Grayscale Image Classification Using Supervised Chromosome Clustering

2009 ◽  
pp. 64-71
Author(s):  
Debnath Bhattacharyya ◽  
Poulami Das ◽  
Samir Kumar Bandyopadhyay ◽  
Tai-hoon Kim
Keyword(s):  
Image Classification ◽  
Grayscale Image

scholarly journals Apakah kombinasi power lbp dan fourier descriptor dapat digunakan untuk klasifikasi citra kerang?

Teknologi ◽  
2016 ◽  
Vol 6 (2) ◽  
pp. 68
Author(s):  
Putri Aisyiyah Rakhma Devi ◽  
Nanik Suciati ◽  
Wijayanti Nurul Khotimah
Keyword(s):  
Feature Extraction ◽  
Image Classification ◽  
Cross Validation ◽  
Texture Features ◽  
Extraction Process ◽  
Binary Image ◽  
Grayscale Image ◽  
Shape Features ◽  
Fourier Descriptor ◽  
Classification Problems

ABSTRAKPermasalahan pengklasifikasian secara manual biasanya terletak pada hasil akurasi dan waktu klasifikasi. Pengklasifikasi citra kerang pada umumnya dilakukan berdasarkan pada karakteristik bentuk dan tekstur cangkang kerang. Pengembangan perangkat lunak untuk pengklasifikasian secara otomatis diharapkan dapat meningkatkan hasil akurasi dan memperbaiki waktu klasifikasi. Pada penelitian ini bertujuan untuk mengkombinasikan fitur tekstur berbasis metode Power LBP dan fitur bentuk berbasis metode fourier descriptor yang digunakan untuk klasifikasi citra kerang.Citra input yang digunakan, sebelumnya telah melalui praproses dan  segmentasi untuk memisahkan objek dengan background. Citra objek yang sudah terpisah ditransformasi menjadi citra biner dan citra grayscale untuk proses ekstraksi fitur. Hasil dari kedua fitur yang sudah diperoleh akan dilakukan kombinasi dengan mempertimbangkan bobot masing-masing fitur yang kemudian dilakukan normalisasi. Dengan mengkombinasikan fitur tekstur dan fitur bentuk diharapkan memperoleh fitur yang signifikan yang dapat meningkatkan akurasi sebuah klasifikasi.Uji coba dilakukan pada 3 jenis dataset kerang yakni kerang darah, kerang pasir dan kerang bulu dengan menggunakan SVM cross validation dengan k=2 . Hasil uji coba menunjukkan bahwa ada keterkaitan antara mengkombinasikan fitur tekstur dan fitur bentuk pada permasalahan klasifikasi citra kerang dapat diperbaiki dengan hasil akurasi klasifikasi yang diperoleh sebesar 99,39% dengan fitur tekstur lebih dominan daripada fitur yang lainnya. Kata Kunci: citra kerang, ekstraksi fitur, fourier descriptor, klasifikasi, power LBP. ABSTRACTShells image classification are generally conducted based on the characteristics of the shape and texture of the shells. The problems of classification usually occur results of accuracy and timing classification. The software development for classification is expected to increase the yield of accuracy result and optimize the time of classification. In this study, we combine extracting texture features based Power LBP method and extracting shape features based Fourier Descriptor method for shells image classification.   The used input images had been conducted preprocessing  and segmentation to separate object and background using Otsu methods. The objects images that had been separated are transformed into a binary image and grayscale image for feature extraction process. Texture features are extracted using Power LBP (PLBP) method and grayscale image as input. Shape features are extracted using Fourier Descriptor (FD) method and binary image as input. The results of these two features will be combined by considering the weight of each feature and then normalized. Combines texture features and shape features, we expect to obtain significant features that can improve the accuracy of classification.Tests was performed on three types of shells dataset that is blood clams, mussels and scallops feather sand by using SVM cross validation with k = 2 fold. The results show that there is a link between features combine texture and shape features on the image classification problems that can be solved with the results obtained classification accuracy of 99.39% with a texture feature more dominant than the other features. Keywords: classification, feature extraction, Fourier Descriptor , Power LBP, Shellfish image.

USING FUZZY CLUSTERING IN STRUCTURAL METHODS OF IMAGE CLASSIFICATION

2020 ◽  
Vol 79 (9) ◽  
pp. 781-791
Author(s):  
V. О. Gorokhovatskyi ◽  
I. S. Tvoroshenko ◽  
N. V. Vlasenko
Keyword(s):  
Image Classification ◽  
Fuzzy Clustering ◽  
Structural Methods

On an Euler-Lagrange equation for color to grayscale conversion

2019 ◽  
Vol 2019 (1) ◽  
pp. 95-98
Author(s):  
Hans Jakob Rivertz
Keyword(s):  
Variational Inequality ◽  
Color Image ◽  
Lagrange Equation ◽  
New Method ◽  
Grayscale Image ◽  
Structure Tensors

In this paper we give a new method to find a grayscale image from a color image. The idea is that the structure tensors of the grayscale image and the color image should be as equal as possible. This is measured by the energy of the tensor differences. We deduce an Euler-Lagrange equation and a second variational inequality. The second variational inequality is remarkably simple in its form. Our equation does not involve several steps, such as finding a gradient first and then integrating it. We show that if a color image is at least two times continuous differentiable, the resulting grayscale image is not necessarily two times continuous differentiable.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Iteration Based Polarimetric SAR Image Classification

PIERS Online ◽  
2007 ◽  
Vol 3 (5) ◽  
pp. 625-628
Author(s):  
Jian Yang ◽  
Xiaoli She ◽  
Tao Xiong
Keyword(s):  
Image Classification ◽  
Sar Image ◽  
Polarimetric Sar
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