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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Stereo Imaging Using Hardwired Self-Organizing Object Segmentation.

Ching-Han Chen¹, Guan-Wei Lan¹, Ching-Yi Chen²

¹Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan.

Sensors (Basel, Switzerland)

|October 20, 2020

Summary

This summary is machine-generated.

This study presents a real-time stereo imaging system using a self-organizing map (SOM) neural network for efficient color segmentation. The system achieves 13.8 FPS on embedded systems, enabling depth map generation for stereo vision applications.

Keywords:

SOM object segmentation stereo vision

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Area of Science:

Computer Vision
Artificial Intelligence
Embedded Systems

Background:

Stereo vision systems rely on depth map generation through disparity calculation from two images.
Object segmentation and stereo matching are crucial technologies for effective stereo vision.

Purpose of the Study:

To implement a highly efficient self-organizing map (SOM) neural network hardware accelerator for unsupervised color segmentation in real-time stereo imaging.
To establish a pipelined, hierarchical stereo imaging system on a resource-constrained embedded platform.

Main Methods:

A self-organizing map (SOM) neural network hardware accelerator was employed for unsupervised color segmentation.
A pipelined, hierarchical architecture was designed, integrating SOM, connected component labeling, and sum-of-absolute-difference stereo matching modules.
Experiments were conducted on an embedded system with limited hardware resources.

Main Results:

The stereo imaging system achieved a performance of 13.8 frames per second.
The system processed color images at a resolution of 640 × 480.

Conclusions:

The developed SOM-based stereo imaging system demonstrates efficient real-time performance on embedded hardware.
This approach offers a viable solution for depth map generation in resource-constrained stereo vision applications.