International business machines corporation (20240119576). IMAGE ANOMALY DETECTION BY ENHANCING PATCHED FEATURES simplified abstract
Contents
- 1 IMAGE ANOMALY DETECTION BY ENHANCING PATCHED FEATURES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IMAGE ANOMALY DETECTION BY ENHANCING PATCHED FEATURES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
IMAGE ANOMALY DETECTION BY ENHANCING PATCHED FEATURES
Organization Name
international business machines corporation
Inventor(s)
TADANOBU Inoue of Yokohama (JP)
Takayuki Katsuki of Tokyo (JP)
IMAGE ANOMALY DETECTION BY ENHANCING PATCHED FEATURES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119576 titled 'IMAGE ANOMALY DETECTION BY ENHANCING PATCHED FEATURES
Simplified Explanation
The patent application relates to accurate anomaly detection in images using patched features.
- Extraction component extracts features from patches of an image using a pretrained convolutional neural network (CNN).
- Feature mapping component concatenates features from multiple layers to generate a tensor feature map with a one-dimensional feature vector for each patch.
- Cropping component performs center cropping on the tensor feature map.
- Calculation component calculates the distance to a feature distribution mean for each patch.
Potential Applications
This technology can be applied in various fields such as medical imaging for detecting anomalies in X-rays or MRIs, security systems for identifying suspicious objects in surveillance footage, and quality control in manufacturing for inspecting products for defects.
Problems Solved
This technology solves the problem of accurately detecting anomalies in images by utilizing patched features and advanced neural network techniques. It improves the efficiency and accuracy of anomaly detection systems.
Benefits
The benefits of this technology include improved accuracy in anomaly detection, faster processing of images, and the ability to detect subtle anomalies that may be missed by traditional methods. It can enhance the overall performance of image analysis systems.
Potential Commercial Applications
- "Enhancing Image Anomaly Detection for Various Industries"
Possible Prior Art
There may be prior art related to image anomaly detection using neural networks and feature extraction techniques. Research papers or patents in the field of computer vision and image processing could potentially be relevant.
Unanswered Questions
How does this technology compare to existing anomaly detection methods in terms of accuracy and efficiency?
This article does not provide a direct comparison with existing methods in the field of anomaly detection. Further research or testing may be needed to evaluate the performance of this technology against other approaches.
What are the limitations of this technology in real-world applications?
The article does not discuss potential limitations or challenges that may arise when implementing this technology in practical settings. Understanding the constraints of the system is crucial for its successful deployment.
Original Abstract Submitted
one or more systems, devices, computer program products, and/or computer-implemented methods provided herein relate to accurate anomaly detection in images using patched features. according to an embodiment, an extraction component can extract multiple layers of features from one or more patches of an image using a pretrained convolutional neural network (cnn). a feature mapping component can concatenate the features from the multiple layers to generate a tensor feature map comprising a one-dimensional feature vector for respective patches. a cropping component can perform center cropping on the tensor feature map. a calculation component can calculate a distance to a feature distribution mean for respective patches.
