The carpet industry is no longer a small business done in the villages on a small scale; instead, it has carved an altogether different space, identity, and appreciation for itself in the cosmopolitan world. As computers are is becoming more and more ubiquitous, most industries, including the carpet industry, use computers for quality improvement, accuracy enhancement, speed development, and cost reduction purposes. Unlike traditional carpet maps, many modern maps include images of human faces for hand-woven carpet tableau. These digital images comprise millions of colors and thousands of pixels, making it practically impossible to construct and weave the carpet in the same dimensions. Many weavers currently use manual and experience-based methods for reducing the size and number of hues for making a hand-woven carpet tableau map. Therefore, the outcomes are not the optimal results and can be improved. Also, many color reduction methods do not focus on the hand-woven carpet tableau map. To overcome these problems and gaps, this research focuses on proposing a new automatic method for reducing the size of color images without compromising facial nuances, lessening the number of colors used while protecting the important areas of the images, and transforming those images into carpet tableau maps. The proposed approach inputs the original color image. It continuously detecting the face and specifying important areas, and finally, outputs carpet tableau map that is proportional to the given dimensions and color count. To evaluate the proposed method, MATLAB, as a powerful simulation tool, was employed. Final results are compared to the existing approaches in terms of face detection, size reduction, and color quantization. The obtained results have shown that the approach improves speed by 39% in face detection and increases the precision of size reduction and color quantization phases. The results have also confirmed that when images of human faces are reduced by a proposed method to form an appropriate image for tableau maps, they are nearly always perceived as more attractive than the reduced faces via traditional methods.

中文翻译：

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一种减少地毯地图中使用的彩色图像中色调大小和数量的混合方法

地毯业不再是农村小规模的小生意；相反，它在国际化的世界中为自己创造了一个完全不同的空间、身份和欣赏。随着计算机变得越来越普遍，包括地毯行业在内的大多数行业都使用计算机来提高质量、提高准确性、加快开发速度和降低成本。与传统的地毯地图不同，许多现代地图都包含用于手工编织地毯画面的人脸图像。这些数字图像包含数百万种颜色和数千个像素，因此几乎不可能以相同的尺寸构造和编织地毯。许多编织者目前使用手动和基于经验的方法来减少用于制作手工编织地毯画面图的色调大小和数量。所以，结果不是最佳结果，可以改进。此外，许多色彩还原方法并不侧重于手工编织的地毯画面图。为了克服这些问题和差距，本研究的重点是提出一种新的自动方法，用于在不影响面部细微差别的情况下减小彩色图像的尺寸，在保护图像重要区域的同时减少使用的颜色数量，并将这些图像转换为地毯画面地图。所提出的方法输入原始彩色图像。它不断检测人脸并指定重要区域，最后输出与给定尺寸和颜色计数成比例的地毯画面图。为了评估所提出的方法，MATLAB 作为一种强大的仿真工具，被采用。在人脸检测方面将最终结果与现有方法进行比较，尺寸减小和颜色量化。获得的结果表明，该方法将人脸检测的速度提高了 39%，并提高了尺寸缩减和颜色量化阶段的精度。结果还证实，当通过提出的方法缩小人脸图像以形成适合画面地图的图像时，它们几乎总是被认为比通过传统方法缩小的人脸更有吸引力。