In:
Textile Research Journal, SAGE Publications, Vol. 93, No. 13-14 ( 2023-07), p. 2956-2970
Abstract:
As an important attribute of fabric appearance and the first element that affects vision, color plays an important part in fabric and garment design, development, production and sales. Fabrics are becoming increasingly colorful which raises the difficulty of automatic recognition. With the development of computer vision technology, it has become a mainstream research hotspot to process and recognize fabric colors through novel computer vision techniques. In this paper, a color perception and representation model (CPRM) for fabric image based on color sensitivity of the human visual system is proposed to deal with the color changes caused by the human eye’s perception of fabric appearance. Inspired by the human visual biological mechanisms, we designed a CPRM by using the color sensitive function. In order to verify the effectiveness of the proposed CPRM, two types of computer simulation experiments are simulated, which are color shifts and color matching. The former verifies that our proposed model can effectively simulate the human visual system in color perception actions, which is consistent with the results verified by biological experimental data; the latter shows that the proposed model’s synergy of assimilation and contrast is closer to the biological model during the color perception stage. Finally, the CPRM is applied on three different fabric image data sets, and extensive experiments are simulated by the proposed CPRM. The CPRM could not only effectively characterize the color contrasts, but also accurately express the color changes caused by the fine adjustment of dyed fibers. The experimental results show the validity of the proposed CPRM, which can effectively perceive and represent fabric color. The proposed CPRM has important application prospects in improving color matching efficiency and intelligent production level in the textile industry.
Type of Medium:
Online Resource
ISSN:
0040-5175
,
1746-7748
DOI:
10.1177/00405175221150647
Language:
English
Publisher:
SAGE Publications
Publication Date:
2023
detail.hit.zdb_id:
2209596-2
Permalink
