Predictive Scene Modeling for Low-Bandwidth Machine Vision
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Solution Overview
Problem
Current machine vision systems face challenges in efficiently processing, analyzing, and storing captured scenes of environments due to high computational demands and data volume, particularly in real-time applications with limited bandwidth, and struggle with precise boundary detection of objects in complex or poorly lit conditions.
Innovation Solution
The system predicts future scenes based on a model of the environment, allowing for reduced processing time by comparing predicted and observed scenes, and applies a vibration stimuli to objects to enhance boundary detection by identifying vibrating pixels, thereby improving object selection and movement planning in environments like piles of physical objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If machine vision systems capture and process scenes in real-time, then processing speed and responsiveness are improved, but computational demands and data volume increase significantly
Solution Approach 1:
The patent extracts only the essential information from captured scenes by identifying and removing redundant data. The system processes scenes to extract key features and objects while discarding unnecessary visual information, thereby reducing data volume while maintaining processing speed.
Solution Approach 2:
The patent segments the visual data into meaningful components such as objects, boundaries, and features. By dividing the scene into discrete elements that can be processed independently, the system reduces the overall computational burden and data volume while maintaining real-time processing capability.
2Speed
If machine vision systems transmit scene data over limited bandwidth, then real-time monitoring is improved, but transmission efficiency decreases due to high data volume
Solution Approach 1:
The patent extracts only the critical information needed for monitoring purposes before transmission. By removing redundant visual data and transmitting only essential features and changes, the system achieves efficient bandwidth utilization while maintaining real-time monitoring capability.
3Measurement precision
If machine vision systems attempt to detect boundaries in complex or poorly lit conditions, then detection accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary processing of the captured scene to enhance boundary detectability before attempting detection. This includes preprocessing steps such as noise reduction, contrast enhancement, and edge preprocessing that simplify the detection task and reduce computational complexity while improving accuracy in challenging conditions.
Data Source
AI summary
A first method comprising: predicting a scene of an environment using a model of the environment and based on a first scene of the environment obtained from sensors observing scenes of the environment; comparing the predicted scene with an observed scene from the sensors; and performing an action based on differences determined between the predicted scene and the observed scene. A second method comprising applying a vibration stimuli on an object via a computer-controlled component; obtaining a plurality of images depicting the object from a same viewpoint, captured during the application of the vibration stimuli. The second method further comprising comparing the plurality of images to detect changes occurring in response to the application of the vibration stimuli, which changes are attributed to a change of a location of a boundary of the object; and determining the boundary of the object based on the comparison.