Bounding Box Precision Models for Efficient Image Classification
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Solution Overview
Problem
Existing digital image processing operations are inefficient and unreliable in determining optimal bounding boxes and classifications, leading to high computational complexity and resource utilization.
Innovation Solution
The use of bounding box precision models to determine optimal primary kernel dimension sizes for object differentiation kernels, combined with image classification machine learning models, to generate precise bounding box classifications and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional digital image processing operations are used to determine bounding boxes and classifications, then comprehensive image analysis is achieved, but computational complexity and resource utilization increase significantly
Solution Approach 1:
The patent pre-calculates and stores precision values for different kernel dimension sizes before actual image processing. This preliminary action creates a lookup table that allows the system to quickly determine optimal kernel sizes during processing without performing complex real-time calculations, thus reducing computational complexity while maintaining accurate bounding box determination
Solution Approach 2:
The patent systematically varies kernel dimension parameters (primary and secondary kernel dimensions) to establish relationships between kernel size and bounding box precision. By pre-analyzing how different parameter values affect precision and storing these results, the system can efficiently select optimal parameters during actual processing without repeating complex computations
2Reliability
If traditional digital image processing operations are used to determine bounding boxes and classifications, then thorough image analysis is performed, but resource utilization increases
Solution Approach 1:
The system performs preliminary analysis to establish precision models that map kernel dimension sizes to expected precision outcomes. This pre-computed knowledge allows the system to make informed decisions about resource allocation and kernel selection during actual processing, avoiding wasteful trial-and-error computations and reducing overall resource utilization while maintaining classification accuracy
3Measurement precision
If larger kernel dimension sizes are used for object differentiation, then more detailed bounding boxes are generated, but the number of processor operations increases
Solution Approach 1:
The patent establishes a comprehensive model that evaluates the relationship between kernel dimension parameters and both precision outcomes and computational costs. By analyzing this relationship in advance, the system can select kernel sizes that achieve the required precision threshold with minimal computational operations, optimizing the trade-off between measurement precision and processing speed
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing digital image processing operations. For example, as described herein, various embodiments of the present invention relate to performing digital image processing operations using at least one of using bounding box precision models to generate an optimal object differentiation kernel, using an optimal object differentiation kernel to generate/detect optimal bounding boxes of an image set, and using an image classification machine learning model to generate bounding box classifications for the optimal bounding boxes of an image set.