Adaptive Image Granulation for Robust Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image classification systems for vehicle surroundings monitoring are susceptible to noise and adversarial examples when dealing with high-resolution images, leading to reduced accuracy and increased computational demands, without a guarantee of improved performance.
Innovation Solution
A trainable pre-processing unit reduces image detail to an optimal level based on a trained relationship, using methods like wavelet transformation or autoencoders, to enhance robustness against noise and adversarial attacks while optimizing hardware requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If high-resolution image data is used from advanced sensors, then image detail and information richness are improved, but classification accuracy decreases due to increased noise and susceptibility to adversarial examples
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the resolution parameter of image data based on scene characteristics. The system transforms high-resolution images into lower-resolution representations when appropriate, changing the physical parameter of image detail to optimize the trade-off between information retention and noise reduction, thereby improving classification reliability without excessive loss of useful information.
Solution Approach 2:
The patent implements partial action by selectively reducing resolution only in regions or conditions where it benefits classification. Rather than uniformly processing all images at full resolution, the system applies resolution reduction partially based on scene complexity, object distance, and classification confidence, avoiding unnecessary processing of already-sufficient lower-resolution data.
2Loss of information
If high-resolution image data is processed, then more information is available, but training data requirements and hardware resources increase
Solution Approach 1:
The system changes the resolution parameter dynamically during training and operation, allowing the model to learn from varied resolution inputs. This parameter variability enriches the effective training data by teaching the classifier to be robust across different detail levels, reducing the need for excessively large training datasets while maintaining information availability when needed.
Solution Approach 2:
The patent introduces dynamics by making the resolution parameter adaptive rather than fixed. The system can adjust image resolution based on scene requirements, creating a dynamic processing pipeline that optimizes the balance between information retention and computational resource consumption during both training and deployment phases.
3Loss of information
If high-resolution image data is transmitted, then complete information is preserved, but bandwidth requirements and transmission time increase
Solution Approach 1:
The system applies parameter changes to the resolution of transmitted image data by dynamically adjusting it based on scene importance and distance. Critical regions maintain higher resolution while less critical areas are downsampled, changing the physical parameter of data size to reduce transmission time while preserving essential information completeness for safety-critical classifications.
Solution Approach 2:
The patent implements local quality by applying different resolution levels to different regions of the image based on their importance. Salient objects and regions receive higher resolution treatment while background areas are reduced, creating a non-uniform quality distribution that optimizes bandwidth usage while maintaining information completeness where it matters most for classification.
Data Source
AI summary
A device for classifying image data includes a trainable pre-processing unit configured to retrieve, from a trained context, and based on the image data, at least one specification in terms of how a degree of granulation of the image data is to be reduced, and to reduce the degree of granulation of the image data in accordance with the at least one specification. The device further includes a trainable classifier configured to map the granulation-reduced image data onto an assignment to one or more classes of a specified classification.