Aalto Interface Metrics (AIM)

Oulasvirta, Antti; Pascale, Samuli De; Koch, Janin; Langerak, Thomas; Jokinen, Jussi; Todi, Kashyap; Laine, Markku; Kristhombuge, Manoj; Zhu, Yixin; Miniukovich, Aliaksei; Palmas, Gregorio; Weinkauf, Tino

doi:10.1145/3266037.3266087

Cited by 37 publications

(12 citation statements)

References 22 publications

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“…A mapping between current task and application importance could be gathered using data-driven approaches (cf. [4]), and the perceptual costs of individual LODs could be determined using the Aalto Interface Metrics [38]. Our main focus is on developing a flexible and general approach to MR online UI adaptation.…”

Section: Application-specific Input Supplied By Content Creatorsmentioning

confidence: 99%

Context-Aware Online Adaptation of Mixed Reality Interfaces

Lindlbauer

Feit

Hilliges

2019

Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology

147

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Section: Application-specific Input Supplied By Content Creatorsmentioning

confidence: 99%

Context-Aware Online Adaptation of Mixed Reality Interfaces

Lindlbauer

Feit

Hilliges

2019

Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology

147

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“…GUIComp [LKH*20] is an authoring tool that gives various real‐time feedback to the current design. Aalto Interface Metrics [ODPK*18] is an online tool that quantitatively evaluates web designs, and the designers can easily find shortcomings in their designs. Zhao et al .…”

Section: Related Workmentioning

confidence: 99%

Modeling Visual Containment for Web Page Layout Optimization

Kikuchi

Otani

Yamaguchi

et al. 2021

Computer Graphics Forum

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Web pages have become fundamental in conveying information for companies and individuals, yet designing web page layouts remains a challenging task for inexperienced individuals despite web builders and templates. Visual containment, in which elements are grouped together and placed inside container elements, is an efficient design strategy for organizing elements in a limited display, and is widely implemented in most web page designs. Yet, visual containment has not been explicitly addressed in the research on generating layouts from scratch, which may be due to the lack of hierarchical structure. In this work, we represent such visual containment as a layout tree, and formulate the layout design task as a hierarchical optimization problem. We first estimate the layout tree from a given a set of elements, which is then used to compute tree‐aware energies corresponding to various desirable design properties such as alignment or spacing. Using an optimization approach also allows our method to naturally incorporate user intentions and create an interactive web design application. We obtain a dataset of diverse and popular real‐world web designs to optimize and evaluate various aspects of our method. Experimental results show that our method generates better quality layouts compared to the baseline method.

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“…For usability evaluation where the UI evaluated as a whole, Koch & Oulasvirta [14] did a work for evaluating layout perception of a UI based on algorithmic representation of gestalt psychology principles -which closely tied to human visual perception such as proximity [15]. Oulasvirta et al [16] also made an online service called Aalto Interface Metrics (AIM) where it provides 17 different metrics evaluation using a page of UI. Those metrics based on many different researches which has validated result on user perception and attention towards GUI (e.g color blindness, white space).…”

Section: Usability Factors Evaluationmentioning

confidence: 99%

Visual Recognition Of Graphical User Interface Components Using Deep Learning Technique

Rahmadi

Sudaryanto

2020

Jurnal Ilmu Komputer dan Informasi

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Graphical User Interface (GUI) building in software development is a process which ideally need to go through several steps. Those steps in the process start from idea or rough sketch of the GUI, then refined into visual design, implemented in coding or prototype, and finally evaluated for its function and usability to discover design problem and to get feedback from users. Those steps repeated until the GUI considered satisfactory or acceptable by the user. Computer vision technique has been researched and developed to make the process faster and easier; for example generating code for implementation, or automatic GUI testing using component images. But among those techniques, there are still few for usability testing purpose. This preliminary research attempted to make the foundation for usability testing using computer vision technique by built minimalist dataset which has images of various GUI components and used the dataset in deep learning experiment for GUI components visual recognition. The experiment results showed deep learning technique suitable for the intended task, with accuracy of 95% for recognition of two different types of components, and accuracy of 72% for six different types of component.

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Aalto Interface Metrics (AIM)

Cited by 37 publications

References 22 publications

Context-Aware Online Adaptation of Mixed Reality Interfaces

Context-Aware Online Adaptation of Mixed Reality Interfaces

Modeling Visual Containment for Web Page Layout Optimization

Visual Recognition Of Graphical User Interface Components Using Deep Learning Technique

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