Smart Clothing Fabric Color Matching with Reference to Popular Colors

Color economy and market fashion trend have an increasing impact on clothing fabric color matching.Therefore,a smart clothing fabric color matching system with reference to popular colors is designed to realize the diversification of clothing color matching,which includes a palette generation module and a clothing fabrics-palette color matching network(CF-PCN).Firstly,palette generation module generates palettes referring popular colors while maintains color styles of clothing fabrics.Secondly,CF-PCN generates color matching images containing color information of palettes.The experimental results show that the color matching system has a higher average pixel ratio of palette colors and contains more palette color information.It demonstrates that the system achieves color matching innovation referring popular colors while retaining color style of clothing brands and provides designers with appropriate color matching solutions.

Image matching algorithm based on SIFT using color and exposure information

Image matching based on scale invariant feature transform（SIFT） is one of the most popular image matching algorithms, which exhibits high robustness and accuracy. Grayscale images rather than color images are generally used to get SIFT descriptors in order to reduce the complexity. The regions which have a similar grayscale level but different hues tend to produce wrong matching results in this case. Therefore, the loss of color information may result in decreasing of matching ratio. An image matching algorithm based on SIFT is proposed, which adds a color offset and an exposure offset when converting color images to grayscale images in order to enhance the matching ratio. Experimental results show that the proposed algorithm can effectively differentiate the regions with different colors but the similar grayscale level, and increase the matching ratio of image matching based on SIFT. Furthermore, it does not introduce much complexity than the traditional SIFT.

3D human face reconstruction based on band-limited binary patterns

Face recognition technology has great prospects for practical applications. Three-dimensional（3D） human faces are becoming more and more important in consideration of the limits of two-dimensional face recognition. We propose an active binocular setup to obtain a 3D colorful human face using the band-limited binary patterns（BBLP） method. Two grayscale cameras capture the BBLP projected onto the target of human face by a digital light processing（DLP） projector synchronously. Then, a color camera captures a colorful image of the human face. The benefit of this system is that the 3D colorful human face can be obtained easily with an improved temporal correlation algorithm and the precalibration results between three cameras. The experimental results demonstrated the robustness, easy operation, and the high speed of this 3D imaging setup.