Graphic Matching for Automatic Tiling Puzzle Answer Generation
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Solution Overview
Problem
The manual design of puzzle answers in tiling puzzle games is time-consuming and inefficient, making it difficult to cover all possible solutions and reducing the development efficiency.
Innovation Solution
A graphic processing method that automatically designs puzzle answers by performing matching calculations on paired shape regions and pieces to obtain matching state parameters, which are then traversed to splice the pieces into the target graphic, thereby reducing the time required for puzzle answer design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual design is used to create puzzle answers, then the developer can control the puzzle design, but the time consumption increases and development efficiency decreases
Solution Approach 1:
The system performs self-service by automatically generating puzzle answers through computer vision and pattern recognition. The graphic processing device analyzes the target graphic and pieces independently without human intervention, extracting features and determining matching relationships to automatically construct the puzzle answer sequence.
Solution Approach 2:
The patent replaces the mechanical manual design process with an automated computer-based system. The graphic processing device uses algorithms for feature extraction, pattern recognition, and matching calculation to substitute the manual creative process, thereby eliminating time consumption associated with hand-crafting puzzle answers.
2Adaptability or versatility
If manual design is used for puzzle answers, then the developer can ensure quality, but the coverage of all possible solutions is limited
Solution Approach 1:
The patent segments the puzzle answer design into distinct computational steps: feature extraction from target graphics, feature extraction from pieces, matching calculation between features, and sequence construction. This segmentation allows the system to handle complex puzzles systematically by breaking down the overall complexity into manageable computational tasks.
Solution Approach 2:
The system changes parameters such as feature extraction methods, matching thresholds, and sequence construction algorithms to adapt to different puzzle types and complexities. By adjusting these parameters, the system can cover a wide range of possible solutions across different puzzle configurations without requiring manual redesign for each case.
Data Source
AI summary
A graphic processing method, apparatus and device, and a medium are provided. The graphic processing method includes: acquiring a target graphic and a target sequence of pieces for forming the target graphic, performing matching calculation on the to-be-matched object groups to obtain matching state parameters of target matching object groups, and traversing the matching state parameters to obtain at least one target state parameter group. The target state parameter group is used in splicing the target sequence of pieces into the target graphic, the target state parameter group includes a plurality of target matching state parameters, and one target matching state parameter corresponds to a target matching object group to which one piece belongs.


