Learned Model Image Recognition Region Refinement
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Solution Overview
Problem
The accuracy of image recognition using learned models is affected by variations in the detection trend of regions of interest, leading to inconsistent results across different models.
Innovation Solution
A method involving the acquisition of a first determination result using a first learned model, selection of a partial region, application of an alteration process to generate second data, and subsequent determination using a second learned model to enhance accuracy, with the final result obtained by combining the first and second determination results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple learned models are used for image recognition, then the coverage of detection capabilities is improved, but the inconsistency in detection trends across models worsens the determination accuracy
Solution Approach 1:
The patent divides the image into multiple regions of interest based on detection results from different learned models. Each region is independently analyzed and determined, allowing the system to leverage the strengths of different models for different regions while maintaining overall determination accuracy through localized processing.
2Area of stationary object
If the target region for the second learned model is not limited, then the detection scope is improved, but the determination accuracy decreases due to irrelevant regions affecting the result
Solution Approach 1:
The patent applies different processing strategies to different regions of the image. The target region identified by the first learned model receives focused attention and is subjected to alteration processes, while other regions are handled differently. This localized quality approach ensures that the most relevant regions are processed with maximum precision.
Solution Approach 2:
The patent extracts and isolates the target region from the rest of the image based on the first learned model's detection results. By separating the target region and applying specific alteration processes only to this extracted portion, the system eliminates interference from irrelevant regions while maintaining comprehensive detection coverage.
3Measurement precision
If alteration processes are applied to the target region, then the focus on relevant areas is improved, but the loss of information from other regions may occur
Solution Approach 1:
The patent applies alteration processes selectively to the target region rather than uniformly to the entire image. This partial action approach concentrates computational resources on the most relevant areas, improving detection precision where it matters most while accepting that other regions receive less processing. The system performs sufficient rather than excessive processing on critical regions.
Data Source
AI summary
A method, computer system, and a computer program product for digital image recognition determination using a learned model is provided. The present invention may include acquiring a first determination result by making a determination concerning first data, using a first learned model. The present invention may include selecting a partial region of the first data. The present invention may then include generating second data obtained by applying a first alteration process to the partial region. The present invention may also include acquiring a second determination result by making a determination concerning the second data, using a second learned model. The present invention may lastly include obtaining a final determination result based on the first determination result and the second determination result.