Identification of orchid species using content-based flower image retrieval

Apriyanti, Diah Harnoni; Arymurthy, A.A.; Handoko, L. T.

doi:10.1109/ic3ina.2013.6819148

Cited by 22 publications

(6 citation statements)

References 10 publications

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“…In addition, differences in condensation levels, gene, and protein content can also affect chromosome size (Apriyanti et al, 2013).…”

Section: Chromosome Sizementioning

confidence: 99%

Orchids Cytogenetics and Karyotype of Phaius callosus and Phaius montanus

Hartati

2022

AFP

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The Phaius callosus and Phaius montanus orchids are ornamental orchids with high ornamental value and are classified as endangered plants. Genetic information from Phaius callosus and Phaius montanus orchids will facilitate plant breeding as it is essential to improve ornamental plant properties. Chromosome analysis aims to determine chromosomes' shape, number, and size. The research was conducted at the Cytology Laboratory of the LIPI Biology Research Center, Bogor, West Java. Analysis of research results based on observations of shooting results and data on the size and shape of chromosomes was carried out descriptively. The chromosome number of Phaius callosus and Phaius montanus had a similarity of 2n = 46, metacentric in shape. The chromosome size of Phaius callosus was 1.30 ± 0.29, while that of Phaius montanus was 2.32 ± 1.01. The karyotype formula of Phaius callosus and Phaius montanus had a similarity of 2n=46=23m which was dominantly metacentric.

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“…In addition, differences in condensation levels, gene, and protein content can also affect chromosome size (Apriyanti et al, 2013).…”

Section: Chromosome Sizementioning

confidence: 99%

Orchids Cytogenetics and Karyotype of Phaius callosus and Phaius montanus

Hartati

2022

AFP

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“…Warna LAB juga sangat sesuai untuk menganalisa statistik warna untuk ekstraksi fitur warna yang lebih baik dan mereduksi jumlah perhitungan pixel [12]. Penggunaan metode mophologi seperti holes [13] dan filter untuk menghapus noise merupakan operator morphologi untuk meningkatkan hasil segmentasi yang sudah dilalui melalui k-means. Kemudian kami menggunakan blob analisa untuk menganalisa area yang terbentuk dari hasil segmentasi menggunakan K-means.…”

Section: Pendahuluanunclassified

Segmentasi Citra Bunga Menggunakan Blob Analisis

Ikasari

Amalia

Rosyani

2021

bits

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Content Based Image Retrieval (CBIR) for image segmentation is a concern this year, especially in the development of computer vision. The object discussed in this study is about interest, which uses a dataset from ImageCLEF2017 by taking 8 flower samples. Image of flowers in the dataset is still a lot of noise such as the initial background behind objects such as leaves, tree trunks or others. So we need a method to eliminate the noise, this method for cleaning noise is done by color clusters using the K-means method. By color clustering using K-Means and using color clusters k=2,3,4,and5. After that, a morphological process is carried out in order to obtain a clean area so that it can be compared with the original image and the Blob values formed. Blob analysis is calculated after the process of cleaning the noise is done in order to get the best value in the process of recognition of images with objects of interest. The results of the segmentation process that have been done are the highest MSE and RMSE values are at k-means results with k=4, while for PNSR are at k=2, and for the lowest MSE and RMSE values are at k=5, while the lowest PNSR is at k=4

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“…Untuk mendapatkan fitur-fitur yang dapat diproses untuk mengenali sebuah objek dibutuhkan sebuah proses segmentasi. Segmentasi merupakan bagian dari pengolahan citra digital dan merupakan proses yang cukup penting [2], [3]. Segmentasi citra ini sudah banyak digunakan salah satunya untuk citra MRI [4], pengenalan plat kendaraan bermotor [5], pengenalan wajah [6] dan pengenalan citra bunga [7].…”

Section: Pendahuluanunclassified

Deteksi Citra Bunga Menggunakan Analisis Segmentasi Fuzzy C-Means dan Otsu Threshold

Rosyani

Saprudin

2020

matrik

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Segmentasi merupakan proses penting di dalam proses pengenalan citra. Segmentasi citra penting untuk mengektrasi fitur yang kita akan ambil sebagai data di dalam penelitian. Beberapa metode segmentasi digunakan di dalam proses pengambilan fitur. Namun di dalam penelitian ini kami menggunakan metode Fuzzy C-Means dan Otsu Threshold untuk mendeteksi citra bunga. Agar citra bunga dapat dikenali oleh komputer seperti penglihatan manusia. Dataset yang digunakan mengunakan Imageclef 2017. Citra yang diambil sebagai sample sebanyak 41 citra dengan kondisi background citra yang komplek dengan noise. Tujuan penelitian ini adalah mendapatkan metode segmentasi yang lebih baik di antara metode Fuzzy C-Means dengan Otsu Threshold. Hasil dari penelitian ini didapat dari 41 percobaan keberhasilan segmentasi Fuzzy C-Means mendeteksi objek secara sempurna adalah sebanyak 28 citra dan 16 citra yang gagal. Sedangkan untuk segmentasi menggunakan Otsu Threshold adalah sebanyak 24 citra yang sesuai dan 17 citra yang gagal. Persentase keberhasilan untuk metode Fuzzy C-Means adalah 61% dan untuk metode Otsu Threshold 70,8%.

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Identification of orchid species using content-based flower image retrieval

Cited by 22 publications

References 10 publications

Orchids Cytogenetics and Karyotype of Phaius callosus and Phaius montanus

Orchids Cytogenetics and Karyotype of Phaius callosus and Phaius montanus

Segmentasi Citra Bunga Menggunakan Blob Analisis

Deteksi Citra Bunga Menggunakan Analisis Segmentasi Fuzzy C-Means dan Otsu Threshold

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