Texture Patch Dataset.zip

This study uses an in-house two-class dataset comprising 1000 texture images, 500 of which denote human skin texture and the rest represent non-skin textures with high degree of similarity to human skin. These skin texture images were taken by our research team using a DSLR camera to maintain the fine skin texture patterns. Images were captured from various ethnic groups and skin color tones to avoid biasness toward any ethnic groups or skin color. Since different human body parts have different skin texture characteristics, texture images in our dataset were collected from various body parts such as face, leg, core, arms with relatively equal amount of contribution in dataset. In order to better simulate the skin detection challenges in real world and improve the robustness and reliability of our experiments, skin texture images were collected in various scales, direction and lighting conditions. The remaining 500 texture images which denote non-skin textures with high degree of similarity to human skin were collected from various online image repositories. These images, represent the texture of wooden surfaces, sand and other objects such as rugs, animal furs and fabric which are highly similar to actual skin texture, color and hue. The dataset which prepared in this study simulates challenging real-world scenarios to evaluate and compare texture analysis techniques performance in challenging conditions. All texture patches were captured and stored in uncompressed Tagged Image File Format (TIFF) to avoid any alteration or compromise in actual texture patterns. Moreover, any kind of color alternation or image enhancement were avoided. All texture images were manually resized to 150x150 dimension to equalize the amount of contribution of each image in the model.本研究采用自研的二分类数据集，共包含1000张纹理图像，其中500张为人类皮肤纹理图像，剩余500张为与人类皮肤高度相似的非皮肤纹理图像。该数据集内的皮肤纹理图像由本研究团队使用数码单反相机（DSLR）拍摄，以保留精细的皮肤纹理细节。拍摄覆盖了不同族群与肤色色调，以避免对特定族群或肤色的偏倚。由于不同人体部位的皮肤纹理特征存在差异，数据集的纹理图像采集自面部、腿部、躯干、手臂等多个身体部位，各部位样本的贡献占比相对均衡。为更好地模拟现实场景中的皮肤检测挑战，提升实验的鲁棒性与可靠性，团队在不同缩放比例、拍摄角度与光照条件下采集了皮肤纹理图像。剩余500张与人类皮肤高度相似的非皮肤纹理图像，则来源于多个在线图像库。这些图像涵盖木质表面、沙土以及地毯、动物皮毛、织物等与真实皮肤纹理、颜色与色调高度相似的物体纹理。本研究构建的数据集模拟了极具挑战性的真实应用场景，用于评估与对比纹理分析技术在复杂条件下的性能表现。所有纹理图像块均以未压缩标记图像文件格式（Tagged Image File Format, TIFF）采集并存储，以避免实际纹理模式遭受任何篡改或损耗。此外，研究过程未进行任何色彩调整或图像增强操作。所有纹理图像均被手动调整至150×150的像素尺寸，以均衡每张图像在模型训练中的贡献权重。