Image Denoising Algorithm Considering Nonlocal Texture Pattern

Image denoising is indispensable for image processing.In this paper,image denoising algorithm based on Nonlocal Means(NLM)filter is proposed.Recently,abundant enhancements based on NLM filter have been performed.However,the performance of NLM filter is still inferior to that of other image processing approaches such as K-SVD.In this paper,NLM algorithm with weight refinement is utilized for image denoising.Weight refinement is performed to thoroughly take advantage of self-similarity of the image.Experimental results show good performance of the proposed method.

Local Binary Patterns and Its Variants for Finger Knuckle Print Recognition in Multi-Resolution Domain

Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.

Web Page Recommendation Using Distributional Recurrent Neural Network

In the data retrieval process of the Data recommendation system,the matching prediction and similarity identification take place a major role in the ontology.In that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching time.Since,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation process.To improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data processing.In this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire database.The overall work was implemented for the application of the data recommendation process.These are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching period.Also,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query data.This was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature set.The training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the dataset.These are formed as clusters and paged with proper indexing based on the MPS parameter of similarity metric.The overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision,Recall,F1-score and the accuracy of data retrieval,the query recommendation output,and comparison with other state-of-art methods.

A retinal blood vessel extraction algorithm based on CART decision tree and improved AdaBoost

This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting,reversing,dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes,where CART binary tree is as the AdaBoost weak classifier,and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels,and the result basically contains complete blood vessel details. Moreover,the segmented blood vessel tree has good connectivity,which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm,the proposed algorithm has higher average accuracy and reliability index,which is similar to the segmentation results of the state-of-the-art segmentation algorithm.

Deep learning of DEM image texture for landform classification in the Shandong area,China

Landforms are an important element of natural geographical environment,and textures are the research basis for the spatial differentiation,evolution features,and analysis rules of the landform.Using the regional difference of texture to describe the spatial distribution pattern of macro landform features is helpful to the landform classification and identification.Digital elevation model(DEM)image texture,which gives full expression to texture difference,is key data source to reflect the surface features and landform classification.Following the texture analysis,landform features analysis is assistant to different landforms classification,even in landform boundary.With the increasing accuracy requirement of landform information acquisition in geomorphic thematic mapping,hierarchical landform classification has become the focus and difficulty in research.Recently,the pattern recognition represented by Convolutional Neural Network has made great achievements in landform research,whose multichannel feature fusion structure satisfies the network structure of different landform classification.In this paper,DEM image texture was taken as the data source,and gray level co-occurrence matrix was applied to extract texture measures.Owing to the similarity of similar landform and the difference of different landform in a certain scale,a comprehensive texture factor reflecting landform features was proposed,and the spatial distribution pattern of landform features was systematically analyzed.On this basis,the coupling relationship between texture and landform type was explored.Thus,the deep learning method of Convolutional Neural Network is used to train the texture features,and the second-class landform classification is carried out through softmax.The classification results in small relief and mid-relief low mountains,overall accuracy are 84.35%and 69.95%respectively,while kappa coefficient are 0.72 and 0.40 respectively,were compared to that of traditional unsupervised landform classification results,and the superiority of Convolutional Neural Network classification was verified,it approximately improved 6%in overall accuracy and 0.4 in kappa coefficient.