图像处理方法、电子设备和计算机可读存储介质
By acquiring multiple images and performing texture feature extraction and neural distance analysis, risk indicators for the monitoring area are generated, which solves the problem of low accuracy in risk prediction of the monitoring area by deep learning models and achieves higher accuracy in risk assessment.
CN117152674BActive Publication Date: 2026-07-17ALIBABA DAMO (HANGZHOU) TECH CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ALIBABA DAMO (HANGZHOU) TECH CO LTD
- Filing Date
- 2023-07-20
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
In existing technologies, prediction models using deep learning have low accuracy in predicting risks in monitored areas and cannot accurately determine whether risks exist in the monitored areas.
Method used
By acquiring multiple images collected at different times, texture features are extracted, and the target neural distance is determined by combining the positional relationship between the monitoring area and other areas. Risk indicators for the monitoring area are generated, taking into full account the contact between the monitoring area and other areas.
Benefits of technology
This improved the accuracy of risk prediction in the monitored area, reduced misjudgments and the workload of staff, and ensured the accuracy of risk assessment.
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Smart Images
Figure CN117152674B_ABST
Abstract
本申请公开了一种图像处理方法、电子设备和计算机可读存储介质。其中，该方法包括：获取不同时间采集到的多期图像，其中，多期图像的显示内容至少包含待监测对象的目标部位的监测区域；对多期图像进行纹理特征提取，得到多期图像的目标纹理特征；基于多期图像中的监测区域和其他区域的位置关系，确定多期图像的目标神经距离，其中，其他区域用于表征目标部位除监测区域之外的区域；基于目标纹理特征和目标神经距离，生成监测区域的风险指标，其中，风险指标用于表征监测区域存在风险的概率。本申请解决了相关技术中确定监测区域是否存在风险的准确度低的技术问题。
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