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Updated: Apr 8, 2026

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Unsupervised Texture Flow Estimation Using Appearance-Space Clustering and Correspondence.

Sunghwan Choi, Dongbo Min, Bumsub Ham

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 26, 2015

Summary

This summary is machine-generated.

This study introduces an automatic texture flow estimation method. It accurately estimates texture properties like scale and orientation, even in complex images, outperforming existing techniques.

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Area of Science:

Computer Vision
Image Processing
Computational Geometry

Background:

Estimating texture flow (scale, orientation, label) often requires user interaction.
Existing methods are limited by strict geometric model assumptions, restricting them to regular textures.
Handling images with multiple, diverse texture categories poses a significant challenge.

Purpose of the Study:

To develop a fully automatic texture flow estimation method.
To overcome limitations of user-dependent and geometrically constrained approaches.
To enable accurate flow estimation in images with complex and varied textures.

Main Methods:

Unsupervised extraction of distinct texture exemplars using medoid-based clustering in appearance space.
Efficient correspondence search in a deformation parameter space using a randomized strategy.
Refinement of the deformation flow field guided by matching confidence scores.

Main Results:

The proposed method achieves fully automatic texture flow estimation.
It successfully handles images containing multiple texture categories.
Experimental results demonstrate superior performance compared to existing methods on synthetic and natural images.

Conclusions:

Local visual similarity in appearance space effectively explains local flow behaviors.
Randomized search strategies combined with appropriate matching criteria enable efficient flow field estimation.
The developed method offers a robust and efficient solution for complex texture flow analysis.