Knowledge Commons of Institute of Automation,CAS
Feature-Fusion-Based Haze Recognition in Endoscopic Images | |
Yu Z(于喆)1,2![]() ![]() ![]() ![]() ![]() ![]() | |
2023-11 | |
会议名称 | ICONIP2023 |
会议日期 | 2023-11 |
会议地点 | 湖南长沙 |
摘要 | Haze generated during endoscopic surgeries significantly obstructs the surgeon’s field of view, leading to inaccurate clinical judgments and elevated surgical risks. Identifying whether endoscopic images contain haze is essential for dehazing. However, existing haze image classification approaches usually concentrate on natural images, showing inferior performance when applied to endoscopic images. To address this issue, an effective haze recognition method specifically designed for endoscopic images is proposed. This paper innovatively employs three kinds of features (i.e., color, edge, and dark channel), which are selected based on the unique characteristics of endoscopic haze images. These features are then fused and inputted into a Support Vector Machine (SVM) classifier. Evaluated on clinical endoscopic images, our method demonstrates superior performance: (Accuracy: 98.67%, Precision: 98.03%, and Recall: 99.33%), outperforming existing methods. The proposed method is expected to enhance the performance of future dehazing algorithms in endoscopic images, potentially improving surgical accuracy and reducing surgical risks. |
收录类别 | EI |
七大方向——子方向分类 | 医学影像处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56735 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Zhou XH(周小虎); Hou ZG(侯增广) |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学人工智能学院 3.中国科学院脑科学与智能技术卓越创新中心 4.澳门科技大学智能科学与技术联合实验室 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yu Z,Zhou XH,Xie XL,et al. Feature-Fusion-Based Haze Recognition in Endoscopic Images[C],2023. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Feature-fusion-based(1772KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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