Dynamic Image Segmentation Using Adaptive Class and Cluster Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image segmentation technologies struggle to adapt dynamically to changes in the complexity of the surrounding environment, particularly when the number of available classes or clusters needs to be adjusted based on the device's position.
Innovation Solution
An electronic device equipped with a processor that extracts feature data from input images, calculates class and cluster maps using adaptable classifier and clustering layers, and generates image segmentation data. The device dynamically selects the appropriate number of classes or clusters based on the environmental complexity, updating the layers with training data collected during movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of available classes or clusters is increased to handle complex environments, then segmentation accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent implements dynamic adaptation by allowing the electronic device to adjust the number of available classes or clusters based on the detected environmental complexity. The system transitions from a static classification approach to a dynamic one where the model structure adapts in real-time to match the scene complexity, optimizing both accuracy and computational efficiency.
Solution Approach 2:
The patent changes the parameter of the number of available classes or clusters based on environmental conditions. By detecting environmental complexity and adjusting this parameter dynamically, the system optimizes segmentation accuracy for complex scenes while maintaining efficiency for simpler scenes, resolving the contradiction between precision and complexity.
2Adaptability or versatility
If the number of available classes or clusters is increased to adapt to complex environments, then adaptability is improved, but processing time increases
Solution Approach 1:
The system dynamically adjusts the number of classes or clusters based on real-time environmental complexity detection. This dynamic adaptation allows the system to be highly adaptable to complex environments when needed while maintaining fast processing for simpler environments, eliminating the need to always use a large fixed number of classes.
Solution Approach 2:
The patent performs preliminary detection of environmental complexity before executing the full segmentation process. By assessing the environment first and adjusting the model parameters in advance, the system prepares the optimal configuration for the upcoming processing task, avoiding unnecessary computational overhead and reducing processing time.
3Device complexity
If a fixed number of classes is used in the classifier layer, then device complexity is reduced, but adaptability to different environments deteriorates
Solution Approach 1:
The patent transforms the static classifier layer with a fixed number of classes into a dynamic structure where the number of classes can be adjusted based on environmental complexity. This allows the system to maintain simple model structures for basic environments while adapting to complex environments when necessary, resolving the contradiction between simplicity and adaptability.
Data Source
AI summary
An electronic device extracts feature data from an input image, calculates one or more class maps from the feature data using a classifier layer, calculates one or more cluster maps from the feature data using a clustering layer, and generates image segmentation data using the one or more class maps and the one or more cluster maps.