Evaluation of metal objects surface parameters informativity using 2D­ and 3D­data For classification of fractures

This article describes evaluation the information content of metal objects surfaces for classification of fractures using 2D and 3D data. As parameters, the textural characteristics of Haralick, local binary patterns of pixels for 2D images, macrogeometric descriptors of metal objects digitized by a 3D scanner are considered. The analysis carried out on basis of information content estimation to select the features that are most suitable for solving the problem of metals fractures classification. The results will be used for development of methods for complex forensic examination of complex polygonal surfaces of solid objects for automated system for analyzing digital images.

Predicting the Particle Size Distributions of Spherical Particle Sets from Synthetic Images: A Comparison of 9 Classic Image Features

The two objectives of this paper were to demonstrate use the of the discrete element method for generating synthetic images of spherical particle configurations, and to compare the performance of 9 classic feature extraction methods for predicting the particle size distributions (PSD) from these images. The discrete element code YADE was used to generate synthetic images of granular materials to build the dataset. Nine feature extraction methods were compared: Haralick features, Histograms of Oriented Gradients, Entropy, Local Binary Patterns, Local Configuration Pattern, Complete Local Binary Patterns, the Fast Fourier transform, Gabor filters, and Discrete Haar Wavelets. The feature extraction methods were used to generate the inputs of neural networks to predict the PSD. The results show that feature extraction methods can predict the percentage passing with a root-mean-square error (RMSE) on the percentage passing as low as 1.7%. CLBP showed the best result for all particle sizes with a RMSE of 3.8 %. Better RMSE were obtained for the finest sieve (2.1%) compared to coarsest sieve (5.2%).

Marked and unmarked speed bump detection for autonomous vehicles using stereo vision

A current challenge for autonomous vehicles is the detection of irregularities on road surfaces in order to prevent accidents; in particular, speed bump detection is an important task for safe and comfortable autonomous navigation. There are some techniques that have achieved acceptable speed bump detection under optimal road surface conditions, especially when signs are well-marked. However, in developing countries it is very common to find unmarked speed bumps and existing techniques fail. In this paper a methodology to detect both marked and unmarked speed bumps is proposed, for clearly painted speed bumps we apply local binary patterns technique to extract features from an image dataset. For unmarked speed bump detection, we apply stereo vision where point clouds obtained by the 3D reconstruction are converted to triangular meshes by applying Delaunay triangulation. A selection and extraction of the most relevant features is made to speed bump elevation on surfaces meshes. Results obtained have an important contribution and improve some of the existing techniques since the reconstruction of three-dimensional meshes provides relevant information for the detection of speed bumps by elevations on surfaces even though they are not marked.

A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging

Glaucoma is one of the eye diseases stimulated by the fluid pressure that increases in the eyes, damaging the optic nerves and causing partial or complete vision loss. As Glaucoma appears in later stages and it is a slow disease, detailed screening and detection of the retinal images is required to avoid vision forfeiture. This study aims to detect glaucoma at early stages with the help of deep learning-based feature extraction. Retinal fundus images are utilized for the training and testing of our proposed model. In the first step, images are pre-processed, before the region of interest (ROI) is extracted employing segmentation. Then, features of the optic disc (OD) are extracted from the images containing optic cup (OC) utilizing the hybrid features descriptors, i.e., convolutional neural network (CNN), local binary patterns (LBP), histogram of oriented gradients (HOG), and speeded up robust features (SURF). Moreover, low-level features are extracted using HOG, whereas texture features are extracted using the LBP and SURF descriptors. Furthermore, high-level features are computed using CNN. Additionally, we have employed a feature selection and ranking technique, i.e., the MR-MR method, to select the most representative features. In the end, multi-class classifiers, i.e., support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), are employed for the classification of fundus images as healthy or diseased. To assess the performance of the proposed system, various experiments have been performed using combinations of the aforementioned algorithms that show the proposed model based on the RF algorithm with HOG, CNN, LBP, and SURF feature descriptors, providing <=99% accuracy on benchmark datasets and 98.8% on k-fold cross-validation for the early detection of glaucoma.

Radiomics Analysis Derived From LGE-MRI Predict Sudden Cardiac Death in Participants With Hypertrophic Cardiomyopathy

Objectives: To identify significant radiomics features derived from late gadolinium enhancement (LGE) images in participants with hypertrophic cardiomyopathy (HCM) and assess their prognostic value in predicting sudden cardiac death (SCD) endpoint.Method: The 157 radiomic features of 379 sequential participants with HCM who underwent cardiovascular magnetic resonance imaging (MRI) were extracted. CoxNet (Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net) and Random Forest models were applied to optimize feature selection for the SCD risk prediction and cross-validation was performed.Results: During a median follow-up of 29 months (interquartile range, 20–42 months), 27 participants with HCM experienced SCD events. Cox analysis revealed that two selected features, local binary patterns (LBP) (19) (hazard ratio (HR), 1.028, 95% CI: 1.032–1.134; P = 0.001) and Moment (1) (HR, 1.212, 95%CI: 1.032–1.423; P = 0.02) provided significant prognostic value to predict the SCD endpoints after adjustment for the clinical risk predictors and late gadolinium enhancement. Furthermore, the univariately significant risk predictor was improved by the addition of the selected radiomics features, LBP (19) and Moment (1), to predict SCD events (P &lt; 0.05).Conclusion: The radiomics features of LBP (19) and Moment (1) extracted from LGE images, reflecting scar heterogeneity, have independent prognostic value in identifying high SCD risk patients with HCM.

Local information pattern descriptor for corneal diseases diagnosis

<span lang="EN-US">Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting.</span>

Adaptive Decision Support System for On-Line Multi-Class Learning and Object Detection

A new online multi-class learning algorithm is proposed with three main characteristics. First, in order to make the feature pool fitter for the pattern pool, the adaptive feature pool is proposed to dynamically combine the three general features, Haar-like, Histogram of Oriented Gradient (HOG), and Local Binary Patterns (LBP). Second, the external model is integrated into the proposed model without re-training to enhance the efficacy of the model. Third, a new multi-class learning and updating mechanism are proposed that help to find unsuitable decisions and adjust them automatically. The performance of the proposed model is validated with multi-class detection and online learning system. The proposed model achieves a better score than other non-deep learning algorithms used in public pedestrian and multi-class databases. The multi-class databases contain data for pedestrians, faces, vehicles, motorcycles, bicycles, and aircraft.

PyTorch Operations Based Approach for Computing Local Binary Patterns

Advances in machine learning frameworks like PyTorch provides users with various machine learning algorithms together with general purpose operations. PyTorch framework provides Numpy like functions and makes it practical to use computational resources for accelerating computations. Also users may define their custom layers or operations for feature extraction algorithms based on the tensor operations. In this paper, Local Binary Patterns (LBP) which is one of the important feature extraction approaches in computer vision were realized using tensor operations of PyTorch framework. The algorithm was written both using Python code with standard libraries and tensor operations of PyTorch in Python. According to experimental measurements which were realized for various batches of images, the algorithm based on tensor operations considerably reduced the computation time and provides significant accelerations over Python implementation with standard libraries.

Refined Color Texture Classification Using CNN and Local Binary Pattern

Representation and classification of color texture generate considerable interest within the field of computer vision. Texture classification is a difficult task that assigns unlabeled images or textures to the correct labeled class. Some key factors such as scaling and viewpoint variations and illumination changes make this task challenging. In this paper, we present a new feature extraction technique for color texture classification and recognition. The presented approach aggregates the features extracted from local binary patterns (LBP) and convolution neural network (CNN) to provide discriminatory information, leading to better texture classification results. Almost all of the CNN model cases classify images based on global features that describe the image as a whole to generalize the entire object. LBP classifies images based on local features that describe the image’s key points (image patches). Our analysis shows that using LBP improves the classification task when compared to using CNN only. We test the proposed approach experimentally over three challenging color image datasets (ALOT, CBT, and Outex). The results demonstrated that our approach improved up to 25% in the classification accuracy over the traditional CNN models. We identify optimal combinations for each dataset and obtain high classification rates. The proposed approach is robust, stable, and discriminatory among the three datasets and has shown enhancement in classification and recognition compared to the state-of-the-art method.