Image Symmetry Detection and Manipulation Under Perspective Distortion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional automated schemes for symmetry detection in digital images are limited to specific types of symmetry or conditions, such as the absence of perspective distortion, requiring manual input for image manipulation, which is tedious and often results in visually poor outcomes.
Innovation Solution
A system that automatically detects multiple kinds of symmetry transformations, including translation, rotation, scale, and reflection, under perspective or affine distortion, by identifying local symmetries, clustering them, and generating global symmetries to accommodate perspective distortion, allowing for pixel-based manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional automated symmetry detection is used, then automation is achieved, but it is limited to specific symmetry types and conditions (e.g., absence of perspective distortion)
Solution Approach 1:
The patent segments the symmetry detection process into local symmetry detection (at small scales where distortion is minimal) and global symmetry generation (combining local results while accounting for perspective distortion). This segmentation enables the system to handle multiple symmetry types and perspective distortions that conventional single-stage approaches cannot manage.
Solution Approach 2:
The patent introduces a new dimension to symmetry detection by detecting local symmetries at small scales before aggregating them into global symmetries. This two-scale approach (local + global) adds a dimensional layer to the detection process, enabling handling of perspective distortion and multiple symmetry types simultaneously.
2Adaptability or versatility
If manual direction is used to specify symmetry, then flexibility in handling complex images is achieved, but tedious and repetitive work is imposed on the end-user
Solution Approach 1:
The system performs self-service by automatically detecting multiple kinds of symmetry transformations (translation, rotation, scale, reflection) without requiring manual input. The automated local symmetry detection and global symmetry generation processes eliminate the need for users to manually specify symmetries, reducing tedious work while maintaining adaptability to complex images.
3Extent of automation
If conventional automated symmetry schemes are used, then automation is achieved, but visually poor results are produced due to limited symmetry types
Solution Approach 1:
The patent implements universality by detecting multiple kinds of symmetry transformations (translation, rotation, scale, and reflection) within a single automated framework. This multi-functional approach allows the system to handle diverse symmetry types and perspective distortions, producing high-quality image manipulation results that conventional single-type automated schemes cannot achieve.
Solution Approach 2:
The system changes parameters dynamically by adapting to different symmetry types and perspective distortion levels. The local symmetry detection operates at small scales where distortion is minimal, while global symmetry generation adjusts for perspective effects, allowing the system to maintain high manipulation quality across various image conditions.
4Measurement precision
If local symmetries are detected at small scales, then perspective distortion effects are minimized, but global symmetries must be synthesized from multiple local results
Solution Approach 1:
The patent segments the symmetry detection into local (small scale, low distortion) and global (large scale, high distortion) components. By detecting local symmetries first where perspective distortion is minimal, the system achieves high measurement precision. The global synthesis then combines these local results while accounting for distortion, managing complexity through structured decomposition.
Solution Approach 2:
The system performs preliminary local symmetry detection before global synthesis. By completing the local detection stage first (where distortion effects are minimal), the system establishes accurate symmetry measurements that can then be reliably aggregated into global symmetries, reducing the overall complexity of the synthesis process.
Data Source
AI summary
Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map. The image modification module produces a manipulated image by manipulating the original image under global symmetry constraints imposed by the global symmetry association map.