Tinjauan Literatur Sistematik tentang Structural Similarity Index Measure untuk Deteksi Anomali Gambar
Abstract
Image enhancement merupakan prosedur yang digunakan untuk memproses gambar sehingga dapat memperbaiki atau meningkatkan kualitas gambar agar selanjutnya dapat dianalis untuk tujuan tertentu. Ada banyak algoritma image enhancement yang dapat diterapkan pada suatu gambar, salah satunya dapat menggunakan algoritma structural similarity index measure (SSIM), algoritma ini berfungsi sebagai alat ukur dalam menilai kualitas gambar, bekerja dengan membandingkan fitur structural dari gambar, dan kualitas gambar dijelaskan oleh kesamaan structural. Selain untuk menilai kualitas suatu gambar, SSIM dapat menjadi metode dalam menganalisis perbedaan gambar, sehingga diketahui anomali dari perbandingan dua gambar berdasarkan data structural dari sebuah gambar. Tinjauan literature sistematis ini digunakan untuk menganalisis dan fokus pada algoritma SSIM dalam mengetahui anomaly 2 gambar yang terlihat mirip secara human visual system. Hasil sistematis review menunjukkan bahwa penggunaan algoritma SSIM dalam menilai kualitas gambar berkorelasi kuat dengan HVS (Human Vision System) dan dalam deteksi anomaly gambar menghasilkan akurasi yang berbeda, karena terpengaruh intensitas cahaya dan posisi kamera dalam mengambil gambar sebagai dataset.
Kata Kunci— SSIM, anomaly, gambar, deteksi
Image enhancement is a procedure used to process images so that they can correct or improve image quality so that they can then be analyzed for specific purposes. Many image enhancement algorithms can be applied to an image. one of the usable methods is the structural similarity index measure (SSIM) algorithm, this algorithm serves as a measuring tool in assessing image quality. It works by comparing the structural features of images, and the image quality is explained by structural similarity. In addition to assessing the quality of an image, SSIM can be a method of analyzing image differences. So, the anomalies are known from the comparison of two images based on the structural data from an image. This systematic literature review is used to analyze and focus on the SSIM algorithm in knowing anomaly 2 images that look similar to the human visual system. Systematic review results show that the use of the SSIM algorithm in assessing image quality is strongly correlated with HVS (Human Vision System). In anomaly detection of images produces different accuracy because it is affected by light intensity and camera position in taking pictures as a dataset.
Keywords— SSIM, anomaly, gambar, deteksi
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Easton, R. L., 2010, Fundamentals of Digital Image Processing, Wiley and Son, New Jersey.
da Silva, E. A. B, Mendonca GV., 2005, Digital Image Processing, Academic Press, USA.
Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E.P., 2004, Image quality assessment: from error visibility to structural similarity, IEEE Transactions on Image Processing, No. 4, Vol. 13, Hal. 600–612.
Liu, H., Tan, T., Kuo, T., 2020, A novel shot detection approach based on ORB fused with structural similarity, IEEE Access, Vol. 8, Hal. 2472-2481.
Vinay, A., Singh, A., Anand, N., Raj, M., Bharati, A., Murthy, K. N. B., 2018, Surveillance Robots based on Pose Invariant Face Recognition SSIM and Spectral Clustering, Procedia Computer Science, Hal. 940–51.
Ponomarenko, M., Egiazarian, K., 2018, Structural Similarity Index with Predictability of Image Blocks, 2018 IEEE 17th International Conference Mathematical Methods Electromagnetic Theory, Hal. 115–118.
Dhall. A., Asthana,A., Goecke. R., 2011, A SSIM-Based Approach for Finding Similar Facial Expressions, 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop, Santa Barbara, 21 - 25 Maret
Peng, J., Shi, C., Leugeman, E., Hu, W., Zhang, Z., Mutic, S., Cai, B., 2020, Implementation of the Structural SIMilarity (SSIM) Index as a quantitative evaluation tool for dose distribution error detection, Medical Physics, No. 4, Vol. 47.
Mauludy, A. T., Khrisne, D. C., Saputra K. O., 2020, Rancang bangun aplikasi slot parkir kosong untuk kendaraan roda empat dengan pendekatan computer vision, Jurnal SPEKTRUM, No. 1, Vol. 7, Hal. 36–40.
Jahan, M., Rushu, F. R., Tabassum, S., Ferdous, J., 2018, Detecting anomalies in human eyes using structural similarity index measurement. School of Engineering and Computer Science. 2018.
Zhou M, Wang G, Wang J, Hui C, Yang W., 2017, Defect Detection of Printing Images on Cans Based on SSIM and Chromatism, 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu - China, 13 - 16 Desember.
Renieblas, G. P., González, A. M., Guibelalde, E., Renieblas, G. P., Nogués, A. T., González, A. M., et al., 2017, Structural similarity index family for image quality assessment in radiological images assessment in radiological images, Journal of Medical Imaging, No. 3, Vol. 4, Hal. 1-11.
Wulandari, M., 2017, Pengukuran SSIM dan analisis kinerja metode interpolasi untuk peningkatan kualitas citra digital, Jurnal Muara Sains Teknologi, Kedokteran, dan Ilmu Kesehatan, No. 1, Vol. 1, Hal. 184–195.
Fastowicz, J., Okarma. K., 2018, Fast quality assessment of 3D printed surfaces based on structural similarity of image regions, 2018 International Interdisciplinary PhD Workshop (IIPhDW), Swinoujście, 9 - 12 Mei.
Chen, G., Shen. Y., Yao, F., Liu, P., Liu Y., 2015, Region-based Moving Object Detection Using SSIM, 2015 4th International Conference on Computer Science and Network Technology (ICCSNT 2015), Harbin, 19 - 20 Desember.
Win, H. L., Soe, Y. Y., Lwin D. Y. Y., 2018, Results Analysis of Real-Time Edge Detection Techniques using LabView, International Journal of Science and Engineering Applications, No. 8, Vol. 7, Hal. 203–7.
Al-ghaib, H., 2016, Morphological Procedure for Mammogram Enhancement and Registration, 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington DC, 18 - 20 Oktober.
DOI: https://doi.org/10.24076/citec.2020v7i2.248
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