Feature Modeling and Classification of Halftone Image Based on Statistical Method

2012 ◽  
Vol 229-231 ◽  
pp. 1693-1696
Author(s):  
Zhi Qiang Wen ◽  
Wen Qiu Zhu ◽  
Yong Xiang Hu ◽  
Zhao Yi Peng
Keyword(s):  
Autocorrelation Function ◽  
Feature Vector ◽  
Classification Performance ◽  
Feature Modeling ◽  
Gray Level ◽  
Halftone Image ◽  
Occurrence Matrix ◽  
Halftone Images ◽  
Better Than

For problem of feature modeling on halftone image, three statistics methods, named gray-level co-occurrence matrix, autocorrelation function and spectrum statistics, are used to extract feature vector of various halftone images. Then, their classification performance is assessed by radial basis function neural network. A mass of experiments show the autocorrelation function is better than other two methods for classification on halftone image.

Image Processing Applications Based on Texture and Fractal Analysis

2011 ◽  
pp. 226-250 ◽  
Author(s):  
Radu Dobrescu ◽  
Dan Popescu
Keyword(s):  
Fractal Dimension ◽  
Texture Classification ◽  
Malignant Tumour ◽  
Classification Performance ◽  
Textured Surfaces ◽  
Box Counting ◽  
Image Partition ◽  
Fractal Analyses ◽  
Occurrence Matrix

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives from the mean co-occurrence matrices: contrast, energy, entropy, homogeneity, and variance. The second class is based on fractal dimension and is derived from a box-counting algorithm. For the purpose of increasing texture classification performance, the notions “mean co-occurrence matrix” and “effective fractal dimension” are introduced and utilized. Some applications of the texture and fractal analyses are presented: road analysis for moving objective, defect detection in textured surfaces, malignant tumour detection, remote land classification, and content based image retrieval. The results confirm the efficiency of the proposed methods and algorithms.

Method of Detecting Sea State from Image Taken by Autonomous Surface Vehicle

2013 ◽  
Vol 316-317 ◽  
pp. 475-478
Author(s):  
Jian Hua Wang ◽  
Gang Li ◽  
Ya Zhou Xiong ◽  
Kang Ke Liu
Keyword(s):  
Water Surface ◽  
Texture Feature ◽  
Gray Level ◽  
Sea State ◽  
Autonomous Surface Vehicle ◽  
State Detection ◽  
Occurrence Matrix ◽  
Safe Approach ◽  
Dangerous Condition ◽  
Better Than

Autonomous surface vehicle provides a safe approach to monitor environment on water surface in dangerous condition. This paper presents a method of sea state detection from images taken by a camera fixed on an autonomous surface vehicle. Based on texture feature of images from water surface scene, gray level co-occurrence matrix is computed, and its features including energy, contrast, correlation and entropy are extracted. Experiments show that the contrast can differentiate the sea state levels better than the others. To improve discrimination at low sea state levels, a transform is proposed. Performance of the method at different light shining conditions is discussed, and the results validate the method.

scholarly journals Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Atsushi Teramoto ◽  
Yuka Kiriyama ◽  
Tetsuya Tsukamoto ◽  
Eiko Sakurai ◽  
Ayano Michiba ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Classification Performance ◽  
Multiple Instance Learning ◽  
Cancer Type ◽  
Malignant Cells ◽  
Cytological Examination ◽  
Automated Method ◽  
Weakly Supervised ◽  
Better Than

AbstractIn cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or malignant. However, it is often difficult to label all cells. In this study, we developed a weakly supervised method for the classification of benign and malignant lung cells in cytological images using attention-based deep multiple instance learning (AD MIL). Images of lung cytological specimens were divided into small patch images and stored in bags. Each bag was then labeled as benign or malignant, and classification was conducted using AD MIL. The distribution of attention weights was also calculated as a color map to confirm the presence of malignant cells in the image. AD MIL using the AlexNet-like convolutional neural network model showed the best classification performance, with an accuracy of 0.916, which was better than that of supervised learning. In addition, an attention map of the entire image based on the attention weight allowed AD MIL to focus on most malignant cells. Our weakly supervised method automatically classifies cytological images with acceptable accuracy based on supervised learning without complex annotations.

scholarly journals Digit Classification of Majapahit Relic Inscription using GLCM-SVM

2018 ◽  
Vol 1 (2) ◽  
pp. 46
Author(s):  
Tri Septianto ◽  
Endang Setyati ◽  
Joan Santoso
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Template Matching ◽  
Support Vector ◽  
Gray Level ◽  
Class 1 ◽  
Occurrence Matrix ◽  
Handwritten Recognition

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

scholarly journals CLASSIFICATION OF MEAT IMAGERY USING ARTIFICIAL NEURAL NETWORK METHOD AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL CO-OCCURRENCE MATRIX METHOD

2021 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Chairul Imam ◽  
Eka Wahyu Hidayat ◽  
Neng Ika Kurniati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Feature ◽  
General Term ◽  
Gray Level ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Occurrence Matrix

Lately, there is often a mixture of beef and pork done by traders to the general public as buyers. This is due to the unconsciousness of the buyer on how to recognize the type of meat purchased. The effect of this meat mix can certainly be detrimental to buyers, especially Muslims. Image processing is a general term for various methods in which it is used to manipulate and modify images in various ways. Classification is a method of grouping some information and ensuring it is listed in a class.. Classification of beef and pork differentiator in this application using Artificial Neural Network (ANN) method while for texture extraction using Gray Level Co-occurrence Matrix (GLCM) method. The information used in the examination was 30 images of fresh meat divided into 15 images of fresh beef and 15 images of fresh pork. The data used is data Classification of Beef and Pork Image based on Color and Texture Characteristics. The result of classification accuracy obtained in this application is 80%.

Deep Learning Based Tobacco Products Classification (Preprint)

2019 ◽  
Author(s):  
Murat Taşkıran ◽  
Sibel Çimen Yetiş
Keyword(s):  
Young People ◽  
Feature Vector ◽  
Weighted Average ◽  
Classification Performance ◽  
Accuracy Rate ◽  
Tobacco Products ◽  
Internet Environment ◽  
Performance Rate ◽  
Fully Connected

BACKGROUND Various images and videos are uploaded every day or even every second on Instagram. These publicly available images are easily accessible as a result of uncontrolled Internet use by young people and children. Shared images include tobacco products and can be encouraging for young people and children when they are accessible. OBJECTIVE In this study, it is aimed to detect tobacco and tobacco products with various Convolutional Neural Networks (CNNs) and to limit the access of young users to these detected tobacco products over the Internet. METHODS A total of 1607 public images were collected from Instagram, and feature vectors were extracted with various CNNs, which proved to be successful in the competitions and CNN was determined to be proper for detect tobacco products. RESULTS MobileNet gave the highest results 99.1% as weighted average. The feature vector of the input images are extracted with CNNs and classified with the latest fully connected layer. CONCLUSIONS The classification of the tobacco products of 4 different classes was studied by using the networks and the classification performance rate was obtained as 100% for 322 test images via MobileNet. In this way, the content that is encouraging for children can be censored or filtered with a high accuracy rate and a secure Internet environment can be provided.

A NEW OPTIMIZED WAVELET TRANSFORM FOR HEART BEAT CLASSIFICATION

2015 ◽  
Vol 15 (05) ◽  
pp. 1550081 ◽  
Author(s):  
BABY PAUL ◽  
K. T. SHANAVAZ ◽  
P. MYTHILI
Keyword(s):  
Wavelet Transform ◽  
Probabilistic Neural Network ◽  
Classification Performance ◽  
Heart Beat ◽  
Medical Instrumentation ◽  
Continuous Wavelet ◽  
Beat Classification ◽  
Ga Algorithm ◽  
Better Than

A method for automatic classification of Arrhythmias from Electrocardiogram based on features generated from a new Continuous Wavelet Transform (CWT) is presented in this paper. The classification performance was studied using the most commonly available database, the MIT-BIH arrhythmia database. The new wavelet for classification was evolved using Genetic Algorithm (GA). The optimum wavelet for classification was obtained after several runs of the GA algorithm. The class labeling was followed according to the Association for the Advancement of Medical Instrumentation (AAMI). The wavelet scales corresponding to the different frequency levels giving maximum classification performance was identified. Probabilistic Neural Network (PNN) classifier was used for classification. The proposed classification system offered an overall sensitivity of 97% for Normal beats (N), 75% for Supraventricular beats (Sv) and 93% for Ventricular beats (V) which is better than existing results reported in literature. This technique could exclusively identify some of the isolated abnormalities compared to other results reported.

scholarly journals Feature extraction and classification of surface discharges on an ice-covered insulator string during AC flashover using gray-level co-occurrence matrix

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong Liu ◽  
Qiran Li ◽  
Boxue Du ◽  
Masoud Farzaneh
Keyword(s):  
Feature Extraction ◽  
High Speed ◽  
Arc Discharge ◽  
Cold Climate ◽  
Surface Discharge ◽  
Gray Level ◽  
Statistical Parameters ◽  
Surface Discharges ◽  
Occurrence Matrix

AbstractThis study focuses on the feature extraction and classification of surface discharges of ice-covered insulator strings during process of alternating current flashover. The test specimen was the five units suspension ceramic insulators, which was artificially accreted with wet-grown ice in the cold-climate room of CIGELE. Based on the IEEE Standard 1783/2009, flashover experiments were conducted on iced insulators to measure the minimum flashover voltage (VMF) and record the propagating process of surface discharges to flashover by using a high-speed video camera. The gray-level co-occurrence matrix (GLCM) method has been used to extract four parameters of arc discharge images features that characterize different stages of flashover process. The parameters are angular second moment (ASM), contrast (CON), inverse difference moment (IDM) and entropy (ENT). These statistical parameters of GLCM can be extracted to reveal the underlying properties of ice flashover on the insulator surface from the quantitative perspective. The different values of these indicators are representative of the different stages in the process of arc discharge. Once the value of quantitative indicators (ASM, CON, IDM, ENT) of surface discharges exceeds the threshold value, the higher flashover risk of iced insulators will appear. Hence, the proposed methods are helpful to understand and monitor surface discharge on iced outdoor insulator strings for preventing flashover accidents.

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