Image Processing Object Detection via Sub-Region Analysis
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Solution Overview
Problem
High-resolution image processing from multiple image capture devices poses significant computational challenges, particularly in real-time environments, due to the complexity of images and the need for detailed analysis of objects within them, such as in sporting events or security systems.
Innovation Solution
A method involving an apparatus with processing circuitry that locates objects in images, generates a smaller image focused on the objects, and detects key points to facilitate efficient real-time analysis, using techniques like foreground extraction and chromatic distribution analysis to reduce computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are captured by multiple image capture devices, then image quality and resolution are improved, but computational overheads increase significantly
Solution Approach 1:
The patent segments the image processing task into multiple stages: initial object detection on full-resolution images, followed by detailed analysis only on detected objects. This segmentation allows the system to maintain high image quality while reducing overall computational overhead by applying intensive processing only where necessary.
Solution Approach 2:
The patent extracts detected objects from the full image to create separate object images for detailed analysis. This extraction approach isolates the computationally intensive analysis to only the relevant portions of the image, significantly reducing the total computational burden while maintaining high-resolution analysis where needed.
2Measurement precision
If detailed image analysis is performed on complex images with many objects, then analysis accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies different processing qualities to different parts of the image: full detailed analysis is applied only to detected objects, while the rest of the image receives minimal processing. This local quality approach ensures high analysis accuracy for objects of interest while reducing overall processing time by avoiding unnecessary detailed analysis of background or irrelevant areas.
Solution Approach 2:
The patent performs partial analysis by focusing computational resources only on detected objects rather than analyzing the entire image in detail. This partial action approach achieves sufficient analysis accuracy for the objects of interest while significantly reducing processing time compared to comprehensive full-image analysis.
3Productivity
If computationally expensive image processing techniques are applied to maintain real-time analysis, then analysis capability is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary object detection and identification before detailed analysis. This preliminary action identifies which objects require detailed processing, allowing the system to maintain real-time analysis capability by preparing the processing pipeline in advance and avoiding unnecessary detailed processing of all image content.
Solution Approach 2:
The patent introduces an intermediary object detection stage that bridges the gap between simple image capture and complex detailed analysis. This intermediary stage identifies objects of interest and directs them to detailed processing, thereby managing system complexity by creating a structured multi-stage processing flow rather than applying complex processing uniformly to all image data.
Data Source
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AI summary
A method of image processing is provided, the method comprising the steps of locating at least one object in an image of a scene, selecting at least a portion of the image of the scene in accordance with the location of the at least one object in the image of the scene, generating a different image of the at least one object in accordance with the selected portion of the image of the scene, the different image comprising the at least one object and being smaller than the image of the scene and detecting a plurality of points corresponding to parts of the at least one object located in the scene using the different image of the at least one object.