Adaptive Object Classification via Downsampled Image Masking
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Solution Overview
Problem
Static object and color tone identification systems in image processing are inefficient, failing to accurately identify desired objects or colors due to narrow or broad definitions, leading to inconsistent performance and excessive power consumption.
Innovation Solution
Adaptive object and color identification systems, utilizing AI processes and adaptive memory color tuning, which downscale images, generate object or color detection masks, and adjust classifications based on histogram analysis to improve accuracy and reduce power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static object and color tone identification systems are used, then the system structure is simple, but the identification accuracy is poor and performance is inconsistent
Solution Approach 1:
The patent implements adaptive object classification by dynamically adjusting classification parameters based on scene content analysis. The system transitions from static predefined classifications to dynamic adaptive classifications that respond to actual image content, thereby improving identification accuracy while maintaining reasonable system complexity through algorithmic adaptation rather than hardware complexity.
Solution Approach 2:
The system changes classification parameters adaptively based on scene analysis. By modifying classification thresholds, color space parameters, and object definitions dynamically according to the specific scene being processed, the system achieves higher identification accuracy without requiring a fundamentally complex system architecture.
2Productivity
If static identification systems with narrow or broad definitions are used, then the system is easy to implement, but power consumption is excessive and performance is inconsistent
Solution Approach 1:
The patent applies partial action by performing comprehensive scene analysis only when necessary to adjust classifications. The system processes images at different resolutions selectively, applying full adaptive classification only when scene changes warrant it, thereby improving processing efficiency while reducing power consumption by avoiding unnecessary full-scene analyses.
Solution Approach 2:
The system implements periodic scene analysis at defined intervals or triggered by specific events (frame changes, scene transitions). This periodic approach allows the system to maintain adaptive classifications without continuously analyzing every frame, thereby improving processing efficiency and reducing power consumption compared to continuous analysis.
3Measurement precision
If adaptive object classification with scene analysis is implemented, then identification accuracy improves, but processing time increases
Solution Approach 1:
The patent segments the image processing into multiple resolution levels. The system first analyzes scenes at downsampled resolutions to determine classification adjustments, then applies these adjustments to full-resolution images. This segmentation approach maintains high identification accuracy while significantly reducing processing time by performing computationally intensive analysis at lower resolutions.
Solution Approach 2:
The system adds a resolution dimension to the processing pipeline, analyzing scenes at multiple scales (downsampled and full resolution). This dimensional approach allows the system to extract classification information efficiently from low-resolution versions while maintaining accurate object identification in full-resolution output, thereby reducing overall processing time.
Data Source
AI summary
The present disclosure relates to methods and apparatus for image processing. The apparatus can generate object mask information for one or more objects in a first image of a plurality of images in a scene. In some aspects, the first image can be at least one of a downscaled image, a down-sampled image, or a low resolution image. The apparatus can also determine one or more object classifications of the first image based on the generated object mask information. Additionally, the apparatus can identify a modification to at least one of the one or more object classifications based on a second image of the plurality of images in the scene. In some aspects, the apparatus can adjust or maintain the one or more object classifications based on the identified modification to at least one of the one or more object classifications.