Instance Segmentation via Pixel Clustering and Graph Coloring
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Solution Overview
Problem
Instance segmentation in cluttered scenes with occlusions and varying object numbers is challenging due to difficulties in accurately differentiating objects and assigning unique identifiers to pixels, as existing methods struggle with occlusions and the complexity of pixel-wise semantic labeling.
Innovation Solution
A system and method that utilize a neural network to generate a learning objective based on pair-wise relationships between pixels, perform graph coloring to assign distinct colors to adjacent object instances, and conduct connected component extraction to recover each object instance, allowing for accurate instance segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If proposal-based methods are used to perform instance segmentation, then object proposals and their classes are predicted first followed by foreground-background segmentation in each bounding box, but the method becomes more complex and computationally intensive
Solution Approach 1:
The patent extracts and removes the object proposal generation step from the instance segmentation pipeline. By directly performing instance segmentation without generating object proposals first, the method eliminates the computational overhead and complexity associated with proposal generation while maintaining segmentation accuracy through direct pixel-wise clustering and labeling
Solution Approach 2:
The patent applies segmentation by dividing the image into pixel clusters through unsupervised learning and assigning instance labels through graph coloring. This approach segments objects at the pixel level directly without requiring bounding box proposals, thereby simplifying the overall process while achieving precise instance segmentation
2Measurement precision
If pixel-wise semantic labeling is performed to differentiate each object at pixel level, then instance segmentation accuracy is improved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent performs preliminary unsupervised pixel-wise clustering to group pixels into clusters before performing instance labeling. This preliminary action organizes pixels into meaningful groups based on visual similarity, which accelerates the subsequent instance segmentation process while maintaining pixel-level accuracy
Solution Approach 2:
The patent introduces pixel clusters as an intermediary representation between the input image and the final instance segmentation output. These clusters serve as a intermediate step that captures visual similarities and facilitates faster instance labeling through graph coloring, reducing the computational burden of direct pixel-wise processing
3Adaptability or versatility
If instance labeling is performed in cluttered scenes with occlusions and varying object numbers, then comprehensive object identification is achieved, but the task becomes significantly more challenging and error-prone
Solution Approach 1:
The patent changes the parameter space by representing instances through graph coloring constraints rather than direct pixel classification. By formulating instance labeling as a graph coloring problem where adjacent pixels must have different colors (instance labels), the method robustly handles occlusions and varying object counts while maintaining labeling accuracy through combinatorial optimization
Data Source
AI summary
Systems and methods for performing instance segmentation. A memory stores instructions for executing processes for performing instance segmentation and a processor configured to execute the instructions. The processes include: generating a learning objective that uses pair-wise relationships between pixels in an input image; sampling pixels in each object instance to determine whether the sampled pixels are within a same object instance; training a neural network using the learning objection, wherein the neural network is configured to make pixel-wise predictions and to assign a cluster index to each pixel of the input image, with each pixel cluster being an object instance; performing graph coloring to assign a color to each object instance, with adjacent object instances having different colors; performing connected component extraction to recover each object instance based on the graph coloring; and generating a rendered image having the assigned color applied to each object instance.