Automatic image evaluation system

1989 ◽  
Vol 22 (6) ◽  
pp. 377
Author(s):  
Siemens-Infoservice
2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Li Deng ◽  
Sui-Huai Yu ◽  
Wen-Jun Wang ◽  
Jun-Xuan Chen ◽  
Guo-Chang Liu

Aiming at the problem that color image is difficult to quantify, this paper proposes an evaluation method of color image for small space based on factor analysis (FA) and gene expression programming (GEP) and constructs a correlation model between color image factors and comprehensive color image. The basic color samples of small space and color images are evaluated by semantic differential method (SD method), color image factors are selected via dimension reduction in FA, factor score function is established, and by combining the entropy weight method to determine each factor weights then the comprehensive color image score is calculated finally. The best fitting function between color image factors and comprehensive color image is obtained by GEP algorithm, which can predict the users’ color image values. A color image evaluation system for small space is developed based on this model. The color evaluation of a control room on AC frequency conversion rig is taken as an example, verifying the effectiveness of the proposed method. It also can assist the designers in other color designs and provide a fast evaluation tool for testing users’ color image.


2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Shutao Zhang ◽  
Shijie Wang ◽  
Aimin Zhou ◽  
Shifeng Liu ◽  
Jianning Su

In actual product development, the cognitive differences between users and designers make it difficult for the designed products to be recognized by users. To reduce the cognitive differences between these two design subjects, this paper proposes a method of cognitive matching of the design subjects. First, we use the relevant methods of Kansei engineering to quantify the Kansei image cognition of the two design subjects and construct a cognitive matching model of the design subjects with information entropy and the technique for order preference by similarity to ideal solution (TOPSIS). Second, according to the Kansei image, the Kansei image prototype cluster is constructed, and the representative Kansei image prototype is obtained. Then, we combine an artificial neural network (ANN) with a cognitive matching model of the design subjects to construct a product Kansei image evaluation system; this is used to evaluate the evolved forms. Finally, a product Kansei image form evolution system is constructed based on the genetic algorithm (GA). To some extent, the system simulates the cognitive matching process between designers and users in product design, helps designers to more accurately understand the cognitive trends of the two design subjects, and provides a theoretical basis for the intelligent design of product forms through the cognitive balance of multiple design subjects. This paper takes a beverage bottle as an example to verify the feasibility of the model through a comparative study.


Cornea ◽  
2013 ◽  
Vol 32 (4) ◽  
pp. 460-465 ◽  
Author(s):  
Christine W. Sindt ◽  
Bruno Lay ◽  
Helene Bouchard ◽  
Jami R. Kern

2010 ◽  
Vol 102-104 ◽  
pp. 905-909
Author(s):  
Fu Qian Shi ◽  
Jian Feng Wu ◽  
Xiao Dong He ◽  
Hai Ning Wang ◽  
Shou Qian Sun

Due to the uncertainness and fuzziness of image evaluation on product form, many fuzzy multi-criteria decision making approaches are applied in this fields. Fuzzy linguistic is usually used by expert to evaluate the importance of the criteria and to rate the alternatives; hence, we integrating fuzzy linguistic based analytic hierarchy process methodology to image evaluation of product form. Trapezoidal fuzzy number and 11-scale linguistic variables were applied in this model. The proposed approach started by a web-based image evaluation system, and then target products were extracted and divided in many design characteristics. Fuzzy linguistic based AHP was applied to generate the critical forms that ordered by vary image adjectives. A case study of mobile phone design was demonstrated the effectiveness.


2003 ◽  
Vol 9 (S02) ◽  
pp. 1038-1039 ◽  
Author(s):  
N. Nakamura ◽  
Y. Fujiyoshi ◽  
K. Mitsuoka ◽  
K. Murata ◽  
T. Shinkawa

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