利用镜面反射区域定位物体，无需训练即可提升分割精度。
Towards Object Segmentation Mask Selection Using Specular Reflections
- 以反射区域最大连通域作为物体，改进分割边界。
- 相比SAM2，IoU提升26.7%，DSC提升22.3%。
- 无需额外数据或模型调整，适用于真实图像。
镜面反射因其强烈的亮度变化，严重干扰物体分割，无论是传统方法还是基于深度学习的方法均易受误导。然而，镜面反射必然位于物体表面，这一特性可被利用来优化分割掩码。本文通过识别包含反射的最大的连通区域作为物体，从而生成更精确的分割结果，且无需专门的训练数据或模型适配。我们在合成与真实图像上评估了该方法，并与经典及前沿技术（包括Otsu阈值、YOLO、SAM2）进行对比。相较于最佳基线SAM2，本方法在IoU上提升26.7%，在DSC上提升22.3%，在像素准确率上提升9.7%。对真实图像的定性评估进一步验证了方法的鲁棒性与泛化能力。
原文摘要 · Abstract (English)
Specular reflections pose a significant challenge for object segmentation, as their sharp intensity transitions often mislead both conventional algorithms and deep learning based methods. However, as the specular reflection must lie on the surface of the object, this fact can be exploited to improve the segmentation masks. By identifying the largest region containing the reflection as the object, we derive a more accurate object mask without requiring specialized training data or model adaption. We evaluate our method on both synthetic and real world images and compare it against established and state-of-the-art techniques including Otsu thresholding, YOLO, and SAM2. Compared to the best performing baseline SAM2, our approach achieves up to 26.7% improvement in IoU, 22.3% in DSC, and 9.7% in pixel accuracy. Qualitative evaluations on real world images further confirm the robustness and generalizability of the proposed approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。