Machine-Perspective Signal Processing for Sensor-Specific Optimization
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Solution Overview
Problem
Existing machine-perspective signal processing methods and apparatuses are not optimized for machine tasks, as they are often based on human-centric sensors and signal processing techniques that may not be ideal for machine vision applications.
Innovation Solution
The proposed method involves selecting a first image sensor from a pool of sensors and processing its output using both sensor-specific image processing with individual settings and sensor-agnostic signal processing with common settings, tailored to specific machine tasks and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human-centric sensors and signal processing techniques are used, then compatibility with human vision applications is improved, but optimization for machine tasks deteriorates
Solution Approach 1:
The patent segments the signal processing pipeline into distinct modules: sensor-specific processing (tailored to individual sensor characteristics) and sensor-agnostic processing (common to all sensors). This segmentation allows independent optimization for both human vision compatibility and machine task performance without compromising either aspect.
Solution Approach 2:
The system dynamically selects and switches between different signal processing pipelines based on the specific machine task requirements. Rather than using a fixed human-centric pipeline, the processing approach adapts in real-time to optimize for detection, tracking, classification, or segmentation tasks while maintaining human vision compatibility when needed.
2Measurement precision
If sensor-specific image processing with individual settings is used, then performance for specific sensors is improved, but system complexity increases
Solution Approach 1:
The processing system is divided into sensor-specific modules (handling individual sensor characteristics) and sensor-agnostic modules (handling common processing tasks). This segmentation allows precise optimization for each sensor type while managing overall system complexity through modular architecture and reusable common processing components.
3Reliability
If multiple sensors with different wavelength bands are used, then machine task performance is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal sensor-agnostic signal processing pipeline that can handle multiple sensor types and wavelength bands (visible light, infrared, etc.) through a single unified processing architecture. This multi-functional approach enables the system to process data from diverse sensors without requiring separate dedicated processing chains for each sensor type, thus improving machine task performance while controlling device complexity.
Data Source
AI summary
A machine-perspective signal processing method and apparatus are provided. The machine-perspective signal processing method includes selecting a first sensor from among a plurality of sensors, processing output data of the first sensor using first sensor-specific signal processing having a first individual setting specialized for the first sensor and sensor-agnostic signal processing having a common setting of the plurality of sensors, and performing a first task based on the processed output data.