Critical parameters are quantitative measures of performance that may be used to determine the overall ability of a design to serve its purpose. Although critical parameters figure in almost every field of design where there is a demand for progressive improvement, they do not appear to figure sig-nificantly in the design of interactive systems. As a result, systems are designed that are recognizably different from other systems but not necessarily better at doing the job in-tended. This paper discusses the role of critical parameters in design, and illustrates their lack of use in interactive sys-tem design by presenting a number of of examples drawn from the HCI literature. It identifies a consequent need for research to establish critical parameters for applications and to build models of the performance of designs against these parameters. Some ideas are presented on how critical pa-rameters might be established for specific applications, and the paper concludes by summarising some of the benefits that might be gained from moving in this direction.
Better or Just Different? On the Benefits of Designing Interactive Systems in terms of Critical Parameters
| William Newman |
| Proceedings of DIS '97, Designing Intereactive Systems, 18-20 August 1997, Amsterdam, The Netherlands, pp 239-245 Designing Interactive Systems, 18-20 August 1997, Amsterdam, The Netherlands, pp 239-245. |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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