Control and optimization of human perception on virtual garment products by learning from experimental data

Textile Research Journal

Bruniaux

Zeng

et al. 2017

Self Cite

This research puts forward a novel knowledge-supported design process for obtaining personalized ready-to-wear garment products aimed at consumers with atypical morphology by using a virtual a three-dimensional-to-two-dimensional (3D-to-2D) design method. The proposed design process starts with designing a personalized garment block, which is then extended into the desired ready-to-wear garment style. The garment block is obtained by using a virtual 3D-to-2D design method. The extension and sizing of the garment block pattern into desired ready-to-wear garment patterns is performed in a 2D environment using classic methods. The proposed design solution begins with a personalized garment block, thus avoiding the complicated operations of simulating a 3D garment directly in the virtual environment. The proposed design process can be fully digitalized, which ensures the involvement of the consumer throughout the design. By repeatedly running the sequence of Design – Display – Evaluation – Adjustment, which is technically accomplished through the virtual 3D-to-2D design method and 3D virtual try-on, the designers’ patterns and principles of elaborating personalized garment products can be validated within a very short time. As a knowledge-supported design process, designers’ design ideas and principles of personalized design solution can be fully extracted to enhance satisfaction of the final product. A set of experiments and case studies validated the approach and application of the proposed knowledge-supported virtual design process, and demonstrated its efficiency.

mentioning

confidence: 99%

Design and evaluation of personalized garment block for atypical morphology using the knowledge-supported virtual simulation method

Textile Research Journal

Bruniaux

Zeng

et al. 2017

Self Cite

“…This article focuses on the development of a design knowledge base for the mask types. Based on the literature, design knowledge is tacit and subjective [7], and normally, it can be expressed as the relationship between mask perception words (modern, chic, elegant, etc.) and design elements (stand collar, X shape, O style, etc.).…”

Section: General Research Approachmentioning

confidence: 99%

“…[4] [5] [6] [7] [8] [9] [10] [11] [12] DEVELOPMENT OF MASK DESIGN KNOWLEDGE BASE BASED ON SENSORY EVALUATION AND FUZZY LOGIC…”

mentioning

confidence: 99%

Development of Mask Design Knowledge Base Based on Sensory Evaluation and Fuzzy Logic

Xue

Liu

et al. 2021

Autex Research Journal

This article focuses on the development of the mask design knowledge base, which is expected to be applied in a personalized mask design system. To realize the proposed knowledge base, a perceptual descriptive space of the mask is first developed for the description of both functional and aesthetic perceptions of a mask. The mask ontology is also developed to form the mask element matrix. Mask design knowledge is expressed as the relationship between the perceptual descriptive space and the mask ontology, which is extracted by a group of experienced designers through a sensory evaluation procedure. This relationship is then simulated by fuzzy logic tools. The proposed knowledge base has been validated that it is reliable. The personalized mask design system can be further developed with the propose mask design knowledge base.

“…The proposed system is an open system, which is capable of dynamically dealing with user's color image perceptual data and integrating the latest color trends [26] . Color design rules are extracted by designers using their professional knowledge through a series of human evaluation experiments [7] . Using these rules, CIWs developed by Kobayashi [21] can be identified in a novel multidimensional CIS defined by a set of Basic Color Sensory Attributes (BCSAs).…”

Section: Introductionmentioning

confidence: 99%

CBCRS: An open case-based color recommendation system

Knowledge-Based Systems

Zeng

Wang

et al. 2018

Self Cite

In this paper, a case-based color recommendation system (CBCRS) is proposed for online color ranges (CRs) recommendation. This system can help designers and consumers to obtain the most appropriate CR of consumer-products (e.g., garments, cars, architecture, furniture …) based on the color image perceptual data of each specific user. The proposed system is an open system, permitting to dynamically integrate new CRs by progressively learning from users' and designers' perceptual data. For this purpose, a Color Image Space (CIS) is initially established by using Basic Color Sensory Attributes (BCSAs) to obtain the color image perceptual data of both designers and consumers. Emotional Color Image Words (CIWs) representing CRs are measured in the proposed CIS through a knowledge-based Kansei evaluation process performed by designers using fuzzy aggregation operators and fuzzy similarity measurement tools. Using this method, new CIWs and related CRs from open resources (such as new color trends) can be integrated into the system. In a new recommendation, user's color image perceptual data measured in the proposed CIS regarding different BCSAs will be compared with those of CIWs previously defined in the system in order to recommend new CRs. CBCRS is an adaptive system, i.e. satisfied CRs will be further retained in a Successful Cases Database (SCD) so as to adapt recommended CRs to new consumers, who have similar user profiles. The general working process of the proposed system is based on case-based learning. Through repeated interactions with the proposed system by performing the cycle of Recommendation-Display-Evaluation-SCD adjustment , users (consumer or designer) will obtain satisfied CRs. Meanwhile, the quality of the SCD can be improved by integrating new recommendation cases. The proposed recommendation system is capable of dynamically generating new CIWs, CRs and new cases based on open resources.