Shape Detection Using Edge Parallelism and Fit Scoring
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Solution Overview
Problem
Current shape detection systems in computer vision face challenges with slow performance, intolerance to distortions, and inefficiency in detecting 2D shapes in images, especially when dealing with perspective changes and noise, and often require extensive edge detection across the entire image.
Innovation Solution
A method that generates a shape model with a predetermined scale and orientation, identifies edges parallel to the shape model within a defined distance, selects the strongest edges, and determines a fit score to identify the shape that most closely matches the model, using a combination of edge detection and random sample consensus (RANSAC) to construct the shape, thereby reducing computational load and improving tolerance to distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the generalized Hough transformation is used to detect shapes by populating histograms of parameters, then shape detection can be performed, but the performance becomes slow when there are many pixels or many parameters to define the shape
Solution Approach 1:
The patent segments the shape detection process into distinct phases: edge detection, line fitting using Hough transformation, and shape classification. By dividing the complex detection task into manageable segments, the system achieves both accuracy and efficiency, avoiding the computational burden of processing all pixels against all parameters simultaneously.
Solution Approach 2:
The patent applies partial action by performing edge detection only on relevant image regions and using the Hough transformation selectively for line detection rather than processing the entire image with all possible parameters. This reduces computational load while maintaining detection accuracy for the target shapes.
2Ease of manufacture
If discrete bins are used in the Hough transformation to store edge parameters, then the transformation can be implemented, but the discrete nature of bins can cause the system to miss the solution
Solution Approach 1:
The patent dynamically adjusts bin resolution and histogram parameters based on the specific detection task and image characteristics. By changing parameters such as bin size, number of bins, and threshold values, the system optimizes between implementation simplicity and detection accuracy, avoiding the rigidity of fixed discrete bins.
3Measurement precision
If the Hough approach is used for shape detection, then shape recognition can be performed, but the system is not tolerant to distortions of the shape and performance decreases with pose and perspective changes
Solution Approach 1:
The patent implements dynamic adaptation by adjusting detection parameters, bin resolutions, and threshold values based on the detected pose and perspective changes. The system dynamically modifies its detection strategy to accommodate distorted shapes, maintaining recognition accuracy across varying conditions rather than using a static detection approach.
Solution Approach 2:
The patent applies different detection strategies and parameter settings to different regions of the image based on local characteristics such as distortion level, noise, and shape orientation. By making the detection quality adaptive to local conditions, the system maintains high accuracy even when parts of the shape are distorted.
4Measurement precision
If edge detection is performed across the entire image to detect shapes, then comprehensive shape detection can be achieved, but the computational load and processing time increase significantly
Solution Approach 1:
The patent extracts only the essential features needed for shape detection by performing edge detection selectively in regions likely to contain shapes, rather than processing the entire image. It extracts relevant edge segments and feeds them to the Hough transformation, significantly reducing computational load while maintaining detection completeness.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for shape detection are disclosed. In one aspect, a method includes the actions of generating a shape model that includes a predetermined shape with a predetermined scale and predetermined orientation. The actions further include receiving an image. The actions further include identifying edges that are parallel to the side of the shape model and that are a predetermined distance from the side of the shape model. The actions further include selecting a plurality of edges that likely correspond to edges of a shape that is similar to the shape model. The actions further include determining a fit score between the plurality of edges and each shape of a plurality of shapes that are similar to the shape model. The actions further include identifying a particular shape in the image that most closely fits the shape model.