Homography transform enhance CNN prediction accuracy on image classification

Research Article
Open access

Homography transform enhance CNN prediction accuracy on image classification

Chang Li 1*
  • 1 University of Illinois Urbana champaign    
  • *corresponding author changli8@illinois.edu
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/15/20230835
ACE Vol.15
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-021-9​
ISBN (Online): 978-1-83558-022-6

Abstract

Convolutional Neural Network (CNN) image classification is a well-established algorithm that has been implemented in many fields. Benefit from the digitalization process and the exponential increase in the base of smart devices, this algorithm can be applied to even more traditional or casual contexts in the future driven by the trend of Internet of Things. Thus, the needs for optimizing image classification in specific domains may have turned out to be ongoing valuable research direction. This paper focuses on providing optimization under one example context which is using traffic signs as the experimental target. For the randomly selected traffic sign samples in the experiment, the accuracy obtained from the samples treated by the homography transform compared to control group passed all three statistical tests: Two Sample t-test, McNemar's test, and Fisher's exact test. Therefore, research direction has achieved small-scale validation and presents optimism for large-sample experiments and further research in optimization using the introduced strategy in the paper.

Keywords:

CNN prediction, homography, image classification, autopilot

Li,C. (2023). Homography transform enhance CNN prediction accuracy on image classification. Applied and Computational Engineering,15,209-218.
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References

[1]. Tanzeel U. Rehmana, Md. Sultan Mahmudb, Young K. Changb, Jian Jina, Jaemyung Shinb. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems, 2018, Computers and Electronics in Agriculture, 156:585-605.

[2]. Wang, Y., Yu, M., Jiang, G., Pan, Z., & Lin, J. Image Registration Algorithm Based on Convolutional Neural Network and Local Homography Transformation. 2020 Applied Sciences, 10(3):732.

[3]. Hartley, R., & Zisserman, A. Multiple View Geometry in Computer Vision. 2011 Cambridge Core. https://doi.org/10.1017/CBO9780511811685

[4]. Liu, Y. Analysis of Key Technical Problems in Internet of Vehicles and Autopilot. 2020, Advances in Intelligent Systems and Computing, 1-10.

[5]. Dingle Robertson, L., King, D.J. Comparison of pixel-and object-based classification in land cover change mapping.2011 Int. J. Remote Sens. 32 (6), 1505–1529.

[6]. Zhang, S., Wu, X., You, Z., Zhang, L., Leaf image-based cucumber disease recognition using sparse representation classification. 2017, Comput. Electron. Agric. 134, 135–141.

[7]. Wang, S., Guo, Z., & Liu, Y. An Image Matching Method Based on SIFT Feature Extraction and FLANN Search Algorithm Improvement. 2021 Journal of Physics: Conference Series. 201-213.

[8]. Lin, C. C., & Wang, M. S. A Vision Based Top-View Transformation Model for a Vehicle Parking Assistant. 2012, Sensors, 12(4):4431-4446.

[9]. The Car and the Camera. (n.d.). Google Books. https://books.google.com/books/about/The_Car_and_the_Camera.html?hl

[10]. Jaemyung Shinb. Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space. (n.d.) 2021 Advances in Intelligent Systems and Computing, 122-134.

Cite this article

Li,C. (2023). Homography transform enhance CNN prediction accuracy on image classification. Applied and Computational Engineering,15,209-218.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-021-9​(Print) / 978-1-83558-022-6(Online)
Editor：Marwan Omar, Roman Bauer, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.15
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Tanzeel U. Rehmana, Md. Sultan Mahmudb, Young K. Changb, Jian Jina, Jaemyung Shinb. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems, 2018, Computers and Electronics in Agriculture, 156:585-605.

[2]. Wang, Y., Yu, M., Jiang, G., Pan, Z., & Lin, J. Image Registration Algorithm Based on Convolutional Neural Network and Local Homography Transformation. 2020 Applied Sciences, 10(3):732.

[3]. Hartley, R., & Zisserman, A. Multiple View Geometry in Computer Vision. 2011 Cambridge Core. https://doi.org/10.1017/CBO9780511811685

[4]. Liu, Y. Analysis of Key Technical Problems in Internet of Vehicles and Autopilot. 2020, Advances in Intelligent Systems and Computing, 1-10.

[5]. Dingle Robertson, L., King, D.J. Comparison of pixel-and object-based classification in land cover change mapping.2011 Int. J. Remote Sens. 32 (6), 1505–1529.

[6]. Zhang, S., Wu, X., You, Z., Zhang, L., Leaf image-based cucumber disease recognition using sparse representation classification. 2017, Comput. Electron. Agric. 134, 135–141.

[7]. Wang, S., Guo, Z., & Liu, Y. An Image Matching Method Based on SIFT Feature Extraction and FLANN Search Algorithm Improvement. 2021 Journal of Physics: Conference Series. 201-213.

[8]. Lin, C. C., & Wang, M. S. A Vision Based Top-View Transformation Model for a Vehicle Parking Assistant. 2012, Sensors, 12(4):4431-4446.

[9]. The Car and the Camera. (n.d.). Google Books. https://books.google.com/books/about/The_Car_and_the_Camera.html?hl

[10]. Jaemyung Shinb. Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space. (n.d.) 2021 Advances in Intelligent Systems and Computing, 122-134.

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