Selective Image Processing via Contextual Bounding Boxes
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Solution Overview
Problem
Existing digital image processing systems require unnecessary computing resources as they process the entirety of an image for contextual purposes, despite only specific portions being relevant, leading to inefficiency.
Innovation Solution
A method that identifies relevant objects or groups within a digital image, overlays bounding boxes around them, and performs further image processing exclusively within these areas, excluding irrelevant parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire digital image is processed for contextual analysis, then comprehensive object detection can be performed, but computing resources and processing time are wasted on irrelevant portions of the image
Solution Approach 1:
The patent divides the digital image into multiple regions of interest (ROIs) based on contextual analysis. Object detection is performed selectively within each ROI rather than processing the entire image, thereby maintaining detection accuracy while significantly reducing computational resources and processing time required for image analysis.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their relevance to the contextual query. High-priority regions receive full object detection processing, while low-priority regions are excluded from processing, optimizing the balance between analysis accuracy and processing efficiency.
2Reliability
If object detection is performed on the entire image, then all objects can be identified, but computational demands increase unnecessarily
Solution Approach 1:
The patent performs preliminary contextual analysis on the digital image to identify regions containing objects relevant to the specified context before performing object detection. This preliminary step allows the system to exclude irrelevant regions from subsequent detection processes, ensuring detection completeness for relevant objects while reducing computational resource consumption.
Solution Approach 2:
The patent performs object detection only on partial regions of the image that are identified as relevant to the contextual query, rather than processing the entire image. This partial action approach ensures that all relevant objects are detected while avoiding unnecessary computational expenditure on irrelevant areas.
3Productivity
If selective processing within bounding boxes is implemented, then processing efficiency improves, but the complexity of identifying and overlaying bounding boxes increases
Solution Approach 1:
The patent employs a contextual analysis module that performs multiple functions: it identifies regions of interest, determines their priority levels, and generates bounding boxes for selective processing. This multi-functional approach consolidates several operations into a single system component, improving processing efficiency while managing system complexity through functional integration.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for selective image processing. The embodiment may include receiving a digital image for selective image processing. The embodiment may include identifying a context of the selective image processing. The embodiment may include performing object detection within the received digital image to identify one or more depicted objects based on the identified context. The embodiment may include overlaying, on at least one portion of the received digital image, one or more bounding boxes which encompass relative positions of the identified one or more depicted objects. The embodiment may include performing the selective image processing exclusively within the one or more overlaid bounding boxes.