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Lip segmentation using automatic selected initial contours based on localized active contour model

机译:利用基于局部活动轮廓模型的自动选择的初始轮廓的唇部分割

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Abstract With the rapid development of artificial intelligence and the increasing popularity of smart devices, human-computer interaction technology has become a multimedia and multimode technology from being computer-focused to people-centered. Among all ways of human-computer interactions, using language to interact with machines is the most convenient and efficient one. However, the performance of audio speech recognition systems is not satisfied in a noisy environment. Thus, more and more researchers focus their works on visual lip reading technology. By extracting lip movement features of speakers rather than audio features, visual lip reading systems can get superior results when noises and interferences exist. Lip segmentation plays an important role in a visual lip reading system, since the segmentation result is crucial to the final recognition accuracy. In this paper, we propose a localized active contour model-based method using two initial contours in a combined color space. We apply illumination equalization to original RGB images to decrease the interference of uneven illumination. A combined color space consists of the U component in CIE-LUV color space and the sum of C2 and C3 components of the image after discrete Hartley transform. We select a rhombus as the initial contour of a closed mouth, because it has a similar shape to a closed lip. For an open mouth, we utilize a combined semi-ellipse as the initial contours of both outer and inner lip boundaries. After attaining the results of each color component separately, we merge them together to obtain the final segmentation result. From the experiment, we can conclude that this method can get better segmentation results compared with the method using a circle as the initial contour to segment gray images and images in combined color space, especially for open mouth. An extremely obvious advantage of this method is the results of open mouth excluding internal information of mouth such as teeth, black holes, and tongue, because of the introduction of the inner initial contour.
机译:摘要随着人工智能的快速发展和智能设备越来越多的普及,人机交互技术已成为多媒体和多模技术,从计算机集中于以人为本。在人机交互的所有方式中,使用语言与机器进行交互是最方便和最有效的。然而,音频语音识别系统的性能在嘈杂的环境中不满足。因此,越来越多的研究人员将他们的作品集中在视觉唇读技术上。通过提取扬声器而不是音频特征的唇部运动特征,当存在噪声和干扰时,视觉唇读系统可以获得卓越的结果。唇部分割在视觉唇读取系统中起着重要作用,因为分段结果对最终识别准确性至关重要。在本文中,我们提出了一种在组合颜色空间中的两个初始轮廓的局部主动轮廓模型的方法。我们将照明均衡应用于原始RGB图像以减少不均匀照明的干扰。组合的颜色空间由CIE-LUV颜色空间中的U分量和分立在离散Hartley变换之后的图像的C2和C3分量的总和组成。我们选择一个菱形作为闭嘴的初始轮廓,因为它具有与封闭唇缘类似的形状。对于张开的嘴,我们利用组合的半椭圆作为外部和内唇边界的初始轮廓。在分别获得每个颜色组分的结果之后,我们将它们合并在一起以获得最终的分段结果。从实验中,我们可以得出结论,与使用圆圈的方法相比,该方法可以获得更好的分割结果作为初始轮廓,以在组合的颜色空间中分段灰度图像和图像,特别是对于张开的嘴巴。这种方法的一个极明显的优点是张开嘴的结果,不包括齿,黑洞和舌头的内部信息,因为内部初始轮廓引入。

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