Photon Energy Image Processing for Low-Compute Object Recognition
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Solution Overview
Problem
Visual image processing technologies face high energy consumption and calculation demands due to the complexity of models and the need for large quantities of sample data, necessitating a low-cost and efficient solution.
Innovation Solution
A visual image processing method based on a strain mechanism that acquires photon energy information, extracts variable elements, generates energy fields with feature information, and performs spatial reasoning to obtain a geometric shape of an object, reducing the need for extensive training and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning technology based on neural networks and large-scale model technology is used for visual image processing, then object recognition and scene understanding capabilities are improved, but energy consumption and calculation requirements increase significantly
Solution Approach 1:
The patent extracts and processes only the essential variable elements (fluctuation difference, dimensionality, frequency) from photon energy information, rather than processing complete image data through complex neural networks. This extraction approach maintains recognition accuracy while significantly reducing computational load and energy consumption.
Solution Approach 2:
The patent segments the image processing task into distinct stages: acquiring photon energy information, extracting variable elements, generating energy fields, and performing spatial reasoning. This segmentation allows each stage to use optimized processing methods, reducing overall energy consumption compared to monolithic deep learning approaches.
2Productivity
If complex models with increased parameters are used for visual image processing, then processing capability is improved, but hardware requirements and costs increase
Solution Approach 1:
The patent replaces complex mechanical/computational systems (neural networks with billions of parameters) with a simplified physical model based on strain mechanisms and energy field theory. This substitution maintains processing capability while dramatically reducing hardware complexity and cost.
Solution Approach 2:
The patent changes the fundamental parameters being processed from raw pixel data to photon energy variable elements (fluctuation difference, dimensionality, frequency). This parameter transformation enables effective processing with simpler hardware that doesn't require massive computational resources.
3Reliability
If large quantities of sample data are collected for model training under various environmental conditions, then model robustness is improved, but data acquisition difficulty and time increase
Solution Approach 1:
The patent enables the system to achieve robustness through self-adaptive processing of variable elements rather than relying on pre-trained models. The strain mechanism and energy field analysis inherently adapt to different environmental conditions without requiring extensive training data, eliminating the time-consuming data collection and model training phases.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method achieves low-energy image processing by accurately identifying objects in various environments without training, thus reducing calculation and energy consumption.
Implementation Method 1
acquiring a plurality of pieces of photon energy information by the CCD camera or the infrared camera, and converting the plurality of pieces of photon energy information into an electronic signal
Data Source
AI summary
The invention discloses a visual image processing method and apparatus based on a strain mechanism, a device and a medium. The visual image processing method based on a strain mechanism includes: acquiring a plurality of pieces of photon energy information based on a CCD camera or an infrared camera, and extracting variable elements between photons based on the photon energy information; generating a plurality of energy fields according to the variable elements; and performing spatial reasoning according to the feature information of the energy fields to obtain a geometric shape of an object. The invention belongs to the field of image processing, realizes a “general algorithm” for visual image processing, solves global application problems, may accurately and reliably find a target of interest in any environments, and reduces energy consumption.