Multi-Layer Image Analysis System for Pattern Recognition
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Solution Overview
Problem
Existing digital image analysis systems that use object-oriented analysis are computationally intensive and slower than statistical pixel-oriented analysis, despite providing more accurate pattern recognition.
Innovation Solution
A computer-implemented analysis system that segments digital image data into image, thematic, and object layers, using a hierarchical network structure to combine pixel-oriented and object-oriented processing, thereby reducing computational requirements while maintaining accurate pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object-oriented analysis is used to analyze digital images, then pattern recognition accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the digital image into multiple layers (image layer, thematic layer, object layer) with different levels of abstraction. The image layer contains raw pixel data, the thematic layer contains classified pixel groups, and the object layer contains high-level object representations. This segmentation allows the system to perform simple statistical analysis on the image layer while reserving object-oriented analysis for specific layers, thereby reducing overall computational complexity while maintaining pattern recognition accuracy.
Solution Approach 2:
The patent introduces a new dimension of processing by creating a multi-layered data structure that adds vertical stratification to the traditional two-dimensional image analysis. Each layer operates at a different level of abstraction, allowing the system to process information in multiple dimensions simultaneously - pixel-level statistics in the image layer and object-level semantics in the object layer - thus resolving the contradiction between accuracy and complexity.
2Measurement precision
If object-oriented analysis is used to analyze digital images, then pattern recognition accuracy is improved, but processing speed decreases
Solution Approach 1:
By segmenting the analysis into multiple layers, the patent enables parallel processing where statistically intensive operations are performed on the image layer while object-oriented operations are performed on the object layer. This segmentation allows the system to maintain high processing speed for routine pixel operations while applying more computationally intensive object-oriented analysis only where needed, thus improving overall processing speed without sacrificing accuracy.
Solution Approach 2:
The patent performs preliminary statistical analysis and pixel classification in the image and thematic layers before proceeding to object-oriented analysis in the object layer. This preliminary action prepares the data in advance, so that when object-oriented analysis is applied, the computational workload is already reduced and organized, thereby maintaining high processing speed while ensuring accurate pattern recognition.
3Measurement precision
If object-oriented analysis is used to analyze digital images, then analysis accuracy is improved, but computational resource management becomes more difficult
Solution Approach 1:
The multi-layer architecture segments computational resources across different processing levels. Each layer (image, thematic, object) has its own optimized data structures and processing algorithms appropriate to its level of abstraction. This segmentation simplifies resource management by allowing independent optimization of each layer without affecting the others, thus maintaining high analysis accuracy while reducing the complexity of overall computational resource management.
Data Source
AI summary
An analysis system analyzes and measures patterns present in the pixel values of digital images using a computer-implemented network structure that includes a process hierarchy, a class network and a data network. The data network includes image layers, thematic layers and object networks. Various types of processing are performed depending on whether the data of the digital images is represented in the image, thematic or object layers. Pixel-oriented and object-oriented processing is combined so that fewer computations and less memory are used to analyze the digital images. Pixel-oriented processes, such as filtering, are selectively performed only at pixel locations that are assigned to a specified thematic class of a thematic layer or that are linked to a particular object of the object network. Similarly, object-oriented processing is performed at pixel locations linked to objects generated using thematic layers or using image layers on which pixel-oriented processes have already been performed.