Image Segmentation Boundary Smoothing via Local Pixel Analysis
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Solution Overview
Problem
Current image segmentation methods face challenges in accurately segmenting foreground objects without prior knowledge of their size, shape, or color, especially when multiple objects with similar characteristics are present, often resulting in rough boundaries that are not visually appealing.
Innovation Solution
An image processing system and method that uses a local pixel analysis window to determine the frequency of foreground pixels relative to background pixels, selecting cost values to generate a smoothed object boundary for precise segmentation, allowing for dynamic separation of foreground objects from the background using a classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image segmentation methods are used without prior knowledge of object characteristics, then the method can be applied to unknown objects, but the segmentation accuracy and boundary quality deteriorate resulting in rough boundaries
Solution Approach 1:
The patent applies segmentation by dividing the image processing into distinct stages: initial segmentation using color quantization to separate foreground and background regions, followed by boundary refinement through cost-based pixel classification. This multi-stage segmentation approach enables the system to handle unknown objects while progressively improving boundary quality from rough initial segments to smooth final boundaries.
Solution Approach 2:
The patent implements local quality by analyzing pixels in local neighborhoods rather than globally. The cost value for each pixel is determined based on local color distribution and frequency of occurrence within its region. This local analysis allows the boundary to adapt to local object characteristics, producing smooth boundaries that conform to actual object shapes even when global object properties are unknown.
2Adaptability or versatility
If segmentation is performed without prior knowledge of object size, shape, and color, then the method can handle diverse objects, but the segmentation becomes computationally challenging and less accurate
Solution Approach 1:
The patent applies preliminary action by performing color quantization and initial region segmentation before detailed boundary refinement. This preliminary classification groups pixels into foreground and background regions based on color characteristics, providing a rough but computationally efficient first pass that reduces the complexity of subsequent boundary smoothing operations.
Solution Approach 2:
The patent uses partial action by applying the computationally intensive cost-based boundary refinement only to pixels near the foreground-background interface, rather than processing all pixels uniformly. The cost value calculation and mask updates are focused on boundary regions, reducing overall computational complexity while maintaining accuracy where it matters most.
3Adaptability or versatility
If multiple objects with similar characteristics are present in the image, then the segmentation problem becomes more difficult, but current methods may fail to segment or produce rough boundaries
Solution Approach 1:
The patent applies local quality by determining cost values and pixel frequencies based on local neighborhoods rather than global image properties. Each pixel's classification is influenced by its local color distribution and surrounding pixels, allowing the algorithm to distinguish between similar objects based on local variations. This local analysis enables precise separation of multiple objects with similar characteristics by adapting to local differences at object boundaries.
Solution Approach 2:
The patent implements feedback through iterative mask updates and cost value recalculations. The segmentation process repeatedly refines the foreground-background classification by using the results of previous iterations to inform subsequent decisions. This feedback mechanism allows the system to progressively improve object separation accuracy, resolving ambiguities between similar objects through multiple passes of cost-based pixel classification.
Data Source
AI summary
Various aspects of an image processing system and method for object boundary smoothening for image segmentation, includes receipt of a user input to enable selection of a foreground object in an input color image. A frequency of occurrence of foreground pixels with respect to background pixels is determined for a plurality of pixels within a local pixel analysis window. The local pixel analysis window is positioned in a first region of the input color image to encompass at least a first pixel to be validated for a correct mask value. A first cost value and a second cost value is selected for the first pixel based on the determined frequency of occurrence of the foreground pixels. An object boundary is generated for a portion of the foreground object based on the selected first cost value and the second cost value for the first pixel.