Image Processor Hierarchical Weak Classifier Target Registration
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Solution Overview
Problem
Conventional image processors face difficulties in detecting human and pet faces accurately, often requiring users to register precise domains, which can be operationally complex and lead to incomplete target detection due to unclear boundaries and incorrect domain assignments.
Innovation Solution
An image processor that includes a classifier with weak classifiers performing hierarchical detection processing, a search image generator to extract domains near user-specified positions, and a registration image generator to produce a registration image based on the largest processing domain hierarchy, allowing for accurate target detection even with slight user position deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually specify precise domains for target detection, then detection accuracy improves, but operational complexity increases and users may incorrectly assign domains due to unclear target boundaries
Solution Approach 1:
The system performs self-service by automatically specifying the search domain based on the user's simple position input, eliminating the need for users to manually define complex domain boundaries. The processor autonomously calculates the search domain range and performs detection within this automatically generated domain.
Solution Approach 2:
The system performs preliminary action by pre-specifying the search domain before actual target detection occurs. The processor determines the appropriate search domain range based on the user's position input and detection requirements, preparing the detection framework in advance to guide subsequent detection operations.
2Reliability
If the search domain is expanded to include entire targets with unclear boundaries, then detection completeness improves, but processing time and computational load increase
Solution Approach 1:
The system applies parameter changes by dynamically adjusting the search domain range based on detection requirements and target characteristics. The processor modifies the domain parameters to optimize the balance between including complete targets and maintaining efficient processing speed.
Solution Approach 2:
The system uses partial action by specifying a search domain that is larger than the minimal target area but not excessively large. This partial expansion ensures complete target inclusion while avoiding unnecessary processing of irrelevant regions, achieving optimal detection completeness with reasonable computational cost.
3Measurement precision
If multiple weak classifiers are applied in sequence to improve detection accuracy, then detection precision improves, but processing complexity and time increase
Solution Approach 1:
The system applies segmentation by dividing the detection process into multiple sequential weak classifier stages. Each weak classifier processes the data in sequence, with results passed to the next stage, breaking down the complex detection task into manageable segments that collectively achieve high precision.
Solution Approach 2:
The system uses an intermediary approach by introducing a search domain specification mechanism that mediates between user input and classifier processing. This intermediary layer pre-processes and structures the input data, making subsequent classifier operations more efficient and reducing overall processing complexity.
Data Source
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AI summary
A registration image including a desired target can easily be registered. A domain near an assigned position assigned by a user on a photographed image is extracted from the photographed image to generate a search image, a classifier performs the processing to the generated search image, and a processing domain having the largest number of hierarchies to which a weak classifier can perform the processing is extracted from the photographed image to generate the registration image.