Existing research in steering often focuses on static paths. However, in real-world systems, such as interfaces for lasso and video games, there are no guarantees that the paths will maintain their initial width and distance during steering. We thus explore steering movements on a dynamically varying path, widening or narrowing. To this end, we empirically studied the impact of several task parameters - including changed path width, path occupancy duration, and position - on steering through the dynamically varying path. As a result, we found different steering movements and performance to the previous steering task in the static path in terms of movement time and error rate. Additionally, we tested various previous extended models and found poor fitness results (mean \(R^2_{adjusted}=0.53\)) compared to the presented models (mean \(R^2_{adjusted}=0.92\)). We believe our model will assist GUI and game designers in evaluating their applications.

Mixed reality (MR) translation systems offer new opportunities for real-time multilingual communication by rendering translated text into users’ visual environments. However, how different interface designs impact their usability and user experience remains largely underexplored. This study presents a systematic exploration of MR translation interface designs through a Wizard-of-Oz experiment (N = 24). We compare three interface designs in both passive reading comprehension and active interaction tasks with a physical display. Our results revealed that interface design significantly affects perceived effectiveness of the system, user performance, and cognitive load. Based on these findings, we outline design recommendations and potential research directions for advancing MR translation systems in everyday use.

Video-based online educational content has been more popular nowadays. However, due to the limited communication and interaction between the learners and instructors, various problems regarding learning performance have occurred. Gaze sharing techniques received much attention as a means to address this problem, however, there still exists a lot of room for improvement. In this work-in-progress paper, we introduce some possible improvement points regarding gaze visualization strategies and report the preliminary results of our first step towards our final goal. Through a user study with 30 university students, we found the feasibility of the prototype system and the future directions of our research.

In this paper, we propose a new method to measure the visualness of a concept. The visualness of a concept is generally defined as what extent a concept has visual characteristics. Even though the visualness of a concept is important and useful for various image search tasks, it has not received much spotlight yet. In this work, we especially focus on how to measure the visualness of a complex concept such as "round table", "dry bed" rather than a simple concept like "ball", "apple". To measure the visualness, we first collect sample images of a complex concept using web image search engines, and then group the images based on the visual features. Finally, we compute visual purity and weighted entropy of the clusters, which will act as a visualness score for the concept. Through various experiments, we show and discuss interesting results about the visualness of a concept.

How to effectively summarize a large-scale image collection is still an important and open problem. In this paper, we propose a novel method to effectively generate a summary of the social image collection using image attractiveness learned from the social behaviors conducted in Flickr. To this end, we exploit the note information of Flickr images. The notes of Flickr images are user generated bounding boxes with text annotations assigned on the interesting image regions. Using the visual features extracted from the images that have notes, we have generated the attractiveness models for various concepts. Finally, the attractiveness models are exploited to make a summary of the social image collection. Through various user studies on the image collections from Flickr groups, we show the feasibility of our method and discuss further directions.

In this paper, we present analytical information about Flickr notes and propose further directions of note based image search. Compared to a tag that is used for traditional social image search, Flickr note is a kind of text directly assigned on the image regions. Even though note has various information that may help intelligent social image sharing and search, there is no significant research that focuses on the potential and the impact of note for image search. In order to reveal the useful information and potential of Flickr notes, we have collected a number of images and analyzed them with regard to various aspects. Additionally, from the analytical results about Flickr notes, we show various possible research issues to which note information can be applied.