Gradient Feature Extraction for Pattern Recognition
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Solution Overview
Problem
Existing pattern recognition methods require high-dimensional feature extraction, leading to increased calculation costs and prolonged processing times, as they often include non-useful parameters and complex feature extraction processes.
Innovation Solution
A learning device and method that extracts gradient feature amounts and sum difference feature amounts based on luminance variations, reducing the need for high-dimensional features by using a gradient feature extraction unit and sum difference feature extraction unit to acquire learning parameters efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-dimensional feature extraction is performed to improve identification accuracy, then identification accuracy is improved, but calculation cost increases and processing time is prolonged
Solution Approach 1:
The patent extracts only the essential gradient direction and gradient intensity features from the image, discarding non-useful parameters. This is achieved by calculating gradient features at each pixel location and then aggregating them by direction, keeping only the dominant gradient directions and their intensities, thereby reducing feature dimensionality while maintaining identification accuracy
Solution Approach 2:
The patent creates a simplified representation of the original image by copying only the essential gradient information (direction and intensity) rather than using the full high-dimensional pixel data. This gradient feature map serves as a compressed copy that retains the most important structural information for pattern recognition
2Measurement precision
If complex feature extraction processes are used to improve identification accuracy, then identification accuracy is improved, but processing time is prolonged
Solution Approach 1:
The patent segments the feature extraction process into distinct stages: gradient calculation at each pixel, grouping by gradient direction, and aggregation to compute gradient magnitude for each direction. This segmentation allows for efficient computation by processing small local regions independently and then combining results, reducing overall processing time
Solution Approach 2:
The patent performs gradient feature extraction at all pixel locations (excessive action) but then selectively aggregates only the essential directional information. This approach ensures no useful information is lost while avoiding the computation of unnecessary high-dimensional features, achieving a balance between completeness and efficiency
3Measurement precision
If high-dimensional feature vectors are used to maintain identification accuracy, then identification accuracy is improved, but calculation cost becomes extremely high
Solution Approach 1:
The patent transforms the feature representation from high-dimensional pixel values to a compact set of gradient direction angles and intensities. By changing the parameter space from raw pixel intensities to gradient features, the system achieves the same identification accuracy with dramatically reduced computational requirements for both training and recognition
Data Source
AI summary
A learning device includes a gradient feature extraction unit which extracts a gradient feature amount including a gradient direction at each coordinate and a gradient intensity value thereof based on an amount of variation between luminance at each coordinate of an inputted learning target pattern and luminance at a periphery thereof, a sum difference feature extraction unit which calculates a predetermined sum difference feature amount by adding the gradient intensity values according to the gradient directions included in a predetermined gradient range indicating a range of the predetermined gradient direction based on the extracted gradient feature amount and subtracting the gradient intensity values according to the gradient directions included in the other gradient range adjacent to the predetermined gradient range from the calculated sum, and a learning unit which acquires a learning parameter at each coordinate.