CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION

2009 ◽  
Vol 09 (02) ◽  
pp. 171-199 ◽  
Author(s):  
SHANKAR BHAUSAHEB NIKAM ◽  
SUNEETA AGARWAL
Keyword(s):  
Nearest Neighbor ◽  
Texture Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Network Support ◽  
Wavelet Energy ◽  
Texture Characteristics ◽  
Energy Signature ◽  
Occurrence Matrix ◽  
Fingerprint Database

Perspiration phenomenon is very significant to detect the liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore may not be suitable for real time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, to detect liveness a new texture-based method using only the first fingerprint is proposed. It is based on the observation that real and spoof fingerprints exhibit different texture characteristics. Textural measures based on gray level co-occurrence matrix (GLCM) are used to characterize fingerprint texture. This is based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Wavelet energy signature is also used to obtain texture details. Dimensionalities of feature sets are reduced by Sequential Forward Floating Selection (SFFS) method. GLCM texture features and wavelet energy signature are independently tested on three classifiers: neural network, support vector machine and K-nearest neighbor. Finally, two best classifiers are fused using the "Sum Rule''. Fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

scholarly journals Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN

2017 ◽  
Vol 3 (1) ◽  
pp. 36-44
Author(s):  
Hema Rajini N
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Nearest Neighbor ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Network Support ◽  
First Order ◽  
Occurrence Matrix

A brain tumor classification system has been designed and developed. This work presents a new approach to the automated classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumors based on first order statistics and gray level co-occurrence matrix, in magnetic resonance images. The magnetic resonance feature image used for the tumor detection consists of T2-weighted magnetic resonance images for each axial slice through the head. To remove the unwanted noises in the magnetic resonance image, median filtering is used. First order statistics and gray level co-occurrence matrix-based features are extracted. Finally, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks are used to classify the brain tumor images. The application of the proposed method for tracking tumor is demon­strated to help pathologists distinguish its type of tumor. A classification with an accuracy of 89%, 90%, 91% and 95% has been obtained by, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks.

scholarly journals Deteksi Batik Parang Menggunakan Fitur Co-Occurence Matrix Dan Geometric Moment Invariant Dengan Klasifikasi KNN

2016 ◽  
pp. 40
Author(s):  
Ni Luh Wiwik Sri Rahayu Ginantra
Keyword(s):  
Matrix Method ◽  
Nearest Neighbor ◽  
Texture Features ◽  
Sufficient Information ◽  
K Nearest Neighbor ◽  
Moment Invariant ◽  
The Public ◽  
Geometric Moment ◽  
Visual Identification ◽  
Occurrence Matrix

Batik motifs are the base or the blueprint of batik patterns which serve as the core of the batik image design, and therefore the meaning of a sign, symbol or logo in a batik work can be revealed through its motifs. Visual identification requires visual skills and knowledge in classifying patterns formed in a batik image. Lack of media providing information on batik motifs makes the public unable to have sufficient information about batik motifs. Looking at this phenomenon, this study is conducted in order to perform visual identification using a computer that can assist and facilitate in identifying the types of batik. The methods used for batik image recognition are the Co-occurrence Matrix method to provide extraction of batik texture features, and the Geometric Moment Invariant method, while K Nearest Neighbor is used to classify batik images. The results on the accuracy values obtained reveal that the of 80%, compared to the accuracy value result using the Co-occurrence Matrix method that is 70%.  

Comparative analysis of breast cancer detection in mammograms and thermograms

2015 ◽  
Vol 60 (1) ◽  
Author(s):  
Marina Milosevic ◽  
Dragan Jankovic ◽  
Aleksandar Peulic
Keyword(s):  
Nearest Neighbor ◽  
Region Of Interest ◽  
Texture Features ◽  
Classification Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Characteristic Analysis ◽  
Analysis Society ◽  
Fold Cross Validation ◽  
Neighbor Classifier

AbstractIn this paper, we present a system based on feature extraction techniques for detecting abnormal patterns in digital mammograms and thermograms. A comparative study of texture-analysis methods is performed for three image groups: mammograms from the Mammographic Image Analysis Society mammographic database; digital mammograms from the local database; and thermography images of the breast. Also, we present a procedure for the automatic separation of the breast region from the mammograms. Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from the region of interest. The ability of feature set in differentiating abnormal from normal tissue is investigated using a support vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross-validation method and receiver operating characteristic analysis was performed.

scholarly journals A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bhagya M. Patil ◽  
Vishwanath Burkpalli
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Texture Features ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Cotton Leaf ◽  
K Nearest Neighbor ◽  
Color Features ◽  
Leaf Image

Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%.

scholarly journals A Novel Machine-Learning Framework-based on LBP and GLCM Approaches for CBIR System

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Meenakshi Garg ◽  
Manisha Malhotra ◽  
Harpal Singh
Keyword(s):  
Wavelet Transforms ◽  
Nearest Neighbor ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Texture Image Classification ◽  
Definition Of ◽  
Occurrence Matrix ◽  
Experimental Findings

This paper presents a Multiple-features extraction and reduction-based approaches for Content-Based Image Retrieval (CBIR). Discrete Wavelet Transforms (DWT) on colored channels is used to decompose the image at multiple stages. The Gray Level Co-occurrence Matrix (GLCM) concept is used to extract statistical characteristics for texture image classification. The definition of shared knowledge is used to classify the most common features for all COREL dataset groups. These are also fed into a feature selector based on the particle swarm optimization which reduces the number of features that can be used during the classification stage. Three classifiers, called the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT), are trained and tested, in which SVM give high classification accuracy and precise rates. In several of the COREL dataset types, experimental findings have demonstrated above 94 percent precision and 0.80 to 0.90 precision values.

AUTOMATED DIAGNOSIS OF EPILEPSY USING CWT, HOS AND TEXTURE PARAMETERS

2013 ◽  
Vol 23 (03) ◽  
pp. 1350009 ◽  
Author(s):  
U. RAJENDRA ACHARYA ◽  
RATNA YANTI ◽  
JIA WEI ZHENG ◽  
M MUTHU RAMA KRISHNAN ◽  
JEN HONG TAN ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Probabilistic Neural Network ◽  
Texture Features ◽  
Epileptic Seizures ◽  
Support Vector ◽  
Svm Classifier ◽  
Automated Classification ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Brain Disorder

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

scholarly journals A Novel Multi-Input AlexNet Prediction Model for Oil and Gas Production

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Yang Wang ◽  
Yin Lv ◽  
Dali Guo ◽  
Shu Zhang ◽  
Shixiang Jiao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Oil And Gas ◽  
Nearest Neighbor ◽  
Gas Production ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Oil And Gas Production ◽  
Network Support ◽  
Parameter Values

In the process of oilfield development, it is important to predict the oil and gas production. The predicted value of oil production is the amount of oil that may be obtained within a certain area over a certain period. Because of the current demand for oil and gas production prediction, a prediction model using a multi-input convolutional neural network based on AlexNet is proposed in this paper. The model predicts real oilfield data and achieves good results: increasing prediction accuracy by 17.5%, 20.8%, 11.6%, 8.9%, 6.9%, and 14.9% with respect to the backpropagation neural network, support vector machine, artificial neural network, radial basis function neural network, K-nearest neighbor, and decision tree methods, respectively. It addresses the uncertainty of oil and gas production caused by the change in parameter values during the process of petroleum exploitation and has far-reaching application significance.

scholarly journals Block-based cloud classification with statistical features and distribution of local texture features

2015 ◽  
Vol 8 (3) ◽  
pp. 1173-1182 ◽  
Author(s):  
H.-Y. Cheng ◽  
C.-C. Yu
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Texture Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Local Texture ◽  
Cloud Classification ◽  
Block Based ◽  
Combined Feature ◽  
Neighbor Classifier

Abstract. This work performs cloud classification on all-sky images. To deal with mixed cloud types in one image, we propose performing block division and block-based classification. In addition to classical statistical texture features, the proposed method incorporates local binary pattern, which extracts local texture features in the feature vector. The combined feature can effectively preserve global information as well as more discriminating local texture features of different cloud types. The experimental results have shown that applying the combined feature results in higher classification accuracy compared to using classical statistical texture features. In our experiments, it is also validated that using block-based classification outperforms classification on the entire images. Moreover, we report the classification accuracy using different classifiers including the k-nearest neighbor classifier, Bayesian classifier, and support vector machine.

scholarly journals Classification of Leukemia Image Using Genetic Based K-Nearest Neighbor (G-KNN)

2018 ◽  
Vol 7 (2) ◽  
pp. 113-117
Author(s):  
M. Bennet Rajesh ◽  
S. Sathiamoorthy
Keyword(s):  
Blood Cell ◽  
Nearest Neighbor ◽  
Median Filter ◽  
Diagnostic System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Initial Stage ◽  
Medical Diagnostic ◽  
Occurrence Matrix

In medical diagnostic system, classification of blood cell is more vigorous to identify the disease. The diseases which are connected with blood is alienated after the categorization of blood cell. Leukemia, a blood cancer that begins in bone marrow. Hence, it must be cured at initial stage and leads to death if left untreated. This paper introduces median filter for noise removing and Genetic based kNN for classification of Leukemia image datasets and features are extracted using gray-level co-occurrence matrix. The outcome of proposed genetic algorithm based kNN is compared with multilayer perceptron and support vector machine. The experimental outcomes evident that proposed combination performs better than the existing approach.

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