Facial Skin Color Segmentation Using Otsu Thresholding Algorithm

Rismayana, Aris Haris; Alfianti, Henny; Ramdan, Dadan Saepul

doi:10.33633/jais.v7i1.5513

Cited by 3 publications

(2 citation statements)

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“…In this study, measurements were carried out using Peak Signal-to-Ratio (PSNR) and Root Mean Square Error (RMSE) [Vicko Bhayyu]. Meanwhile, research [21], [22] used the Otsu Thresholding method to separate background from objects in digital images.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the suitability of the otsu method thresholding and multilevel thresholding for flower image segmentation

Hadiq,

Solehatin,

Djuniharto

et al. 2023

J. Soft Comput. Explor.

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The digital representation of flowers, characterized by their vivid chromatic attributes, establishes them as viable candidates for deployment as input imagery within the object recognition paradigm. Within the context of object recognition, the imperative of a proficient image segmentation process is underscored, serving to effectively discern the object from its background and, consequently, optimizing the efficacy of the object recognition process. This research unfolds through a methodologically structured tripartite framework, encompassing the initial stage involving input imagery, the subsequent intermediate phase dedicated to image segmentation, and a conclusive stage centered on the quantitative evaluation of methodological outcomes. The second stage, focusing on image segmentation, employs the Otsu thresholding and multilevel thresholding methods. The subsequent third stage involves a thorough assessment of segmentation outcomes through the application of quantitative metrics, including Peak signal-to-oise ratio (PSNR) and Root Mean Square Error (RMSE). Empirical investigations, incorporating a diverse array of floral input images, reveal a conspicuous inclination towards a specific segmentation methodology. Specifically, the Otsu Thresholding method emerges as the more judicious choice relative to multilevel Thresholding, demonstrating superior performance with a diminished RMSE value and an augmented PSNR value, substantiated by an average RMSE value. This research is propelled by the overarching objective of discerning the most optimal method for the segmentation of flower images, particularly in the face of diverse input images. Its significant contribution lies in providing nuanced insights into the discerning selection of segmentation methodologies, attuned to the variability inherent in diverse forms of input imagery, thereby culminating in optimized outcomes within the domain of flower image recognition. Where did these results come from? please show it in the sub-discussion.

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Section: Introductionmentioning

confidence: 99%

Comparison of the suitability of the otsu method thresholding and multilevel thresholding for flower image segmentation

Hadiq,

Solehatin,

Djuniharto

et al. 2023

J. Soft Comput. Explor.

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“…Dengan menggunakan teknik thresholding pada metode Otsu, nilai threshold dapat diperoleh secara otomatis berdasarkan citra yang dimasukkan sehingga proses segmentasi juga akan lebih cepat. Metode Otsu terbukti cukup handal dan dapat dimanfaatkan dalam berbagai obyek seperti segmentasi melanoma pada kasus kanker kulit (Pitoy and Suputra, 2021), pengenalan warna kulit wajah (Rismayana, Alfianti and Ramdan, 2022), penginderaan jauh (Srinivas, Prasad and Sirisha, 2019), hingga pengenalan makanan tradisional (Harnis, Sari and Rahman, 2019). Selanjutnya, proses ekstraksi fitur digunakan untuk mengambil ciri atau karakteristik dari masing-masing jenis keris menggunakan Hu Moment Invariant.…”

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Implementasi Metode Otsu dan Momen Hu pada Citra Keris

Sebatubun,

Haryawan

2024

JIP

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UNESCO telah menetapkan keris Indonesia sebagai Masterpiece of The Oral and Intangible Heritage of Humanity. Keris memiliki bilah yang terdiri dari pamor, dhapur, dan Tangguh yang merupakan istilah yang digunakan untuk menyebut nama bentuk dari bilah keris. Dhapur keris ada yang berbentuk lurus dan lengkok (Luk dalam bahasa jawa). Yang berbentuk luk, jumlahnya bermacam-macam, mulai dari luk 3 (tiga) sampai luk 29 (dua puluh Sembilan). Jenis keris berdasarkan dapur yang diakui secara baku sekitar 150 jenis. Namun, bentuk dhapur keris tidak mudah dikenali secara langsung. Selain karena jenisnya yang banyak, bentuk dhapur ini terkadang memiliki karakteristik yang mirip meskipun jenisnya berbeda. Hal ini menyebabkan keris sulit untuk dikenali. Penelitian ini akan melakukan klasifikasi jenis keris berdasarkan dhapur yaitu Parung Sari dan Tilam Sari menggunakan teknik pengolahan citra. Tahap awal yaitu proses cropping citra, kemudian segmentasi menggunakan metode Otsu dan proses morfologi. Setelah itu, proses ekstraksi fitur dilakukan dengan tujuh momen Hu. Hasil ekstraksi fitur ini menjadi masukkan pada algoritma klasifikasi MultiLayer Perceptron (MLP) dengan k-fold cross-validation. Akurasi yang diperoleh yaitu 95% dengan nilai precision 100% dan recall 90%. Dapat disimpulkan bahwa metode yang digunakan mampu membedakan kedua jenis keris dengan baik.

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