Recurrent Neural Network Overlay Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting overlays in images, such as logos and text, require human oversight and are inefficient, as they rely on manual marking of unwanted regions and subsequent inpainting.
Innovation Solution
A method utilizing deep recurrent neural networks to create feature and probability maps from images, where a feature map network characterizes image features and a probability map network assigns probabilities to these features indicating their likelihood of being overlays. The method determines if the probability exceeds a threshold, and upon confirmation, performs processing actions such as removing or inpainting the overlay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual marking and inpainting methods are used to detect and remove overlays, then reliability of overlay detection is improved, but productivity is worsened due to requiring human oversight and manual processing
Solution Approach 1:
The system enables automated overlay detection and removal by training neural networks to independently identify and process overlays without human intervention. The feature map network and probability map network work together to automatically detect overlays, determine their characteristics, and perform removal or inpainting operations, eliminating the need for manual marking while maintaining high reliability through learned patterns from training data
Solution Approach 2:
The patent replaces manual mechanical processes (human marking and inpainting) with automated computational systems. Neural networks substitute for human operators in detecting overlay features, determining their properties, and executing removal operations. This substitution dramatically increases processing speed while maintaining or improving reliability through consistent algorithmic application across all images
2Measurement precision
If deep recurrent neural networks with multiple layers are used, then overlay detection accuracy is improved, but device complexity is worsened
Solution Approach 1:
The complex detection task is segmented into distinct functional components: a feature map network that extracts overlay characteristics, a probability map network that determines overlay likelihood, and a processing module that executes removal actions. This segmentation allows each component to specialize in specific functions, improving overall accuracy while making the system more manageable and interpretable despite the depth of the neural networks
Solution Approach 2:
The patent transforms the detection problem by introducing multiple dimensional representations: feature maps that capture spatial and contextual information, probability maps that add a likelihood dimension, and groundtruth masks that provide binary classification. This multi-dimensional approach enables the network to process complex overlay variations accurately while maintaining structured output that simplifies subsequent processing decisions
Data Source
AI summary
In one aspect, an example method includes a processor (1) applying a feature map network to an image to create a feature map comprising a grid of vectors characterizing at least one feature in the image and (2) applying a probability map network to the feature map to create a probability map assigning a probability to the at least one feature in the image, where the assigned probability corresponds to a likelihood that the at least one feature is an overlay. The method further includes the processor determining that the probability exceeds a threshold, and responsive to the processor determining that the probability exceeds the threshold, performing a processing action associated with the at least one feature.