Vector Image Similarity Detection via Transformation Matrices
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Solution Overview
Problem
Existing digital image editing tools face difficulties in efficiently and accurately identifying and modifying similar image objects within vector images, often resulting in time-consuming manual processes or significant computational resources, with high rates of false positives and negatives.
Innovation Solution
A system that utilizes a graphical user interface to select a key path in a vector image, performs a pairwise comparison with candidate paths using transformation matrices to determine similarity, and applies edits simultaneously to similar objects, leveraging parallel processing to ensure fast and accurate identification and modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processing is used to identify similar vector image objects, then productivity is improved, but measurement precision deteriorates due to false positives and negatives
Solution Approach 1:
The system provides visual feedback by displaying bounding boxes around identified similar objects, allowing users to verify and correct automated identification results. This feedback loop enables users to see which objects were selected and make adjustments if false positives or negatives occur, thereby maintaining high productivity while improving measurement precision through user verification.
Solution Approach 2:
The system introduces an intermediary visual indication layer between the automated processing and the final editing action. The bounding boxes serve as an intermediary that translates automated identification results into a form that users can easily verify and adjust, bridging the gap between fast automated processing and accurate final results.
2Measurement precision
If manual identification of similar image objects is performed, then measurement precision is improved, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The system segments the identification task by automatically detecting and bounding similar objects, then presenting them to the user for verification. This segmentation allows the computationally intensive automated detection to handle the bulk of the work while user intervention is only needed for verification and correction, combining the speed of automation with the precision of manual review.
Solution Approach 2:
The system performs preliminary automated identification and displays bounding boxes around potential matches before the user performs any editing actions. This preliminary action reduces the user's workload by pre-identifying similar objects, allowing users to focus only on verification and correction rather than manual identification from scratch.
3Ease of operation
If existing digital image editing tools are used to locate similar objects, then ease of operation is maintained, but difficulty of detecting and measuring increases due to inability to efficiently find all similar objects
Solution Approach 1:
The system introduces visual bounding boxes as an intermediary between the user's simple selection action and the complex task of finding all similar objects. Users simply select an object as before, but the system automatically generates and displays bounding boxes around all similar objects, making the detection capability transparent and easy to use without increasing operational complexity.
Solution Approach 2:
The system copies the visual indication style of traditional selection tools by using bounding boxes, a familiar concept from existing image editing software. This copying of visual language makes the new automated detection feature intuitive and easy to understand, maintaining ease of operation while dramatically improving detection capability.
Data Source
AI summary
A selection of a key path of a vector image displayed using a graphical user interface (GUI) may be received, via the GUI. At least one candidate path of the vector image is identified. A pairwise comparison of the key path with the at least one candidate path is executed, the pairwise comparison including characterization of a translation, scaling, and rotation of the at least one candidate path with respect to the key path. Based on the pairwise comparison, it is determined that the at least one candidate path is within a similarity threshold defined with respect to the key path. A visual indicator of the at least one candidate path within the GUI, identifying the at least one candidate path as being within the similarity threshold, may be provided.