Name
Abstract | ||
|---|---|---|
Fashion designers conceive color composition as an ensemble of tones, shades and tints that often resemble an abstract or natural theme. Color and tone adjustments are among the most frequent fabric design operations. Color and tone style adjustments are part of a generic process of cognition involved in the creation of new fabric designs. A challenging problem in this process is given a s et of natural images associated with a subject or a theme, the problem is to discover the underlying cognitive relationships that connect textures and color tones that are perceived by an observer or in our case, a fabric designer. These relationships culminate in optimal combinations of colors and tones that resemble the original theme in which a template of colors is defined with respect to an associated verbal description. We formulate the fabric design process as a cognitive process that simultaneously considers multiple color tones to form a desired theme as well as texture characteristics in the fabric. In this paper, we incorporate color assembly and texture knowledge in the fabric design process in order to automatically produce the color combinations learnt from a given natural or abstract theme. We demonstrate that the use of color theme associations can automatically generate new fabric designs that rival complex commercial designs that are otherwise difficult to generate even by experienced designers. Preliminary experiments confirm the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICCI-CC.2012.6311173 | 2012 IEEE 11th International Conference on Cognitive Informatics and Cognitive Computing |
Keywords | Field | DocType |
Fabric design,color theme,cognitive design,weave pattern,color enhancement | Color enhancement,Computer vision,Computer science,Image texture,Artificial intelligence,Observer (quantum physics),Design process,Cognition | Conference |
ISBN | Citations | PageRank |
978-1-4673-2794-7 | 4 | 0.47 |
References | Authors | |
18 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dejun Zheng | 1 | 25 | 3.38 |
| Yu Han | 2 | 114 | 8.61 |
| George Baciu | 3 | 409 | 56.17 |
| Jinlian Hu | 4 | 43 | 6.29 |