Modified Panoptic Labeling Neural Network for Accurate Image Infill
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Solution Overview
Problem
Existing digital image processing systems using panoptic labeling neural networks often misidentify object labels due to training on naive infill shapes, leading to poor segmentation information and incorrect infill operations in digital graphics editing systems.
Innovation Solution
A modified panoptic labeling system that extracts feature vectors from input and annotated images, compares them to determine similarity, and generates mask labels based on probabilities derived from distances between mask pixels and object pixels in the input image, using a modified panoptic labeling neural network trained on comparable infill shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a panoptic labeling neural network is trained on naive infill shapes (rectangles), then the training process is simple and fast, but the object label identification accuracy deteriorates
Solution Approach 1:
The patent changes the shape parameter of training infill data from simple rectangles to complex shapes that match actual image content. By modifying the geometric parameters of training samples to reflect real-world object diversity, the system improves labeling accuracy while maintaining training efficiency through automated shape generation from image segmentation.
Solution Approach 2:
The system performs preliminary segmentation and shape extraction from training images before neural network training. By pre-processing images to extract accurate object shapes and generating corresponding infill masks, the system prepares high-quality training data that reflects actual image content, thereby improving labeling accuracy without significantly increasing training time.
2Ease of manufacture
If a panoptic labeling neural network is trained on naive infill shapes, then the training data preparation is simple, but the segmentation information quality deteriorates
Solution Approach 1:
The system automatically generates complex infill shapes and training data by processing actual image content through segmentation algorithms. Instead of requiring manual creation of complex training shapes, the system self-generates training data with accurate object boundaries and shapes, maintaining ease of implementation while dramatically improving segmentation quality.
3Productivity
If pixels are modified using misidentified labels, then the infill operation completes quickly, but the image content coherence deteriorates
Solution Approach 1:
The system uses the neural network's probability outputs and confidence scores as feedback to verify label accuracy before performing infill operations. By incorporating feedback mechanisms that check labeling confidence and consistency, the system ensures that only high-confidence, accurate labels are used for pixel modification, maintaining image coherence while preserving operational efficiency.
Data Source
AI summary
A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.