Image Driver Sampling High-Resolution Data via Feature Extraction
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Solution Overview
Problem
Existing image analytics systems require significant computing resources to process high-resolution images, making them difficult to scale and inflexible, as they rely on pre-defined rules to detect patterns, which can miss unpredictable behavior unless it conforms to predefined criteria.
Innovation Solution
A computer-implemented method generates a low-dimensional representation of images by extracting robust features such as homogeneity, gradient values, color, and entropy from image regions, creating a sample vector that can be analyzed by machine learning components to detect abnormalities without pre-defined patterns, allowing for real-time processing and adaptive learning of behavioral patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are processed directly by image analytics systems, then detection accuracy is improved, but computing resource consumption increases significantly
Solution Approach 1:
The image processing system divides the high-resolution image into multiple regions of interest (ROIs) and processes each region separately. This segmentation allows the system to focus computational resources on specific areas containing potential patterns, rather than processing the entire high-resolution image, thereby reducing overall computing resource consumption while maintaining detection accuracy in critical regions.
Solution Approach 2:
The system extracts only the essential features and regions from the high-resolution image that are relevant for pattern detection. By taking out and processing only these extracted features rather than the complete high-resolution image data, the system maintains detection accuracy while significantly reducing the computational burden and resource consumption.
2Device complexity
If pre-defined rules are used for pattern detection, then system simplicity is maintained, but adaptability to unpredictable behavior is reduced
Solution Approach 1:
The system dynamically adjusts its detection parameters and patterns based on the analyzed data. Rather than relying solely on static pre-defined rules, the system can adapt its detection criteria in real-time based on observed anomalies and patterns in the image data, allowing it to detect unpredictable behaviors while maintaining operational simplicity through automated adaptation.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results and anomaly patterns are fed back into the system to refine and update detection rules. This feedback loop allows the system to learn from observed behaviors and improve its ability to detect unpredictable events, transforming the static rule-based system into an adaptive system that maintains simplicity while gaining versatility.
3Loss of information
If full high-resolution image data is analyzed, then complete information is available for detection, but processing time and scalability are reduced
Solution Approach 1:
The system extracts and processes only the essential features and regions from the high-resolution image that are relevant for pattern detection. By taking out and processing only these extracted features rather than the complete high-resolution image data, the system maintains detection accuracy while significantly reducing the computational burden and resource consumption.
Solution Approach 2:
The system processes a partial representation of the image data (extracted features and regions of interest) rather than the complete high-resolution image. This partial action approach provides sufficient information for effective pattern detection while dramatically reducing processing time and improving scalability, as the system handles only the necessary subset of data required for detection.
Data Source
AI summary
Techniques are disclosed for generating a low-dimensional representation of an image. An image driver receives an image captured by a camera. The image includes features based on pixel values in the image, and each feature describes the image in one or more image regions. The image driver generates, for each of the plurality of features, a feature vector that includes values for that feature corresponding to at least one of the image regions. Each value indicates a degree that the feature is present in the image region. The image driver generates a sample vector from each of the feature vectors. The sample vector includes each of the values included in the generated feature vectors.