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首页> 外文会议>IEEE International Conference on Image Processing >FIGARO, HAIR DETECTION AND SEGMENTATION IN THE WILD
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FIGARO, HAIR DETECTION AND SEGMENTATION IN THE WILD

机译：FigoRo，野生的毛发检测和细分

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摘要

Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of hair detection without relying on a-priori information related to head shape and location. Without using any human-body part classifier, we first classify image patches into hair vs. non-hair by relying on Histogram of Gradients (HOG) and Linear Ternary Pattern (LTP) texture features in a random forest scheme. Then we obtain results at pixel level by refining classified patches by a graph-based multiple segmentation method. Achieved segmentation accuracy (85%) is comparable to state-of-the-art on less challenging databases.
机译：头发是主要表征人类外观的元素之一。能够检测图像中的头发可以在许多应用中有用，例如面部识别，性别分类和视频监控。为此目的，我们提出了一种新型多级图像数据库，用于野外的野发检测，称为Figaro。我们解决头发检测问题而不依赖于与头部形状和位置相关的A-priori信息。不使用任何人体零件分类器，我们首先通过依赖于随机林方案中的梯度（HOG）和线性三元图案（LTP）纹理特征的直方图，将图像补丁分类为非头发。然后，我们通过基于图形的多个分段方法精炼分类补丁来获得像素级别的结果。达到分割精度（85％）与较不具有挑战性的数据库的最先进的准确性相当。

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