Image Region Grouping for Composition Quality
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Solution Overview
Problem
Millions of photos taken with non-professional cameras often have poor composition, making it difficult to focus on a particular object, as existing technologies lack effective methods for improving image composition in real-time.
Innovation Solution
A computer-implemented method that determines image features, groups them into regions of interest, and allows users to select and crop these regions, using techniques like face detection and wavelet transforms to enhance image composition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If face detection and wavelet transform are used to automatically determine regions of interest, then image composition quality is improved, but device complexity and processing time increase
Solution Approach 1:
The image processing is divided into distinct stages: face detection phase, wavelet transform phase, and region merging phase. Each stage processes specific features independently, allowing the system to manage complexity through modular processing steps while achieving high composition quality
Solution Approach 2:
Face detection is performed as a preliminary step before applying wavelet transform. By pre-identifying face locations, the system reduces the computational scope of subsequent wavelet operations, managing processing complexity while maintaining composition quality
2Manufacturing precision
If multiple image features are detected and processed to create regions of interest, then image composition quality is improved, but processing time increases
Solution Approach 1:
Multiple detected features (faces and wavelet-transformed regions) are merged into unified regions of interest using merging criteria. This consolidation reduces the number of separate processing operations needed and enables faster crop generation while maintaining composition quality
Solution Approach 2:
The system applies wavelet transform at selective levels and regions rather than processing the entire image at maximum detail. By using partial action (selective processing), the system achieves sufficient composition quality without the full computational cost of exhaustive processing
Data Source
AI summary
Implementations generally relate to providing regions of interest in an image. In some implementations, a method includes receiving an image. The method further includes determining one or more image features. The method further includes grouping the one or more image features into one or more regions of interest.