Graph-Based Image Segmentation Using Texture and Intensity Cues
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Solution Overview
Problem
Existing image segmentation techniques, such as seed-based region growing, face challenges with highly textured images and noise, leading to over-inclusive or under-inclusive segmentations due to insufficient contrast and noise interference, making it difficult to accurately distinguish between regions.
Innovation Solution
A method that computes image features using Law masks, dyadic Gabor filter banks, wavelet transforms, or discrete cosine transforms, and represents the image data as a graph with edge weights established by a classifier trained on intensity and texture features, allowing for graph-based segmentation to accurately determine pixel probabilities and assign them to regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If seed-based region growing algorithm is used for image segmentation, then the segmentation process is simple and fast, but the segmentation accuracy deteriorates in highly textured images with noise
Solution Approach 1:
The patent segments the image processing task into multiple stages: (1) computing intensity and texture features separately, (2) combining these features into a composite representation, and (3) performing segmentation based on the combined features. This multi-stage segmentation approach allows the system to leverage both intensity and texture information, improving accuracy in textured images while maintaining computational efficiency through the modular structure.
Solution Approach 2:
The patent creates a composite feature representation by combining intensity features and texture features into a unified framework. This composite approach mirrors the use of composite materials in engineering, where combining different materials with complementary properties creates a superior overall performance. Here, intensity and texture features complement each other to overcome the limitations of using either feature type alone in textured, noisy images.
2Device complexity
If traditional region growing uses only intensity threshold, then the algorithm is computationally efficient, but it cannot distinguish regions with similar intensity but different textures
Solution Approach 1:
The patent merges intensity-based segmentation and texture-based segmentation into a unified framework. By combining these two complementary approaches, the system achieves more accurate region distinction than either method could achieve alone, while the integrated formulation maintains computational tractability through efficient feature combination strategies.
Solution Approach 2:
The patent extends the segmentation approach from a single dimension (intensity only) to multiple dimensions by incorporating texture features as an additional dimension of analysis. This multi-dimensional feature space allows the algorithm to distinguish regions that would be indistinguishable in intensity alone, effectively adding a new dimension of discrimination capability.
3Ease of manufacture
If region growing algorithm is applied to highly textured images, then texture information is ignored, but including texture features increases computational complexity
Solution Approach 1:
The patent performs preliminary computation of texture features and intensity features before the segmentation step. By pre-computing these features and organizing them in an efficient data structure, the system reduces the computational burden during the actual segmentation process, allowing texture information to be incorporated without proportionally increasing overall computational complexity.
Data Source
AI summary
A method for processing image data for segmentation includes receiving image data. One or more seed points are identified within the image data. Intensity and texture features are computer based on the received image data and the seed points. The image data is represented as a graph wherein each pixel of the image data is represented as a node and edges connect nodes representative of proximate pixels of the image data and establishing edge weights for the edges of the graph using a classifier that takes as input, one or more of the computed image features. Graph-based segmentation such as segmentation using the random walker approach may then be performed based on the graph representing the image data.