Automatic voice based disease detection method using one dimensional local binary pattern feature extraction network

ExHiF: Alzheimer's disease detection using exemplar histogram-based features with CT and MR images

Face recognition is efficiently used in computer vision, pattern recognition, biometric, law enforcement, surveillance, criminal investigations, and missing person detection. However, face recognition is still a challenging task under variant illuminations, pose, expression, occlusion, and background. In this paper, we proposed a cascading of histogram of oriented gradients (HOG) and local binary pattern (LBP) feature extraction method on ORL face dataset, which can improve recognition rates while also addressing pose, scale, expression, and variant illumination issues. The facial recognition network was trained using various classifiers such as with KNN, SVM, and random forest (RF). The results have been shown that the accuracy rate of the combined LBP and HOG feature extraction method is better than individual LBP or HOG features for ORL database face recognition.

A novel octopus based Parkinson’s disease and gender recognition method using vowels

A novel ensemble local graph structure based feature extraction network for EEG signal analysis

This work has the objective of developing a methodology for the classification of regions of interest (ROI) extracted from mammograms into masses and non-masses. To this end, we conducted a comparative study by combining several techniques in the various stages of the problem. We compared several texture-based techniques for the classification of ROIs. This study combines image processing, phylogenetic trees (PT), local binary patterns (LBP), support vector machines (SVM) and the micro-genetic algorithm (GA). The analysis of texture is performed, either through the combination PT/LBP or with grey levels, to compute the taxonomic diversity () and taxonomic distinction (*) indexes extracted from sub-regions (circular, circular crown, internal mask, external mask and the combination of internal and external mask) of an ROI. A GA is used to estimate the best phylogenetic weights. Then, its results are compared with the results achieved with the use of PT only. We also analyse the behaviour of the methodology when using the ROIs with and without enhancement. This enhancement consists of the application of a mean filter and the contrast-limited adaptive histogram equalisation (CLAHE). PTs and SVM were used to perform the selection of features. To evaluate the performance of the methodologies under analysis, we used the following metrics: sensitivity, specificity, accuracy and area under the receiver operating characteristic (ROC) curve (). Sensitivity and specificity measure the efficiency of the classifier at the correct detection of positive (masses) and negative (non-masses) cases, respectively. Accuracy measures the performance of the classification in both cases. The ROC curve is the graphical representation of the pairs (1-specificity, sensitivity). is the area formed by the ROC curve, which equals to 1 in an ideal test. The comparison of the possible combinations for each stage of the study revealed the following results. In the analyses without feature selection, the best results were (1) 100% accuracy and of 0.99 for the combination of PT, LBP and internal masks and (2) 99.5% accuracy and of 0.99 for the combination of GA, LBP and internal masks for the extraction of features. In the analyses with feature selection, the best results were (1) 100% accuracy and of 1.0 for the combination of GA and LBP (feature extraction) with the union of internal and external masks and (2) 98.5% accuracy and of 0.99 for the combination of PT, LBP and the union of internal and external masks.

This article presents an intelligent automatic approach for galaxies images classification based on Artificial Neural Network (ANN) and moment-based features extraction algorithms. The proposed approach consists of three phases; namely, image denoising, feature extraction, and classification phases. For the denoising phase, noise pixels are removed from input images, then input galaxy image is normalized to a uniform scale and Hu seven invariant moment algorithm is applied to reduce the dimensionality of the feature space during the feature extraction phase. Finally, during the classification phase, Self-Organize Feature Maps (SOFMs) and Time Lag Recurrent Networks (TLRNs) algorithms are utilized for classifying the input galaxies images into one of four obtained source catalogue types. Experimental results showed that SOFMs provided better classification results than having TLRNs applied. It is also concluded that a small set of features is sufficient to classify galaxy images and provide a fast classification.

An automated faults classification method based on binary pattern and neighborhood component analysis using induction motor

Congestive heart failure detection using random forest classifier

Palm vein authentication is among the recent research in the field of access control applications. Because palm veins are tough to forge, they act as a reliable metric in security applications. Accurate extraction of palm veins is challenging in the presence of various dynamics, such as variant light conditions, variations in palm vein patterns from person to person, the cleanliness of the hand, etc. This paper presents a robust recognition process that makes use of a ridge filter for vein pattern extraction, and local binary patterns (LBP) for feature extraction. The ridge filter takes the major eigenvalue of the Hessian matrix, which contains the second-order derivative of the image pixels. The eigenvalue is then processed using LBP feature extraction from the vein patterns. Finally, a support vector machine is used for classification of subsequent images. The result shows that the system can provide accuracy of 89%, with a computation time of 0.423s. The false acceptance rate and false rejection rate were also evaluated as benchmark parameters, which show significantly good performance.

Image hashing schemes have been widely used in content authentication, image retrieval, and digital forensic. In this paper, a novel image hashing algorithm (SSL) by incorporating the most stable keypoints and local region features is proposed, which is robust against various content-preserving manipulations, even multiple combinatorial manipulations. The proposed algorithm combines S_cale invariant feature transform (SIFT) with S_aliency detection to extract the most stable keypoints. Then, the L_ocal binary pattern (LBP) feature extraction method is exploited to generate local region features based on these keypoints. After that, the information of keypoints and local region features are merged into a hash vector. Finally, a secret key is used to randomize the hash vector, which can prevent attackers from forging the image and the hash value. Experimental results demonstrate that the proposed hashing algorithm can identify visually similar images which are under both single and combinatorial content-preserving manipulations, even multiple combinations of manipulations. It can also identify maliciously forged images which are under various content-changing manipulations. The collision probability between hashes of different images is nearly zero. Besides, the evaluation of key-dependent security shows that the proposed scheme is secure that an attacker cannot forge or estimate the correct hash value without the knowledge of the secret key.

In the member of mud crab, species of Scylla are the most traded seafood commodity in Asia and the culturing practice is already done in most of Asia country a few years ago. The demand for mud crabs has increased rapidly over the last decade, providing great potential for the development of the mud crab aquaculture industry. But, there is still unsolved problem where limitation of knowledge in identification is limited due to similar colours and feature characteristic. Thus, this study proposed an automatic technique that can evaluate and produce the subset of mud crab genus Scylla features by using Local Binary Pattern (LBP) as a feature extraction tool. The main objective of the study is to find the optimal subset of mud crab genus Scylla features from a carapace images dataset. Based on 153 extracted features chosen by LBP features selection methods, the accuracy rates of three classification algorithms were obtained for analysis. The results from the MatLab experiment demonstrated that, the LBP method produced an accuracy under 60% for entire classifier.

Because it is so difficult to distinguish handwritten digits, digit identification is one of the most critical applications in computer vision. This is one of the reasons why it is so tough. The field of handwritten character recognition is one in which a great deal of application of numerous deep learning models has occurred. The startling parallels that can be drawn between deep learning and the brain are primarily responsible for its meteoric rise in popularity. In this study, the Artificial Neural Network and the Convolutional Neural Network, two of the most used Deep Learning algorithms, were investigated with an eye toward the recognition process's feature extraction and classification phases. With the assistance of the categorical cross-entropy loss and the ADAM optimizer, the models were trained on the MNIST dataset. Backpropagation and gradient descent are the two methods utilized during the training process of neural networks that contain reLU activations and carry out automatic feature extraction. In computer vision, one of the most common and widely used classifiers is the Convolution Neural Network, sometimes referred to as ConvNets or Convolutional neural networks. This network is used for the recognition and categorization of images.

Automated knee ligament injuries classification method based on exemplar pyramid local binary pattern feature extraction and hybrid iterative feature selection

Palm tree detection using remote sensing images has received increasing attention in recent years, concerning the issues of sustainability, productivity and profitability. There has been significant progress in the research of using machine learning techniques, especially convolutional neural networks (CNNs) for automatic palm tree detection. However, whether CNNs can actually outperform traditional human-engineered approaches in terms of classification accuracy and detection speed is yet unknown. In the present study, we have compared human-engineered features namely histogram of oriented gradients (HOG), local binary pattern (LBP) and scale-invariant feature transform (SIFT) with features extracted using pre-trained AlexNet model for detecting palm trees in high resolution images obtained via unmanned aerial vehicle (UAV). Support vector machines (SVM) with linear and non-linear kernels were used to classify feature vectors obtained by different feature extractors. Haar-like features used in Viola-Jones framework was also tested in the study. Results showed that features extracted from the fifth convolutional layer of AlexNet achieved the highest accuracy of 96.1% using SVM with RBF kernel as classifier, which surpassed the accuracy obtained by fully-connected CNN, namely 95.6%. The results also suggest that SVM classifier with radial basis function (RBF) kernel using LBP as features is the optimal combination as it achieved comparable accuracy but higher detection speed than CNN approaches.

Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are good descriptor for brain MR image retrieval. But there are many challenges facing in medical application. An empirical study of the impact of increasing bins number in the HOG descriptor concluded that larger the number is more accurate the descriptor is. In fact this is due to the reduction of orientations range that each bin covers. Despite the efficiency of augmenting the bins number, this technique has limited spatial support as the augmentation of the number of bins used leads to increase the histogram dimension. So here proposed a method called Histogram of Fuzzy Oriented Gradients (HFOG), in which a pixel can belong several bins with different degrees. The Local Binary Patterns feature extraction method is widely used for texture analysis; however, the original LBP is based on hard thresholding the neighborhood of each pixel. Therefore, texture representation with LBP is very sensitive to noise and cannot distinguish between a strong and a weak pattern. In this study, Fuzzy Local Binary Patterns was introduced to improve the original LBP.