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The 'open approach' is rooted in the open-source and free-software movements. Its application has spread to more fields than computer engineering. Product design is impacted as well: we observe new stakeholders and practices challenging current structured design processes and leading to industrial successes. Open-design appears to be promising yet disruptive. Moreover, its distinctive features remain unclear.This paper aims to popularize this new concept, as well as to give both researchers and practitioners an overview of current research on open-design, and its consequences on design. For this, we conducted a systematic quantitative bibliometric analysis of 624 entries corresponding to the keyword 'open-design' in the Scopus database. This supports a qualitative synthesis of scientific literature, enabling us to summarize practices falling under the umbrella term 'open-design' . As such, this paper traces the evolution of product design and the open approach. It also analyzes the impact of open-design on the design process as presented in the scientific literature. Finally, this paper develops a typology of open-design of tangible artifacts that distinguishes among three currently reported varieties of practice: do-it-yourself, meta-design, and industrial ecosystems. As the major contribution of this paper, this typology is developed as a final discussion.

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In this paper, we first introduce the fuzzy similarity measures in the context of a CBIR system. This leads to the observation of an invariance in the ranking for different similarity measures. We then propose an explanation to this phenomenon, and a larger theory about order invariance for fuzzy similarity measures. We introduce a definition for equivalence classes based on order conservation between these measures. We then study the consequences of this theory on the evaluation of document retrieval by fuzzy similarity.

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible.

Abstract. This paper presents a CBIR system that is based on a segmented representation of image content. It compares regional features using fuzzy similarity, which have been shown to be psychologically intuitive. We show that they can be aggregated to support four different type of original queries. The system also supports competitive queries to test different visual comparison measures, and lets the expert user manipulate the parameters of the functions involved.