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Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

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Improving Census Transform by High-Pass with Haar Wavelet Transform and Edge Detection.

Jiun-Jian Liaw¹, Chuan-Pin Lu², Yung-Fa Huang¹

¹Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan.

Sensors (Basel, Switzerland)

|May 6, 2020

Summary

This summary is machine-generated.

This study enhances stereo vision disparity calculation using improved census transform methods. Wavelet transforms and adaptive windowing reduce computation and boost accuracy for distance measurement applications.

Keywords:

census transform disparity sparse census transform stereo vision

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Area of Science:

Computer Vision
Image Processing
Robotics

Background:

Stereo vision is a common technique for 3D distance measurement using two cameras.
The census transform is a robust method for calculating disparity but suffers from window selection issues.
Existing methods require significant computational resources and can lack accuracy.

Purpose of the Study:

To improve the performance of the census transform for disparity calculation in stereo vision.
To reduce computational load and enhance accuracy in stereo vision systems.
To introduce novel methods for adaptive window selection in disparity estimation.

Main Methods:

Utilizing wavelet transform's low-pass and high-pass bands to reduce computation and refine disparity.
Implementing adaptive window size selection based on edge information for census transform.
Applying adaptive window size to sparse census transform for enhanced efficiency.

Main Results:

Wavelet transform multiresolution features significantly reduce computational requirements.
Modified disparity processing improves accuracy, as measured by Percentage of Bad Matching Pixels (PoBMP) and Root Mean Squared (RMS).
Adaptive window size methods decrease computational complexity and the number of operation points.

Conclusions:

The proposed wavelet-based and adaptive windowing techniques offer substantial improvements in stereo vision disparity calculation.
These methods provide a more efficient and accurate approach to 3D distance measurement.
The findings contribute to advancements in computer vision and its applications.