Rock Slice Identification Using Multi-Dimensional Image Features
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Solution Overview
Problem
Traditional rock identification methods using manual observation and image input for rock slices suffer from high subjectivity, leading to low accuracy due to errors in microscopic observation and image analysis.
Innovation Solution
A rock identification method and system utilizing an image capturing device and computer device to generate geometric, mineral, and structural features from rock slice images, employing machine learning models for segmentation and identification, improving accuracy through multi-dimensional feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation and image input methods are used for rock slice identification, then the operation process is simple and equipment requirements are low, but the identification accuracy is low due to subjective errors
Solution Approach 1:
The patent replaces the manual mechanical observation system with an automated image processing system. The image capturing device automatically captures rock slice images, and the computer device processes these images through algorithms to extract features and identify rock types, eliminating subjective human error while maintaining operational simplicity.
Solution Approach 2:
The patent creates a digital copy of the rock slice through high-resolution image capture. Instead of directly observing the physical rock slice under a microscope, the system captures an accurate digital representation that can be processed, analyzed, and stored without altering the original sample, enabling repeated analysis without subjectivity.
2Measurement precision
If multi-dimensional feature extraction is implemented through automated image processing, then identification accuracy is improved, but the device complexity and processing time increase
Solution Approach 1:
The patent performs preliminary feature extraction by pre-processing the rock slice images to identify and extract geometric features, mineral features, and structural features before the actual identification process. This preliminary organization of data accelerates the subsequent analysis and reduces overall processing time while maintaining comprehensive feature analysis.
Solution Approach 2:
The patent divides the complex identification process into distinct segments: image capture, geometric feature extraction, mineral feature extraction, structural feature extraction, and final identification. Each segment is processed independently and efficiently, allowing parallel computation and reducing total processing time while maintaining accuracy.
3Reliability
If automated image processing and machine learning models are used, then subjectivity is reduced and accuracy is improved, but the device complexity and initial investment increase
Solution Approach 1:
The patent implements a self-service system where the computer device automatically processes rock slice images without requiring manual intervention. The machine learning models self-train on extracted features and automatically perform identification, reducing the need for expert operators and minimizing subjective bias while improving reliability and consistency of results.
Data Source
AI summary
A method for rock identification includes the steps of receiving a rock slice image transmitted by an image acquisition device, generating a geometric feature, a mineral feature and a structural feature corresponding to a rock slice based on the rock slice image, and generating an identification result of the rock slice based on the geometric feature, the mineral feature and the structural feature. After the rock slice image is obtained, the rock slice image is subject to feature extraction based on three dimensions, i.e., the geometric feature, the mineral feature and the structural feature, the properties of rock are determined based on multiple dimensional features, and finally the identification result comprising a textual description is generated. After a microscopic visual image corresponding to the rock slice is obtained, feature extraction based on multiple dimensions is performed on the image, and the rock slice is identified with reference to multi-dimensional features.