scholarly journals Optimasi Hasil Akuisisi Wajah Dengan Variasi Proyeksi Menggunakan Kedekatan Pola Jarak Pixel

2019 ◽  
Vol 5 (1) ◽  
pp. 44-54
Author(s):  
Arief Bramanto Wicaksono Putra ◽  
Yusi Aribah
Keyword(s):  
Correlation Coefficient ◽  
Euclidean Distance

Akuisisi merupakan proses awal untuk mendapatkan citra digital. Pada penelitian ini menerapkan akuisisi dengan jenis-jenis proyeksi sudut yang bertujuan untuk menganalisis perbandingan hasil akuisisi yang data sudutnya bervariasi dari sebuah obyek wajah. Ekstraksi ciri dilakukan untuk memperoleh nilai spesifik dari akuisisi wajah, obyek penelitian berupa wajah memiliki 9 sampel dengan identitas sudut 0o, +15o, +45o, +75o, +90o, -15o, -45o, -75o, dan -90o. Pada proses ekstraksi ciri menggunakan Correlation Coefficient semua hasil akuisisi obyek wajah telah melalui tahapan pre-processing. Pengujian performansi dari variasi proyeksi tersebut menggunakan metode Euclidean Distance sebagai pengukur jarak kedekatan antar variasi sudut, dimana selisih antar jarak yang diperoleh akan menjadi nilai dari performansi yang diinginkan. Hasil performansi akuisisi terbaik dari obyek A adalah sudut 0o terhadap +15o dan -15o, obyek B sudut 0o terhadap +15o dan -15o, dan obyek C sudut 0o terhadap +90o dan -90o.

scholarly journals Selection of Features on Mining Techniques for Classification

2018 ◽  
Vol 7 (S1) ◽  
pp. 108-111
Author(s):  
Gurrampally Kumar ◽  
S. Mohan ◽  
G. Prabakaran
Keyword(s):  
Feature Selection ◽  
Linear Regression ◽  
Gaussian Process ◽  
Statistical Method ◽  
Correlation Coefficient ◽  
Euclidean Distance ◽  
Experimental Results ◽  
Selection Of

Feature selection has been developed by several mining techniques for classification. Some existing approaches couldn’t remove the irrelevant data from dataset for class. Thus it needs the selection of appropriate features that emphasize its role in classification. For this it consider the statistical method like correlation coefficient to identify the features from feature set whose data are very important for existing classes. The several methods such as Gaussian process, linear regression and Euclidean distance have taken into consideration for clarity of classification. The experimental results reveal that the proposed method identifies the exact relevant features for several classes.

scholarly journals A cophenetic correlation coefficient for Tocher's method

2013 ◽  
Vol 48 (6) ◽  
pp. 589-596 ◽  
Author(s):  
Anderson Rodrigo da Silva ◽  
Carlos Tadeu dos Santos Dias
Keyword(s):  
Correlation Coefficient ◽  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Empirical Distribution ◽  
Morphological Characters ◽  
The Other ◽  
Cophenetic Correlation ◽  
R Language ◽  
Average Linkage ◽  
Cophenetic Correlation Coefficient

The objective of this work was to propose a way of using the Tocher's method of clustering to obtain a matrix similar to the cophenetic one obtained for hierarchical methods, which would allow the calculation of a cophenetic correlation. To illustrate the obtention of the proposed cophenetic matrix, we used two dissimilarity matrices - one obtained with the generalized squared Mahalanobis distance and the other with the Euclidean distance - between 17 garlic cultivars, based on six morphological characters. Basically, the proposal for obtaining the cophenetic matrix was to use the average distances within and between clusters, after performing the clustering. A function in R language was proposed to compute the cophenetic matrix for Tocher's method. The empirical distribution of this correlation coefficient was briefly studied. For both dissimilarity measures, the values of cophenetic correlation obtained for the Tocher's method were higher than those obtained with the hierarchical methods (Ward's algorithm and average linkage - UPGMA). Comparisons between the clustering made with the agglomerative hierarchical methods and with the Tocher's method can be performed using a criterion in common: the correlation between matrices of original and cophenetic distances.

The Analysis of Similarity Measure Function in Image Matching Algorithms

2013 ◽  
Vol 842 ◽  
pp. 649-653 ◽  
Author(s):  
Hong Liang Liu ◽  
Wei Song ◽  
Peng Yu Na ◽  
Ming Li ◽  
Pei Yang
Keyword(s):  
Correlation Coefficient ◽  
Similarity Measure ◽  
Image Matching ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Noise Pollution ◽  
Manhattan Distance ◽  
Real Time Processing ◽  
Measure Function ◽  
Measure Methods

Similarity measure function is one of the most important factors influencing the matching precision in the field of computer vision. In this paper, a survey is done on the application frequency of distance similarity measure methods and related similarity measure methods, also the statistic characteristic is been given. The significance of Measure functions variable parameters in image matching is showed. In the real time processing aspect, drawn the conclusion that Manhattan distance measure is the fastest, Euclidean distance take second place, correlation coefficient is worst. However, in the robustness of the noise pollution aspect, correlation coefficient has the strongest robustness, then followed is Manhattan distance, Euclidean distance is worst.

scholarly journals DETECÇÃO DE LESÕES EM MAMOGRAFIAS ATRAVÉS DA ASSIMETRIA DAS MAMAS E EXTRAÇÃO DE CARACTERÍSTICAS COM ÍNDICE DE GETIS-ORD

2013 ◽  
Vol 20 (3) ◽  
pp. 81
Author(s):  
Antônio Marcos Vieira Sales ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Correlation Coefficient ◽  
Euclidean Distance ◽  
Cross Correlation ◽  
Lesion Detection ◽  
Cáncer De Mama ◽  
Support Vector ◽  
Processing Techniques

O câncer de mama é aquele que tem início nas células das mamas. A principal forma de prevençãoe diagnóstico precoce é através de exames de mamografia. Este trabalho tem como objetivo principalapresentar uma metodologia de auxílio à detecção de lesões em mamografias a partir da determinação de regiões suspeitas por nível de simetria. Técnicas de Processamento de Imagem foram usadas para preparar as mamografias e, em seguida, o nível de simetria entre a mama esquerda e a direita foi medido com coeficiente de correlação cruzada e distância euclidiana. O índice de Getis-Ord na sua forma geral foi usado para extrair características das imagens para treinar uma Máquina de Vetores de Suporte que classificouregiões das mamografias em lesão e não lesão. A metodologia, de modo geral, apresentou 80,11% de sensibilidade, 84,41% de especificidade e 84,38% de acurácia.Palavras-chave: Câncer de mama. Mamografia. Coeficiente de correlação cruzada. Distância euclidiana. Índice de Getis-Ord. Máquina de vetores de suporte. LESION DETECTION IN MAMMOGRAMS THROUGH THE ASYMMETRY OF THEBREASTS AND FEATURE EXTRACTION WITH INDEX GETIS-ORDAbstract: Breast cancer is one that starts in the cells of the breast. The main form of prevention and early diagnosis is through mammograms. This work has as main goal to present a methodology to aid in the detection of lesions on mammograms from the determination of suspicious regions by level of symmetry. Image processing techniques were used to prepare the mammograms and then the degree of symmetry between left and right breasts was measured using cross-correlation coefficient and Euclidean distance. The index Getis-Ord was used to extract features from images to train a Support Vector Machine which classified regions of mammograms in lesion and non-lesion. The methodology, in general, showed 80.11% sensitivity, 84.41% specificity and 84.38% accuracy.Keywords: Breast cancer. Mammography. Cross-correlation coefficient. Euclidean distance. Index Getis-Ord. Support vector machine. DETECCIÓN DE LESIONES EN LAS MAMOGRAFÍAS A TRAVÉS DE LA ASIMETRÍA DE LAS MAMAS Y EXTRACCIÓN DE CARACTERÍSTICAS CON EL ÍNDICE GETIS-ORDResumen: El cáncer de mama comienza en las células de los senos. La principal forma de prevención y diagnóstico precoz es a través de mamografías. Este trabajo tiene como objetivo principal presentar una metodología para ayudar en la detección de lesiones en las mamografías a partir de la determinación de las regiones sospechosas por nivel de simetría. Técnicas de procesamiento de imágenes se utilizaron para preparar las mamografías y luego el nivel de simetría entre el pecho izquierdo y derecho se midió utilizando el coeficiente de correlación cruzada y la distancia euclidiana. El índice Getis-Ord se utilizó para extraer características de las imágenes para formar una máquina de vectores de soporte que las regiones clasificadasde mamografías en lesión y no la lesión. La metodología, en general, mostró 80,11% de sensibilidad, especificidad 84,41% y 84,38% de precisión.Palabras clave: Cáncer de mama. Mamografía. Coeficiente de correlación cruzada. Distancia euclídea. Índice Getis-Ord. Máquina de vectores soporte.

scholarly journals Verifikasi Golongan Darah Manusia Berbasis Citra Dijital Menggunakan Logika Fuzzy

2018 ◽  
Vol 4 (1) ◽  
pp. 23-32
Author(s):  
Arief Bramanto Wicaksono Putra ◽  
Didi Susilo Budi Utomo ◽  
M Dicky Rahmawan
Keyword(s):  
Correlation Coefficient ◽  
Euclidean Distance ◽  
Acceptance Rate ◽  
False Acceptance Rate ◽  
Fuzzy Linguistic ◽  
False Acceptance

Sistem biometrika merupakan teknologi pengenalan diri dengan menggunakan bagian tubuh manusia ataupun dari perilaku manusia, untuk meningkatkan efisiensi dan efektifitas dalam setia aspek kehidupan dengan mengurangi pemakaian kartu identitas dan kata sandi. Diperlukan sebuah sistem yang dapat membantu manusia untuk mengenali tipe golongan darah. pengenalan tipe golongan darah dapat dilakukan computer salah satunya dengan metode pengenalan pola dan pelatihan masing masing karakterristik golongan darah melalui citra.Percobaan pada penelitian ini membahas tentang verifikasi golongan darah manusia yang diawali dengan pengumpulan data, akusisi citra, preprocessing, ekstraksi ciri. dari sel darah manusia yang nantinya dapat membentuk suatu pola khusus dari kumpulan hasil ekstraksi ciri. Dengan menggunakan kombinasi metode euclidean distance dan correlation coefficient diperoleh pola hasil pelatihan yang menggunakan fuzzy linguistic value berada pada rentang low medium dan medium. Dengan menggunakan 20 data uji dimana setiap golongan darah terdiri dari 5 sampel, diperoleh keputusan hasil verifikasi kecocokan yang diuji dengan menggunakan metode unjuk kerja False Acceptance Rate (FAR) sebesar  dan False Rejected Rate (FRR) sebesar 45% dengan tingkat Akurasi (Acc) sebesar 83%

scholarly journals Morphometric diversity and phenotypic relationship among indigenous buffaloes of Banten, Indonesia

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Dewi Murni ◽  
UMIE LESTARI ◽  
SRI ENDAH INDRIWATI ◽  
ACHMAD EFENDI ◽  
NANI MARYANI ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Correlation Coefficient ◽  
Body Length ◽  
Euclidean Distance ◽  
Genetic Distances ◽  
The Body ◽  
Chest Circumference ◽  
Morphometric Characters ◽  
Distance Analysis ◽  
Multivariate Discriminant

Abstract. Murni D, Lestari U, Indriwati SE, Efendi A, Maryani N, Amin M. 2020. Morphometric diversity and phenotypic relationship among indigenous buffaloes of Banten, Indonesia. Biodiversitas 21: 933-940. This study aimed to describe the morphometric diversity and phenotypic relationship among indigenous buffaloes of Banten, Indonesia. In this study, 125 buffaloes from six regions were investigated based on 11 morphometric characters. Morphometric diversity was analyzed using multivariate discriminant analysis. The Euclidean genetic distances were used to estimate the phenotypic relationship among the buffalo populations. The indigenous buffaloes of Banten have high morphometric diversity, with a coefficient from 2.83 to 41.43%. The body length and chest circumference can be used as a morphometric marker to determine potential indigenous buffaloes as their high correlation coefficient value (0.506). The Serang district buffaloes have the highest mean of body length and chest circumference, which shows that this population is potential compared to the populations from other regions. The morphometric of buffalo population from Serang City, Cilegon City, Serang District, and Pandeglang District tend to be homogenous. Meanwhile, Lebak and Tangerang District population tends to heterogeneous. According to Euclidean distance analysis, the proximate phenotypic relationship was between Serang and Pandeglang District's buffalo populations. Our results indicated that morphometric diversity and phenotypic relationships of the populations were related to geographical origins and can be used to determine the potential indigenous of buffaloes.

scholarly journals Analisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias

2020 ◽  
Vol 5 (2) ◽  
pp. 57
Author(s):  
Novia Hasdyna ◽  
Rozzi Kesuma Dinata
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Ornamental Fish ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor ◽  
Matthew Correlation Coefficient

K-Nearest Neighbor (K-NN) is a machine learning algorithm that functions to classify data. This study aims to measure the performance of K-NN algorithm by using Matthew Correlation Coefficient (MCC). The data that used in this study are the ornamental fish which consisting of 3 classes named Premium, Medium, and Low. The analysis results of the Matthew Correlation Coefficient on K-NN using Euclidean Distance obtained the highest MCC value in Medium class which is 0.786542. The second highest MCC value is in Premium class which is 0.567434. The lowest MCC value is in Low class which is 0.435269. Overall, the MCC values is statistically which is 0,596415.

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