Manifold learning using Euclidean k-nearest neighbor graphs [image processing examples]

2004 ◽  
Author(s):  
J.A. Costa ◽  
A.O. Hero
Keyword(s):  
Image Processing ◽  
Manifold Learning ◽  
Nearest Neighbor ◽  
K Nearest Neighbor

scholarly journals Kombinasi Metode ISOMAP Dan KNN Pada Image Processing Untuk Pengenalan Wajah

2020 ◽  
Vol 5 (2) ◽  
pp. 166
Author(s):  
Rifki Kosasih
Keyword(s):  
Image Processing ◽  
Manifold Learning ◽  
Nearest Neighbor ◽  
K Nearest Neighbor

Wajah manusia memiliki ciri khusus yang dapat membedakan dengan orang lainnya sehingga pengenalan wajah sangat penting dilakukan untuk mengenali seseorang. Ciri khusus pada wajah ini disebut juga dengan fitur. Pada penelitian ini, untuk mendapatkan fitur dilakukan pengenalan pola citra wajah dengan menggunakan metode isomap. Metode isomap merupakan salah satu metode dari manifold learning yang menghasilkan fitur-fitur dengan cara mereduksi dimensi. Citra wajah yang digunakan dalam penelitian ini terdiri dari 6 orang dengan tiap orang memiliki 4 citra wajah dengan ekspresi yang berbeda-beda. Data citra ini dibagi menjadi dua bagian yaitu data latih dan data uji. Selanjutnya data citra tersebut diubah menjadi vektor. Metode isomap digunakan untuk mentransformasikan vektor tersebut menjadi vektor yang mengandung fitur wajah. Setelah fitur wajah diperoleh, selanjutnya dilakukan pengujian pada data uji dengan menggunakan algoritma K Nearest Neighbor (KNN).  Algoritma K Nearest Neighbor digunakan untuk pengklasifikasian dengan cara mencari K data latih yang terdekat dengan data uji. Dari hasil klasifikasi diperoleh bahwa tingkat akurasi sebesar 83,33%.

An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

2021 ◽  
Vol 87 (6) ◽  
pp. 445-455
Author(s):  
Yi Ma ◽  
Zezhong Zheng ◽  
Yutang Ma ◽  
Mingcang Zhu ◽  
Ran Huang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Training Data ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

scholarly journals Detection of Diabetic Retinopathy by Retinal Screening using Image Processing Techniques

2020 ◽  
pp. 447-457
Author(s):  
Shaziya Banu S ◽  
Ravindra S
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Nearest Neighbor ◽  
Extreme Condition ◽  
Diabetic Patients ◽  
K Nearest Neighbor ◽  
Image Processing Techniques ◽  
Primary Driver ◽  
Processing Techniques

<p>Diabetic Retinopathy (DR) is a related malady with diabetes and primary driver of sightlessness in diabetic patients. Epidemiological overview categorizes DR among four significant reasons for sight impedance. DR is a microvascular entanglement in which meager retinal veins may blast, bringing about vision misfortune. In this condition veins in retina swells and may blast in severe extreme condition. Operative medication is timely discovery by steady screenings that is by emphasizing the determination of retinal images using appropriate image processing techniques such as, Preprocessing of retinal image, image segmentation using sobel edge detector, local features extraction like mean, standard deviation, variance, Entropy, histogram values and so on. For classification of retina, system uses K-Nearest Neighbor (KNN) classifier. By adopting this approach, The classification of normal and abnormal images of retina is easy and will reduce the number of reviews for the ophthalmologists. Developing a method to automate functionality of retinal examination helps doctor to identify patient’s condition on disease. So that they can medicate the disease accordingly.</p>

scholarly journals Quality and Defect Inspection of Green Coffee Beans Using a Computer Vision System

2019 ◽  
Vol 9 (19) ◽  
pp. 4195 ◽  
Author(s):  
García ◽  
Candelo-Becerra ◽  
Hoyos
Keyword(s):  
Image Processing ◽  
Nearest Neighbor ◽  
Vision System ◽  
Coffee Bean ◽  
Green Coffee ◽  
K Nearest Neighbor ◽  
Green Coffee Beans ◽  
Coffee Beans ◽  
Supervised Learning Algorithms ◽  
K Nearest Neighbor Algorithm

There is an increased industry demand for efficient and safe methods to select the best-quality coffee beans for a demanding market. Color, morphology, shape and size are important factors that help identify the best quality beans; however, conventional techniques based on visual and/or mechanical inspection are not sufficient to meet the requirements. Therefore, this paper presents an image processing and machine learning technique integrated with an Arduino Mega board, to evaluate those four important factors when selecting best-quality green coffee beans. For this purpose, the k-nearest neighbor algorithm is used to determine the quality of coffee beans and their corresponding defect types. The system consists of logical processes, image processing and the supervised learning algorithms that were programmed with MATLAB and then burned into the Arduino board. The results showed this method has a high effectiveness in classifying each single green coffee bean by identifying its main visual characteristics, and the system can handle several coffee beans present in a single image. Statistical analysis shows the process can identify defects and quality with high accuracy. The artificial vision method was helpful for the selection of quality coffee beans and may be useful to increase production, reduce production time and improve quality control.

scholarly journals Aplikasi Penentuan Tingkat Kesehatan Terumbu Karang Menggunakan Metode K-Nearest Neighbor dan Curve Fitting Berbasis Pengolahan Citra Digital

d'CARTESIAN ◽  
2017 ◽  
Vol 6 (1) ◽  
pp. 17
Author(s):  
Christian Elric Koba ◽  
Chriestie Montolalu ◽  
Altien Rindengan
Keyword(s):  
Image Processing ◽  
Curve Fitting ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Coral Health

Terumbu karang merupakan sebuah ekosistem laut yang secara langsung sangat mempengaruhi kehidupan manusia. Akan tetapi, terumbu karang telah banyak mengalami kerusakan yang ditandai dengan pemutihan terumbu karang. Tujuan dari penelitian ini yakni untuk membangun sebuah sistem aplikasi yang mampu melakukan klasifikasi kelas warna terumbu karang berdasarkan Coral Health Chart dengan menggunakan ciri warna RGB (red, green, blue) serta menentukan persentasi tingkat kesehatan terumbu karang tersebut.  K-Nearest Neighbor merupakan metode yang digunakan untuk melakukan klasifikasi kelas warna terumbu karang. Dalam melakukan perhitungan persentase kesehatan terumbu karang digunakan metode Curve Fitting yang didasarkan pada rata-rata nilai RGB gambar terumbu karang. Penelitian ini menggunakan basis data citra Coral Health Chart. Sebagai data uji diambil 10 sampel citra terumbu karang.  Baik K-Nearest Neighbor maupun Curve Fitting keduanya dapat digunakan untuk mengolah sebuah data berbentuk citra digital serta dapat diimplementasikan kedalam sebuah sistem aplikasi.Kata Kunci :  Terumbu    Karang,    K-Nearest   Neighbor,  Curve   Fitting,  Coral  Health Chart, Image Processing.

scholarly journals Klasifikasi Sel Darah Putih Berdasarkan Ciri Warna dan Bentuk dengan Metode K-Nearest Neighbor (K-NN)

2016 ◽  
Vol 6 (2) ◽  
pp. 151 ◽  
Author(s):  
Mizan Nur Khasanah ◽  
Agus Harjoko ◽  
Ika Candradewi
Keyword(s):  
Image Processing ◽  
Blood Cells ◽  
Nearest Neighbor ◽  
White Blood Cells ◽  
Processing Technique ◽  
Image Processing Technique ◽  
K Nearest Neighbor ◽  
Traditional Procedure ◽  
Early Diagnose

The traditional procedure of classification of blood cells using a microscope in the laboratory of hematology to obtain information types of blood cells. It has become a cornerstone in the laboratory of hematology to diagnose and monitor hematologic disorders. However, the manual procedure through a series of labory test can take a while. Thresfore, this research can be helpful in the early stages of the classification of white blood cells automatically in the medical field.Efforts to overcome the length of time and for the purposes of early diagnose can use the image processing technique based on morphology of blood cells. This research aims to classify the white blood cells based on cell morphology with the k-nearest neighbor (knn). Image processing algorithms used hough circle, thresholding, feature extraction, then to the process of classification was used the method of k-nearest neighbor (knn).In the process of testing used 100 images to be aware of its kind. The test results showed segmentation accuracy of 78% and testing the classification of 64%.

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