Analisis Perbandingan Kinerja Metode Superpixel dan Gradien berbasis Edge Detector pada Pendeteksian Objek Bergerak

MUHAMMAD KHAERUL NAIM MURSALIM, IHSAN VERDIAN

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ABSTRAK

Salah satu bagian dalam algoritma pemrosesan citra adalah proses segmentasi yang menjadi tahap pra-pemrosesan untuk ekstraksi fitur objek. Superpixel menjadi salah satu solusi pada proses segmentasi dengan mendefenisikan kumpulan piksel yang mempunyai kesamaan karekterisitik sehingga membawa banyak informasi mengenai fitur objek. Adapun tantangan yang dihadapi dalam mendeteksi objek bergerak adalah ketidakmampuan untuk memisahkan objek bergerak dari background objek. Sehingga, pada citra yang dideteksi akan dikelilingi oleh wilayah yang terdapat derau. Pada penelitian ini, diusulkan metode superpixel berbasis deteksi tepi untuk mendeteksi objek bergerak. Selanjutnya, kinerja metode superpixel diuji dengan membandingkan dengan metode deteksi tepi yang berbasis gradient. Hasilnya menunjukkan bahwa metode yang diusulkan mampu meminimalisir derau lebih baik dan hasil perhitungan MSE, RMSE, dan PSNR hanya berbeda 0.06% dan 0.1% dari metode Sobel dan Prewitt.

Kata kunci: Deteksi tepi, Objek bergerak, Proses Segmentasi, Superpixel

 

ABSTRACT

One part of the image processing is the segmentation which becomes the preprocessing stage for feature extraction. Superpixel becomes solutions in the segmentation process by defining a collection of pixels that have the same characteristics ang bringing the information about the object's features. The challenge faced in detecting moving objects is the inability to separate moving objects from the object's background. Thus, the detected image will be surrounded by an area with noise. In this study, a superpixel-based edge detection method is proposed to detect moving objects. Furthermore, the performance of the superpixel method is tested by comparing it to the gradient-based edge detection method. The results show that the proposed method is able to minimize noise better and the results of MSE, RMSE, and PSNR calculations differ only 0.06% and 0.1% from the Sobel and Prewitt methods.

Keywords: Edge detection, Moving objects, Segmentation, Superpixels


Kata Kunci


Deteksi tepi; Objek bergerak; Proses Segmentasi; Superpixel

Teks Lengkap:

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Referensi


Chandwadkar, Dhole, Gadewar, Raut, & Tiwaskar. (2013). Comparison of edge detection techniques. Proceedings of Sixth IRAJ International Conference No. 5036.7123.

Heath, Mike, Sudeep, Sanocki, & Kevin. (1998). Comparison of edge detectors: a methodology and initial study. Computer vision and image understanding. (pp. 38-54).

Mursalim. (2018). Pendeteksian dan Pelacakan Objek Bergerak pada UAV berbasis Metode SUED. Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 7(1), 105-111.

Radhakrishna and Sabine . (2017). Superpixels and polygons using simple non-iterative clustering. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4651– 4660). IEEE.

Radhakrishna, Appu, Kevin, Aurelien, Pascal, & Sabine. (2012). SLIC superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence, (pp. 2274–2282).

Saif, Anton, & Zainal. (2014). Moving object detection using dynamic motion modelling from UAV aerial images. The Scientific World Journal.

Voskuil, R. P. (1996). Bandoeng: Beeld van Een Stad (Indonesian ed.). (S. M. Supardan, S. Sumardi, N. Darsono, & I. I. Yousda, Penerj.) Bandung: Dept. Planologi and Jagaddhita.

Wu, C., Zhang, L., Zhang, H. & Yan, H. (2018). Superpixels using fuzzy simple linear iterative clustering and fast precise number control. arXiv.org (pp. 1812.10932).

Xiaofeng and Jitendra. (2003). Learning a classification model for segmentation. in Proceedings of the IEEE International Conference on Computer Vision (pp. 10).

Zhang, Wu, & Zheng. (2019). A novel centroid update approach for clustering-based superpixel method and superpixel-based edge detection. arXiv.org. Cornell University.

Zhengqin and Jiansheng. (2015). Superpixel segmentation using linear spectral clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern (pp. 1356–1363).




DOI: https://doi.org/10.26760/elkomika.v8i2.362

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ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

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