Symmetry Detection Using Group Equivariant CNN Score Maps
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Solution Overview
Problem
Conventional convolutional neural networks (CNN) face challenges in deriving equivariant results for reflection and rotation in image classification and object detection, leading to issues in symmetry detection tasks.
Innovation Solution
Employing a group equivariant convolution neural network (GECNN) to extract feature maps with equivariance, predict score maps for symmetry types, and integrate them to detect reflection and rotational symmetry elements using a trained neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional convolutional neural networks are used for image classification and object detection, then the models can process images, but they fail to derive equivariant results for reflection and rotation, leading to poor symmetry detection performance
Solution Approach 1:
The patent changes the parameter of equivariance by introducing group equivariant convolution operations that explicitly handle reflection and rotation symmetries. The model parameters are designed to transform predictably under these operations, allowing the network to maintain consistent feature representations across different orientations and reflections, thereby improving symmetry detection accuracy while maintaining adaptability.
2Measurement precision
If group equivariant convolution neural network is used to extract feature maps with equivariance, then symmetry detection accuracy is improved, but the model complexity increases
Solution Approach 1:
The patent segments the model into distinct components: a feature extraction backbone that processes images, and separate symmetry detection heads for different symmetry types (reflection, rotation). This segmentation allows the complex group equivariant operations to be applied only where needed for symmetry detection, rather than throughout the entire network, thereby improving accuracy while managing overall model complexity.
Solution Approach 2:
The patent implements a universal feature extraction backbone that serves multiple purposes: general image feature extraction and providing equivariant features for various symmetry detection tasks. By making the backbone multi-functional, the patent avoids duplicating complex equivariant operations across multiple specialized networks, thus improving symmetry detection while controlling model complexity through shared representations.
3Adaptability or versatility
If multiple score maps are predicted for different symmetry types and classes, then comprehensive symmetry detection is achieved, but computational overhead increases
Solution Approach 1:
The patent merges multiple symmetry detection tasks into a unified framework where a single group equivariant network processes the input image and generates feature maps that are simultaneously used for detecting different symmetry types. The score maps for different symmetries are computed from shared equivariant features, reducing redundant computations and lowering overall computational overhead while maintaining comprehensive symmetry detection coverage.
Solution Approach 2:
The patent performs preliminary feature extraction using group equivariant convolution operations before branching into specific symmetry detection tasks. By pre-computing equivariant feature maps that encode symmetry information, the model avoids re-processing the entire image for each symmetry type, thus achieving comprehensive symmetry detection with reduced computational overhead through this preliminary feature preparation step.
Data Source
AI summary
A method may include predicting, using a machine learning model, score maps for each of a plurality of pre-set classes for predetermined symmetry types, using a plurality of pixels included in the feature map, and detecting a symmetry element of the input image based on the predicted score maps.