An improved<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si9.gif" display="inline" overflow="scroll"><mml:mi>K</mml:mi></mml:math>-means clustering algorithm for fish image segmentation

Yao, Hong Tao; Duan, Qingling; Li, Bingbing; Wang, Jianping

doi:10.1016/j.mcm.2012.12.025

Cited by 130 publications

(63 citation statements)

References 8 publications

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“…Pada penelitian [3] melakukan deteksi kesegaran ikan bandeng dengan mempertimbangkan jarak euclidean pada ruang warna RGB (red green blue). Pada peneltian [4] dan [5] melakukan deteksi kesegaran pada ikan rohu berdasarkan jaringan insangnya, sedangkan untuk permasalahan pada segmentasi dengan ikan terdapat pada penelitian [6]. Penelitian [6] melakukan segmentasi citra ikan dengan menggunakan k-means dimana parameter jumlah kluster dan kluster pusatnya ditentukan berdasarkan jumlah titik puncak histogram.…”

Section: Pendahuluanunclassified

“…Operasi standar yang digunakan adalah erosion, dilation, opening dan closing. Berikut adalah formula operasi tersebut [6]. …”

Section: Morfologi Matematikaunclassified

“…Dari hasil segmentasi tersebut kemudian dilakukan proses refinement, yaitu perbaikan pada garis tepi citra. Proses refinement dilakukan dengan persamaan (7) dan (8) untuk membuang pixel yang tidak terputus dan menutup lubang kecil dari objek, kemudian dilakukan perbaikan tepi citra dengan menggunakan persamaan (5) dan (6). Struktur elemen yang digunakan pada penelitian ini adalah matrik disk dengan lebar tetangganya bernilai 2.…”

Section: Hasil Penelitianunclassified

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Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna

Saputra¹,

Arifin²

2017

INFOTEL

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Abstrak -Citra ikan tuna harus dilakukan proses segmentasi yang baik sebelum masuk ke tahapan klasifikasi. Hasil segmentasi yang baik adalah objek dan background terpisah dengan jelas. Citra ikan tuna yang memiliki sebaran cahaya tidak merata dan memiliki tekstur yang komplek akan menghasilkan kesalahan segmentasi. Salah satu metode segmentasi pada citra adalah seeded region growing dan parameter yang digunakan hanya dua yaitu seed dan threshold. Penelitian ini mengusulkan metode seeded region growing pada ruang warna HSI untuk segmentasi citra ikan tuna. Ruang warna RGB (red green blue) pada citra ikan tuna ditransformasikan ke dalam ruang warna HSI (hue saturasi intesitas) yang kemudian hanya ruang hue untuk dijadikan segmentasi dengan menggunakan seeded region growing. Penentuan parameter seed dan threshold dilakukan secara manual dan hasil dari segmentasi tersebut dilakukan refinement dengan morfologi matematika. Pengujian dilakukan sebanyak 30 citra dan metode evaluasi hasil segmentasi menggunakan RAE (relative foreground area error), MAE (missclassification error) dan MHD (modified Hausdroff distance). Citra ikan tuna berhasil dilakukan segmentasi dengan dibuktikan nilai RAE, ME dan MHD secara berturut adalah 5,40%, 1,53% dan 0,41%.Kata kunci -HSI, citra ikan tuna, morfologi matematika, seeded region growing Abstract -The image of the tuna must have a good segmentation results before entering process classification. The result of good segmentation is object and background separate clearly. The image of tuna which has a distribution of light that is uneven and has a complex texture will produce an error segmentation. One method of image segmentation was seeded region growing and parameters that used only two, namely seed and threshold. This research proposed method seeded region growing in the HSI color space for image segmentation of tuna. The Color space of RGB (red green blue) on image of tuna transformed into a color space HSI (hue saturation intensity) then only the hue color space used as segmentation by using seeded region growing. Determination of seed and threshold parameters can do manually and the result of the segmentation do refinement with mathematical morphology. The experiment using 30 image of tuna to segmentation and evaluation methods using RAE (relative foreground area error), MAE (missclassification error) and the MHD (modified Hausdroff distance). The image of the tuna successfully performed segmentation evidenced by a value RAE, ME and MHD respectively are 5,40%, 1,53% dan 0,41%.

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Section: Pendahuluanunclassified

“…Operasi standar yang digunakan adalah erosion, dilation, opening dan closing. Berikut adalah formula operasi tersebut [6]. …”

Section: Morfologi Matematikaunclassified

Section: Hasil Penelitianunclassified

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Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna

Saputra¹,

Arifin²

2017

INFOTEL

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“…Metode segmentasi citra otomatis dapat dikategorikan ke dalam beberapa kelompok, yaitu berbasis histogram, berbasis tepi, berbasis wilayah (cluster), dan teknik hybrid [7]. Namun, proses optimasi perlu dilakukan untuk mendapatkan parameter optimal yang sangat berpengaruh pada akurasi hasil segmentasi [8].…”

Section: Pendahuluanunclassified

Lokal Fuzzy Thresholding Berdasarkan Pengukuran Fuzzy Similarity Pada Interaktif Segmentasi Citra Panoramik Gigi

Gunawan

Arifin

2017

INFOTEL

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Abstrak -Dalam segmentasi citra, thresholding merupakan salah metode yang mudah dan sederhana untuk diimplementasikan. Pada citra panoramik gigi, penentuan global threshold masih kurang begitu optimal untuk diimplementasikan, hal tersebut dikarenakan adanya faktor penghambat seperti pencahayaan yang tidak merata dan citra yang kabur. Faktor-faktor tersebut dapat menyebabkan histogram tidak bisa dipartisi dengan baik, sehingga akan berpengaruh pada hasil segmentasi. Pada penelitian ini diusulkan local fuzzy thresholding berdasarkan pengukuran fuzzy similarity pada interaktif segmentasi citra panoramik gigi. Metode yang diusulkan terdiri dari tiga tahapan utama, tahap pertama adalah region splitting untuk mendapatkan lokal region. Tahap kedua adalah user marking untuk mendapat initial seed background dan objek, tahap terakhir adalah pengukuran fuzzy similarity pada setiap lokal region untuk mendapatkan nilai lokal threshold. Hasil uji coba pada citra panoramik gigi, metode yang diusulkan berhasil melakukan segmentasi dengan rata-rata missclasification error (ME) 5.47%.Kata kunci -interaktif segmentasi citra, citra panoramik gigi, lokal thresholding, pengukuran fuzzy similarity Abstract -In image segmentation, thresholding is one of the method that is easy and simple to be implemented. In the dental panoramic image, determination of global threshold is still less optimal to be implemented. This is due to inhibiting factors such as uneven contrast and blurred images. These factors can lead to the problem in partitioning histogram, that would-be effect in the segmentation result. In this study, we proposed local fuzzy thresholding based on fuzzy similarity measurement in interactive image segmentation for dental panoramic image. The proposed method consists of three main stages. The first stage is region splitting. This stage is performed to get the local regions. The second stage is the user marking that used to receive initial seed background and object. The last stage is a fuzzy similarity measurement at each local region to get the local threshold value. The results on dental panoramic image show that the proposed method successfully segmenting the images with an average misclassification error (ME) 5.47%.

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“…3c, and it contains noise, such as holes and spurs, which would affect the accuTarget segmentation by the K-means clustering algorithm. The K-means clustering algorithm is a partitioning algorithm of the feature space and has been widely used in image segmentation fields (Rekik et al, 2006;Yao et al, 2013). The K-means algorithm has many advantages, such as its simple description, high efficiency and suitability for large-scale data processing, which make it more suitable to segment images in this work.…”

Section: Apple Contour Extractionmentioning

confidence: 99%

An improved contour symmetry axes extraction algorithm and its application in the location of picking points of apples

Wang

Song

et al. 2015

Span. j. agric. res.

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The key problem for picking robots is to locate the picking points of fruit. A method based on the moment of inertia and symmetry of apples is proposed in this paper to locate the picking points of apples. Image pre-processing procedures, which are crucial to improving the accuracy of the location, were carried out to remove noise and smooth the edges of apples. The moment of inertia method has the disadvantage of high computational complexity, which should be solved, so convex hull was used to improve this problem. To verify the validity of this algorithm, a test was conducted using four types of apple images containing 107 apple targets. These images were single and unblocked apple images, single and blocked apple images, images containing adjacent apples, and apples in panoramas. The root mean square error values of these four types of apple images were 6.3, 15.0, 21.6 and 18.4, respectively, and the average location errors were 4.9°, 10.2°, 16.3° and 13.8°, respectively. Furthermore, the improved algorithm was effective in terms of average runtime, with 3.7 ms and 9.2 ms for single and unblocked and single and blocked apple images, respectively. For the other two types of apple images, the runtime was determined by the number of apples and blocked apples contained in the images. The results showed that the improved algorithm could extract symmetry axes and locate the picking points of apples more efficiently. In conclusion, the improved algorithm is feasible for extracting symmetry axes and locating the picking points of apples. Additional key words: picking robot; fruit picking point; symmetry axes extraction; moment of inertia; convex hull

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An improvedK-means clustering algorithm for fish image segmentation

Cited by 130 publications

References 8 publications

Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna

Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna

Lokal Fuzzy Thresholding Berdasarkan Pengukuran Fuzzy Similarity Pada Interaktif Segmentasi Citra Panoramik Gigi

An improved contour symmetry axes extraction algorithm and its application in the location of picking points of apples

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