Zero-Sum Convolution Filters for Brightness-Insensitive Image Recognition
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Solution Overview
Problem
Image recognition models are affected by brightness variations in target images, leading to reduced recognition accuracy, especially in environments with changing illumination.
Innovation Solution
A learning model is trained using zero-sum convolution filters, where the total sum of coefficients in convolution filters approaches zero, reducing the influence of brightness and improving recognition accuracy across varying lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional learning models are used for image recognition, then recognition processing can be performed, but recognition accuracy deteriorates when brightness in the target image varies
Solution Approach 1:
The patent applies parameter changes by modifying the convolution filter coefficients to satisfy the zero-sum constraint (sum of coefficients equals zero). This parameter modification makes the learning model insensitive to brightness variations while maintaining recognition accuracy across different lighting conditions
Solution Approach 2:
The zero-sum convolution filter creates a universal learning model that functions effectively across varying brightness conditions. The model achieves multi-functionality by simultaneously handling both bright and dark images with consistent accuracy, eliminating the need for separate processing paths
2Reliability
If the learning model is trained with zero-sum convolution filters, then influence of brightness is reduced, but training complexity increases
Solution Approach 1:
The learning model performs self-service by automatically adapting to brightness variations through the zero-sum constraint during training. The model self-regulates its response to different lighting conditions without requiring external brightness correction or manual adjustment, achieving reliability while keeping training procedures straightforward
Data Source
AI summary
The present technology relates to a method for generating a learning model, a program, and an information processing apparatus that enable image recognition with reduced influence of brightness in a target image.A learning model is trained such that a total sum of coefficients in one or more channels of at least one or more convolution filters among the convolution filters for a first layer of a neural network including a plurality of the convolution filters approaches zero, the neural network being applied to the learning model that performs recognition processing on input data.