Geometry-Aware Eye Image-To-Image Translation
Geometry-Aware Eye Image-To-Image Translation
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DOI：
10.1145/3517031.3532524
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Conny Lu;Qian Zhang;K. Krishnakumar;Jixu Chen;H. Fuchs;S. Talathi;Kunlin Liu
中科院分区：
文献类型：
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作者：
Conny Lu;Qian Zhang;K. Krishnakumar;Jixu Chen;H. Fuchs;S. Talathi;Kunlin Liu
近年来，图像到图像的翻译(I2I)在计算机视觉领域取得了巨大的成功，但很少有人关注翻译过程中发生的几何变化。为了减少域之间的几何间隙，需要进行几何改变，代价是破坏翻译图像和原始地面真实之间的对应关系。我们提出了一种新的几何感知的半监督方法来保持这种对应关系，同时仍然允许几何变化。该方法以合成的图像掩模对作为输入，生成相应的实数对。我们还利用一个目标函数来确保图像和蒙版在平移过程中的几何运动一致。大量的实验表明，在下游的眼睛分割任务中，我们的方法得到的平均交集/并集比现有的方法高11.23%。生成的图像在Frechet初始距离中减少了15.9%，表明图像质量更高。
Recently, image-to-image translation (I2I) has met with great success in computer vision, but few works have paid attention to the geometric changes that occur during translation. The geometric changes are necessary to reduce the geometric gap between domains at the cost of breaking correspondence between translated images and original ground truth. We propose a novel geometry-aware semi-supervised method to preserve this correspondence while still allowing geometric changes. The proposed method takes a synthetic image-mask pair as input and produces a corresponding real pair. We also utilize an objective function to ensure consistent geometric movement of the image and mask through the translation. Extensive experiments illustrate that our method yields a 11.23% higher mean Intersection-Over-Union than the current methods on the downstream eye segmentation task. The generated image has a 15.9% decrease in Frechet Inception Distance indicating higher image quality.