Development of DFTF algorithm for real-time processing
Article Sidebar
Main Article Content
Abstract
This paper presents an advancement in the acceleration of object rotation direction detection through the implementation of a feature accumulation system derived from the Discriminant Feature Trace Transform (DFTF). DFTF data will be utilized in conjunction with machine learning to forecast the rotational direction of an object via the DFTF algorithm. It was discovered that in relation to the time required to obtain DFTF results, the procedure is intricate and involves multiple processing steps. As a result, the objective of the development guidelines that are outlined in this article is to accelerate the quest for solutions and streamline the work process. Adjusting the dimensions of the image data is the initial step in reducing the quantity of information that must be computed. Following this, the procedure for producing data in the trace transform domain was eradicated and substituted with machine learning in order to convert the image data to DFTF format. It is then incorporated into the standard procedure for calculating the rotation of an object. The outcomes of experiments conducted in this paper utilizing on Caltech-256 database. The suggested method allows for the measurement of data error using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), with data errors of 4.09 and 2.2, respectively. The optimal running speed per image in this experiment is 0.12 seconds, yielding an average accuracy of 98.7%.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright @2021 Engineering Transactions
Faculty of Engineering and Technology
Mahanakorn University of Technology
References
Nattapong Jundang and Suchada Sitjongsataporn, “Discriminant Feature Trace Transform for Predictive Object Rotation”, International Journal of Intelligent Engineering and Systems, vol. 16, No. 1, pp. 419-434, 2023.
X. Zhang and J. Saniie, "Material Texture Recognition using Ultrasonic Images with Transformer Neural Networks," 2021 IEEE International Conference on Electro Information Technology (EIT), Mt. Pleasant, MI, USA, pp. 1-5 2021.
A. Kumari Sirivarshitha, K. Sravani, K. S. Priya and V. Bhavani, "An approach for Face Detection and Face Recognition using OpenCV and Face Recognition Libraries in Python," 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, pp. 1274-1278, 2023.
N. Jundang, "Development of Trace Transform using Machine Learning for reducing the tracing line calculation," 2022 International Electrical Engineering Congress (iEECON), Khon Kaen, Thailand, pp. 1-4, 2022.
Y. Huang and W. Liu, "Regression Analysis Model Based on Data Processing and MATLAB Numerical Simulation," 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Dalian, China, pp. 1115-1118, 2022.
H. A. Sayyed, S. Rushikesh Sugave, S. Paygude and B. N Jazdale, "Study and Analysis of Emotion Classification on Textual Data," 2021 6th International Conference on Communication and Electronics Systems (ICCES), Coimbatre, India, , pp. 1128-1132, 2021.
X. Chang, "Soft Measurement Modeling of Turbidity in Flocculation Process of Drinking Water Treatment Using Gaussian Process Regression," 2021 40th Chinese Control Conference (CCC), Shanghai, China, pp. 6196-6200, 2021.
L. Melki, M. Tourni and E. E. Konofagou, "Electromechanical Wave Imaging With Machine Learning for Automated Isochrone Generation," in IEEE Transactions on Medical Imaging, vol. 40, no. 9, pp. 2258-2271, Sept. 2021.
B. M. Rashed and N. Popescu, "Machine Learning Techniques for Medical Image Processing," 2021 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania, pp. 1-4, 2021.
M. Sabouri et al., "Cardiac Pattern Recognition from SPECT Images Using Machine Learning Algorithms," 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Piscataway, NJ, USA, pp. 1-3, 2021.
N. Jan, A. Makhdoomi, P. Handa and N. Goel, "Machine learning approaches in medical image analysis of PCOS," 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS), Bhubaneswar, India, pp. 48-52, 2022.
A. Sivasangari, Sivakumar, S. Helen, S. Deepa, Vignesh and Suja, "Detection of Abnormalities in Brain using Machine Learning in Medical Image Analysis," 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, pp. 102-107, 2022.
A. Makhdoomi, N. Jan, P. Palak and N. Goel, "Machine learning techniques for medical images in PCOS," 2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST), Delhi, India, pp. 1-6, 2022.
G. Griffin, A. Holub, and P. Perona, "Caltech256 Object Category Dataset", Technical Report 7694, 2007.
Nattapong Jundang and Suchada Sitjongsataporn, “Coordinated-axis Weight for Real-Time Trace Transform”, in Proc. IEEE International Electrical Engineering Congress (iEECON), Krabi, Thailand, pp. 184-187, 2023.