoWheel can enhance the motivation to annotate images.

Tags You Don’t Forget: Gamified Tagging of Personal Images

Mobile multi-purpose devices such as smartphones are progressively replacing digital cameras; people use their smartphones as everyday companions and increasingly take pictures in their daily life. Tagging is a way to organize huge collections of photos but raises two challenges. First, tagging (especially on mobile devices) is a boring task. Second, remembering the assigned tags is important to find images with tags. We propose gamification for more entertaining tagging. Most gamification approaches use crowd-based assessments of good or bad tags, which is a good way to prevent cheating and to not assign improper tags. However, it is not appropriate for personal images because users don’t want to share every image with the crowd. We developed and evaluated two mobile apps with gamification elements to tag images, a single-player and a multiplayer app. While both variants were more entertaining than a simple tagging app, the single-player app helps users to remember significant more tags.

MoviTouch: Mobile Movement Capability Configurations

Strong adaptability is a major requirement and challenge in the physiotherapeutic use of motion-based games for health. For adaptation tool development, tablets are a promising platform due to their similarity in affordance compared to traditional clipboards. In a comparative study, we examined three different input modalities on the tablet that allow for configuring joint angles: direct-touch, classic interface components (e.g. buttons and sliders), and a combination of both. While direct touch emerged as the least preferable modality, the results highlight the benefits of the combination of direct-touch and classic interface components as the most accessible modality for configuring joint angle ranges. Furthermore, the importance of configuring joint angles along three distinct axes and the interesting use-case of configuration tools as communication support emerged.

Using split screens to combine maps and images for pedestrian navigation

Studies in the field of pedestrian navigation have shown advantages of map–image combinations. While in a previous work, the map view was either replaced with an image view in predefined situations, or the user was required to manually switch between both views, we suggest to use a split screen displaying maps and images at the same time. We developed two variants, one employing panoramic images aligned to the mobile devices orientation and the other using simple photographs, and we evaluated both against manual switching in a field study. While none of the interfaces outperformed the other, results indicate advantages of panoramic images independent of manual switching or the split screen.