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3 January 2020 Automated pixel correspondence searching method using CNN and Z-NCC for interactive pixel depth calculation
Changmin Kim, Minhyung Lee, Changyoon Yang, Junyoung Park, Sanghoon Kwon, Hyangin Jang
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Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 1137323 (2020) https://doi.org/10.1117/12.2557460
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China
Abstract
In terms of methods for measuring the distance between two points in space, field measurements have limitations due to time-space constraints. Moreover, methods using 3D scanners require high costs and long working hours. In order to improve such limitations, this study proposes an algorithm of automated pixel correspondence searching method using CNN (Convolutional Neural Network) and Z-NCC (Zero-Mean Normalized Cross-Correlation). The proposed algorithm applies a method where the user selects one pixel from a photo among multiple photos from the field after aligning the photos by position using SfM (Structure from Motion) and then automatically calculates the corresponding pixel from another photo using CNN and Z-NCC to triangulate the depth of the selected pixel.
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Changmin Kim, Minhyung Lee, Changyoon Yang, Junyoung Park, Sanghoon Kwon, and Hyangin Jang "Automated pixel correspondence searching method using CNN and Z-NCC for interactive pixel depth calculation", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137323 (3 January 2020); https://doi.org/10.1117/12.2557460
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KEYWORDS
Distance measurement
3D metrology
Cameras
3D modeling
3D scanning
Measurement devices
Convolutional neural networks
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