Deteksi Penyakit Brown Eye Spot pada Daun Kopi Menggunakan Metode Euclidean Distance dan Hough Transform

2020 ◽  
Vol 1 (1) ◽  
pp. 43-50
Author(s):  
Ike Fibriani ◽  
Widjonarko ◽  
Catur Suko Sarwono ◽  
Firecky Dwika
Keyword(s):  
Hough Transform ◽  
Euclidean Distance ◽  
Brown Eye Spot

Pengolahan citra digital memiliki manfaat yang bisa digunakan dalam lingkup yang beragam, salah satunya dalam lingkungan perkebunan kopi. Dengan memanfaatkan pengolahan citra, citra daun yang didapat dalam perkebunan kopi, bisa diketahui jenis kopi beserta penyakit yang diderita. Untuk mengetahui jenis daun akan menggunakan metode euclidean distance, dimana daun yang digunakan sebagai objek penelitian merupakan daun kopi robusta dan daun kopi arabika. Untuk penyakit pada daun kopi terdapat berbagai macam, namun penyakit yang digunakan sebagai objek penelitian hanyalah penyakit brown eye spot. Pendeteksian penyakit dilakukan menggunakan metode hough transform, dikarenakan metode ini dapat digunakan untuk mendeteksi lingkaran yang merupakan gejala dari penyakit brown eye spot . Tujuan dalam penelitian ini yaitu untuk menganalisa keefektifan dari metode yang digunakan, yang pertama yaitu tingkat akurasi euclidean distance untuk mendeteksi daun uji coba antara daun kopi arabika dan daun kopi robusta. Metode yang kedua menganalisa tingkat keefektifan tingkat akurasi dalam pendeteksian penyakit brown eye spot pada daun uji coba menggunakan hough transform. Uji coba dilakukan terhadap 7 daun kopi arabika dan 4 daun kopi robusta menggunakan Matlab R2017a, dimana hasil tidak terjadi kekeliruan terhadap pendeteksian tehrhadap daun uji coba, ketujuh daun kopi arabika dikenali sebagai daun kopi arabika, dan keempat daun kopi robusta dikenali sebagai daun kopi robusta. Pada metode kedua untuk pendeteksian penyakit brown eye spot pada daun uji coba didapatkan keakurasian pada daun arabika sebesar 55% dan untuk daun kopi robusta sebesar 50%.

scholarly journals DOA estimation of spectrally overlapped LFM signals based on STFT and Hough transform

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiaofa Zhang ◽  
Weike Zhang ◽  
Ye Yuan ◽  
Kaibo Cui ◽  
Tao Xie ◽  
...  
Keyword(s):  
Hough Transform ◽  
Euclidean Distance ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Single Source ◽  
Selection Algorithm ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Time Frequency ◽  
Short Time

AbstractTraditional subspace methods which are based on the spatial time-frequency distribution (STFD) matrix have been investigated for direction-of-arrival (DOA) estimation of linear frequency modulation (LFM) signals. However, the DOA estimation performance may degrade substantially when multiple LFM signals are spectrally overlapped in time-frequency (TF) domain. In order to solve this problem, this paper proposes single-source TF points selection algorithm based on Hough transform and short-time Fourier transform (STFT). Firstly, the signal intersections in TF domain can be solved based on the Hough transform, and multiple-source TF points around the intersections are removed, so that the single-source TF points set is reserved. Then, based on the Euclidean distance operator, the single-source TF points set belonging to each signal can be obtained according to the property that TF points of the same signal have same eigenvector. Finally, the averaged STFD matrix is constructed for each signal, and DOA estimation is achieved based on multiple signal classification (MUSIC) algorithm. In this way, the proposed algorithm exhibit remarkable superiority in estimation accuracy and angular resolution over the state-of-the-art schemes and can achieve DOA estimation in the underdetermined cases. In addition, the proposed algorithm can still perform DOA estimation when multiple LFM signals intersect at one point. Numerical simulations demonstrate the validity of the proposed method.

ELLIPSOIDAL DROPLET DETECTION USING RANDOMIZED HOUGH TRANSFORM

2006 ◽  
Vol 16 (4) ◽  
pp. 365-378
Author(s):  
Y. J. Choo ◽  
B. S. Kang
Keyword(s):  
Hough Transform ◽  
Randomized Hough Transform

Modified Hough Transform in Problem of Increasing Probability of Target Detection in Electronic Surveillance Systems

2019 ◽  
Vol 24 (3) ◽  
pp. 291-300
Author(s):  
Evgeny I. Minakov ◽  
◽  
Aleksandr V. Meshkov ◽  
Elena O. Meshkova ◽  
◽  
...  
Keyword(s):  
Target Detection ◽  
Hough Transform ◽  
Surveillance Systems ◽  
Electronic Surveillance

The lane line detection based on the improved Hough transform

2014 ◽  
Author(s):  
Bo Yin ◽  
Haitao Wang ◽  
Yanping Cong
Keyword(s):  
Hough Transform ◽  
Line Detection ◽  
Lane Line

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

Level Set Method Optimized with the Euclidean Distance Transform

2019 ◽  
Author(s):  
Luis Fernando Segalla ◽  
Alexandre Zabot ◽  
Diogo Nardelli Siebert ◽  
Fabiano Wolf
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

IMPLEMENTASI METODE EUCLIDEAN DISTANCE UNTUK EKSTRAKSI FITUR JARAK PADA CITRA SKELETON

2019 ◽  
Vol 24 (2) ◽  
pp. 134-139
Author(s):  
Miftahul Jannah ◽  
Nurul Humaira
Keyword(s):  
Euclidean Distance

Gait adalah cara atau sikap berjalan kaki seseorang. Tiap orang memiliki cara berjalan yang berbeda, sehingga gerak jalan seseorang sulit untuk disembunyikan ataupun direkayasa. Analisis gait adalah ilmu pengetahuan yang mempelajari tentang kemampuan atau cara bergerak manusia. Dalam bidang kedokteran, analisis gait digunakan untuk menentukan penanganan dan terapi bagi pasien rehabilitasi medik. Dalam penelitian ini digunakan fitur jarak pada citra skeleton. Ekstraksi fitur jarak pada citra skeleton menggunakan metode euclidean distance terbagi dalam beberapa tahapan, dimulai dengan mengambil citra skeleton, konversi citra RGB menjadi citra Biner, proses menemukan titik koordinat dari titik akhir dan titik percabangan, dan ekstraksi fitur pada skeleton. Metode yang digunakan menghasilkan persentase tingkat keberhasilan sebesar 87.84%.

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