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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

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Published on: September 27, 2019

Analysis of multiple orientations.

Matthias Muhlich¹, Til Aach

¹Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany. matthias.muehlich@lfb.rwth-aachen.de

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

|May 19, 2009

Summary

This summary is machine-generated.

This study introduces a new mathematical model for estimating multiple orientations in image data, crucial for computer vision tasks like optical flow. The framework effectively handles complex scenarios with occlusions and transparencies.

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Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Area of Science:

Computer Vision
Image Processing
Multivariate Signal Analysis

Background:

Estimating local orientations in multivariate signals is vital for image processing and computer vision.
Existing methods often struggle with occlusions and transparencies, necessitating models for multiple orientations.

Purpose of the Study:

To develop a unifying mathematical model for estimating multiple orientations in image data.
To enable unified treatment of additively and occludingly superimposed oriented structures.

Main Methods:

Developed a novel mathematical framework for multiple orientation estimation.
Designed estimation schemes for superimposed orientations in scalar- and vector-valued image data.
Utilized both synthetic and real image datasets for validation.

Main Results:

The proposed model successfully describes an arbitrary number of orientations.
The framework allows unified treatment of additive and occlusive superposition of orientations.
Performance was validated on diverse image data, demonstrating robustness.

Conclusions:

The developed unifying model provides a robust solution for multiple orientation estimation in images.
This framework advances the analysis of complex image structures with occlusions and transparencies.
The proposed methods show strong performance on both synthetic and real-world image data.