AKUISI DATA CITRA PERMUKAAN REL MENGGUNAKAN PENGOLAHAN CITRA DIGITAL

Sunaryo Sunaryo, M.Afif Amalul Arifidin

Abstract


Human visual abilities are very different from those of computer vision systems. The concept of computer vision is to embed human intelligence into a computer. The most important part of the initial computer vision process is image processing. The image processing process applied in this research uses a histogram because the image used is a true color image. The result of image acquisition is changing from analog images to digital images in digital computerization. This research design has three processes, namely the data acquisition process, feature extraction from each true color image, and histograms. The results of this study are 100 images obtained low contrast because the histogram tends to collect in the middle. And in the distribution of blue color extraction histogram values, the intensity value obtained reaches 255 compared to the distribution of red and green color extraction histogram values

Keywords


Digital Image, Image Data Acquisition, Rail Surface

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References


B. Firmanto, E. Rikasanti, and ..., “Optimasi Hasil Akuisisi Obyek Wajah Menggunakan,” Semnas SENASTEK …, 2019.

A. I. Khan and S. Al-Habsi, “Machine Learning in Computer Vision,” in Procedia Computer Science, 2020. doi: 10.1016/j.procs.2020.03.355.

G. G. Maulana, N. Wisma Nugraha, and S. A. Garini, “Supervisory System Based on Image Processing Using Library Open Computer Vision In Tool Management System,” in ISMEE 2021 - 2021 3rd International Symposium on Material and Electrical Engineering Conference: Enhancing Research Quality in the Field of Materials and Electrical Engineering for a Better Life, 2021. doi: 10.1109/ISMEE54273.2021.9774199.

A. F. A. Fernandes, J. R. R. Dórea, and G. J. de M. Rosa, “Image Analysis and Computer Vision Applications in Animal Sciences: An Overview,” Frontiers in Veterinary Science. 2020. doi: 10.3389/fvets.2020.551269.

M. R. Rasyid, Z. Tahir, and Syafaruddin, “Pengolahan Citra Digital untuk Mendeteksi Kesalahan Kerja Mesin Industri dengan Metode Learning Vector Quantization,” J. Pekommas, 2019.

M. H. Guo et al., “Attention mechanisms in computer vision: A survey,” Computational Visual Media. 2022. doi: 10.1007/s41095-022-0271-y.

H. Rashid, N. Zafar, M. J. Iqbal, H. Dawood, and H. Dawood, “Single Image Dehazing using CNN,” in Procedia Computer Science, 2019. doi: 10.1016/j.procs.2019.01.201.

J. Dapello, T. Marques, M. Schrimpf, F. Geiger, D. D. Cox, and J. J. DiCarlo, “Simulating a primary visual cortex at the front of CNNs improves robustness to image perturbations,” in Advances in Neural Information Processing Systems, 2020.

G. W. Lindsay, “Convolutional neural networks as a model of the visual system: Past, present, and future,” J. Cogn. Neurosci., 2021, doi: 10.1162/jocn_a_01544.

S. Jaiswal, L. Asper, J. Long, A. Lee, K. Harrison, and B. Golebiowski, “Ocular and visual discomfort associated with smartphones, tablets and computers: what we do and do not know,” Clinical and Experimental Optometry. 2019. doi: 10.1111/cxo.12851.

R. Millón, E. Frati, and E. Rucci, “A Comparative Study between HLS and HDL on SoC for Image Processing Applications,” Elektron, 2020, doi: 10.37537/rev.elektron.4.2.117.2020.

C. Li, Y. Bi, F. Marzani, and F. Yang, “Fast FPGA prototyping for real-time image processing with very high-level synthesis,” J. Real-Time Image Process., 2019, doi: 10.1007/s11554-017-0688-1.

M. Orisa and T. Hidayat, “ANALISIS TEKNIK SEGMENTASI PADA PENGOLAHAN CITRA,” J. Mnemon., 2019, doi: 10.36040/mnemonic.v2i2.84.

A. F. Hastawan, R. Septiana, and Y. E. Windarto, “Perbaikan Hasil Segmentasi HSV Pada Citra Digital Menggunakan Metode Segmentasi RGB Grayscale,” Edu Komputika J., 2019, doi: 10.15294/edukomputika.v6i1.23025.

X. Chen, Q. An, K. Yu, and Y. Ban, “A Novel Fire Identification Algorithm Based on Improved Color Segmentation and Enhanced Feature Data,” IEEE Trans. Instrum. Meas., 2021, doi: 10.1109/TIM.2021.3075380.

F. Shi et al., “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19,” IEEE Reviews in Biomedical Engineering. 2021. doi: 10.1109/RBME.2020.2987975.

Y. Zhang, Z. Han, and Y. Tang, “Color image denoising based on low-rank tensor train,” Tenth Int. Conf. Graph. Image Process. (ICGIP 2018), vol. 11069 SPIE, 2019.

D. Abdalla, H. Ramdan, and R. Dungani, “Spectral Colour Characteristic’s (Red, Green, Blue) of Sick Acacia Mangium Stand,” in IOP Conference Series: Earth and Environmental Science, 2020. doi: 10.1088/1755-1315/528/1/012049.

B. Ramzan, M. S. Malik, M. Martarelli, H. T. Ali, M. Yusuf, and S. M. Ahmad, “Pixel frequency based railroad surface flaw detection using active infrared thermography for Structural Health Monitoring,” Case Stud. Therm. Eng., 2021, doi: 10.1016/j.csite.2021.101234.

I. M. S. preddy Marpaung, “Analisis Dan Perbandingan Metode Sobel Dan Canny Pada Deteksi Tepi Citra Daun Sirh Merah,” JIKOMSI [Jurnal Ilmu Komput. dan Sist. Informasi], 2021.




DOI: https://doi.org/10.30873/ji.v24i1.3968


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