scholarly journals A Suggested System For Palmprint Recognition Using Curvelet Transform And Co-Occurrence Matrix.

2021 ◽  
Vol 30 (5) ◽  
pp. 65-76
Author(s):  
meaad alhadidi
Keyword(s):  
Curvelet Transform ◽  
Palmprint Recognition ◽  
Occurrence Matrix

Feature fusion of palmprint and face via tensor analysis and curvelet transform

2012 ◽  
Vol 20 (2) ◽  
Author(s):  
X. Xu ◽  
X. Guan ◽  
D. Zhang ◽  
X. Zhang ◽  
W. Deng ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Curvelet Transform ◽  
Image Features ◽  
Tensor Analysis ◽  
Small Samples ◽  
Palmprint Recognition ◽  
Biometric Recognition ◽  
Fusion Level ◽  
Knn Classifier ◽  
Multimodal Biometric Recognition

AbstractIn order to improve the recognition accuracy of the unimodal biometric system and to address the problem of the small samples recognition, a multimodal biometric recognition approach based on feature fusion level and curve tensor is proposed in this paper. The curve tensor approach is an extension of the tensor analysis method based on curvelet coefficients space. We use two kinds of biometrics: palmprint recognition and face recognition. All image features are extracted by using the curve tensor algorithm and then the normalized features are combined at the feature fusion level by using several fusion strategies. The k-nearest neighbour (KNN) classifier is used to determine the final biometric classification. The experimental results demonstrate that the proposed approach outperforms the unimodal solution and the proposed nearly Gaussian fusion (NGF) strategy has a better performance than other fusion rules.

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 9 (2S3) ◽  
pp. 590-592
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

scholarly journals Multi Object and Dynamic Query Based CBIR System using DCT Incorporated with HOG and HTF

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1358-1369
Keyword(s):  
Retrieval System ◽  
Similarity Index ◽  
Curvelet Transform ◽  
Texture Features ◽  
Gray Level ◽  
Query Image ◽  
User Expectations ◽  
Multi Level ◽  
Occurrence Matrix ◽  
Dynamic Query

This work contributes multi object detection and dynamic query image based retrieval system. Generally, finding relevance and matching user expectations is very critical based on query key information and these results irrelevant responses which will produce low similarity index. Consequently, CBIR system took a major responsibility of identifying new objects, retrieving similar objects or contents based on multi query and dynamic keywords with improved recall and precision as per requirement of the users. At this juncture, Discrete Curvelet Transform with the incorporation of HOG and HTF based approach is proposed to handle commercial image, medical images and types of multi model images. This proposed approach mainly focuses on extracting scaled features for finding correlation among the query and database images. To start with the process, query image is decomposed into multi level sub images to extract set of texture features at two levels. These features are estimated by Gray Level Co-occurrence Matrix (GLCM) and HOG descriptor based techniques is adapted to find scaled vectors with reduced dimensionality. This method outperform compared as compared to existing method is authenticated from experimental results.

scholarly journals Mammogram Classification using Curvelet Coefficients and Gray Level Co-Occurrence Matrix for Detection of Breast Cancer

2019 ◽  
Vol 8 (12) ◽  
pp. 4819-4824
Keyword(s):  
Breast Cancer ◽  
Curvelet Transform ◽  
Test Methods ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Energy Feature ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Computer-aided diagnosis system plays an important role in diagnosis and detection of breast cancer. In computer-aided diagnosis, feature extraction is one of the important steps. In this paper, we have proposed a method based on curvelet transform to classify mammogram images as normal -abnormal, benign and malignant. The feature vector is computed from the approximation coefficients. Directional energy is also calculated for all sub-bands. To select the efficient feature we used t-test and f-test methods. The selected feature is applied to Artificial Neural Network (ANN) classifier for classification. The effectiveness of the proposed method has been tested on MIAS database. The performance measures are computed with respect to normal vs. abnormal and benign vs. malignant for using approximation subband and energy feature of all curvelet coefficients. The highest classification accuracy of 95.34% is achieved for normal vs. abnormal and 80.86% is achieved for benign vs malignant class using energy feature of all curvelet coefficients.

scholarly journals Palmprint Recognition Based on Curvelet Transform Decision Fusion

2011 ◽  
Vol 23 ◽  
pp. 303-309 ◽  
Author(s):  
Wang Xinchun ◽  
Yue Kaihua ◽  
Liu Yuming ◽  
Ye Qing
Keyword(s):  
Curvelet Transform ◽  
Decision Fusion ◽  
Palmprint Recognition

An intelligent technique for detection of diabetic retinopathy using improved alexnet model based convoluitonal neural network

2020 ◽  
pp. 1-12
Author(s):  
S. Sadhana ◽  
R. Mallika
Keyword(s):  
Diabetic Retinopathy ◽  
Research Work ◽  
Curvelet Transform ◽  
Histogram Equalization ◽  
Model Based ◽  
Early Stages ◽  
Intelligent Technique ◽  
Specificity And Sensitivity ◽  
Equalization Technique ◽  
Occurrence Matrix

Blindness is one of the serious issues in the present medical world scenario mainly caused by Diabetic Retinopathy (DR). It is a diabetes complication, that is produced due to the problems in retina blood vessel. For clinical treatment, it will be extremely helpful, if diabetic retinopathy is detected in early stages. In recent years, the manual detection of DR consumes more time and moreover, the detection of DR in early stages is still a challenging task. In order to avoid these issues, this research work focus on an automated as well as effective solution for detecting DR symptoms from retinal images and requires less time for accurate detection. A Novel histogram equalization technique is used for performing contrast enhancement and equalization in initial pre-processing stage. Then, from these pre-processed images, image patches are extracted regularly. Improved Discrete Curvelet Transform based Grey Level Co-occurrence Matrix (IDCT-GLCM) is used in second stage for extracting features. Then, extracted features are given to Classifier. At last, an Improved Alexnet model-based CNN (IAM-CNN) classification approach is used for diagnosing DR from digital fundus images. In terms of accuracy, specificity and sensitivity, effectiveness and efficiency of proposed method is shown by extensive simulation results.

scholarly journals A Novel Framework for Detection of Cervical Cancer

2018 ◽  
Vol 7 (2) ◽  
pp. 26-30
Author(s):  
V. Pushpalatha
Keyword(s):  
Cervical Cancer ◽  
Support Vector Machine ◽  
Curvelet Transform ◽  
Uterine Cervical Cancer ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Adaptive Support ◽  
Occurrence Matrix ◽  
Five Phases

Today, Uterine Cervical Cancer is most general form of cancer for women. Prevention of cervical cancer is possible via various screening courses. Colposcopy images of cervix are analyzed in this study for the recognition of cervical cancer. An innovative framework is suggested to correctly identify cervical cancer by employing effective pre-processing, image enhancement, and image segmentation techniques. This framework comprises of five phases, (i) Dual tree discrete wavelet transform to pre-process the image (ii) Curvelet transform and contour transform to enhance the image (iii) K-means for segmentation (iv) features computation using Gray level co-occurrence matrix (v) classification using adaptive Support vector machine. The experimental results evident that proposed technique is superior to existing methodologies.

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