Search results for "Feature extraction"
showing 10 items of 275 documents
Early detection and classification of bearing faults using support vector machine algorithm
2017
Bearings are one of the most critical elements in rotating machinery systems. Bearing faults are the main reason for failures in electrical motors and generators. Therefore, early bearing fault detection is very important to prevent critical system failures in the industry. In this paper, the support vector machine algorithm is used for early detection and classification of bearing faults. Both time and frequency domain features are used for training the support vector machine learning algorithm. The trained classier can be employed for real-time bearing fault detection and classification. By using the proposed method, the bearing faults can be detected at early stages, and the machine oper…
Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis
2015
In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…
Predicting year of plantation with hyperspectral and lidar data
2017
This paper introduces a methodology for predicting the year of plantation (YOP) from remote sensing data. The application has important implications in forestry management and inventorying. We exploit hyperspectral and LiDAR data in combination with state-of-the-art machine learning classifiers. In particular, we present a complete processing chain to extract spectral, textural and morphological features from both sensory data. Features are then combined and fed a Gaussian Process Classifier (GPC) trained to predict YOP in a forest area in North Carolina (US). The GPC algorithm provides accurate YOP estimates, reports spatially explicit maps and associated confidence maps, and provides sens…
Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs…
2020
Abstract Image processing and artificial intelligence (AI) techniques have been applied to analyze, evaluate and classify mulberry fruit according to their ripeness (unripe, ripe, and overripe). A total of 577 mulberries were graded by an expert and the images were captured by an imaging system. Then, the geometrical properties, color, and texture characteristics of each segmented mulberry was extracted using two feature reduction methods: Correlation-based Feature Selection subset (CFS) and Consistency subset (CONS). Artificial Neural Networks (ANN) and Support Vector Machine (SVM) were applied to classify mulberry fruit. ANN classification with the CFS subset feature extraction method res…
A nondestructive intelligent approach to real‐time evaluation of chicken meat freshness based on computer vision technique
2019
In this study, the capability of a procedure based on combination of computer vision (CV) and artificial intelligence techniques examined for intelligent and nondestructive prediction of chicken meat freshness during the spoilage process at 4°C. The proposed system comprises the following stages: capture images, image preprocessing, image processing, computing channels, feature extraction, feature selection by a hybrid of genetic algorithm (GA) and artificial neuronal network (ANN), and prediction by using ANN. The number of neurons in input layer was determined 33 (selected features) and freshness used as the output. The ideal ANN model was obtained with 33‐10‐1 topology. The high performa…
ES1D: A Deep Network for EEG-Based Subject Identification
2017
Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…
Deep Learning Architectures for DNA Sequence Classification
2017
DNA sequence classification is a key task in a generic computational framework for biomedical data analysis, and in recent years several machine learning technique have been adopted to successful accomplish with this task. Anyway, the main difficulty behind the problem remains the feature selection process. Sequences do not have explicit features, and the commonly used representations introduce the main drawback of the high dimensionality. For sure, machine learning method devoted to supervised classification tasks are strongly dependent on the feature extraction step, and in order to build a good representation it is necessary to recognize and measure meaningful details of the items to cla…
Automatic monitoring system for the detection and evaluation of the evolution of hemangiomas
2016
In this paper we introduce an automatic monitoring system for the detection and the evaluation of the evolution of hemangiomas using a fuzzy logic system based on two parameters: area and redness. We have considered pairs of images (from two different moments in time) that show hemangiomas either evolving, stationary or regressing. The starting points of the algorithm are the rectangular regions of interest (ROI), manually selected for each of the two images, and automatically segmented using Fuzzy C-means. Using the area and the redness of the hemagiomas extracted with Fuzzy C-means, for the same patient, at different moments of time, the algorithm decides whether the hemangioma is evolvin…
Luminance Information Is Required for the Accurate Estimation of Contrast in Rapidly Changing Visual Contexts.
2020
Summary Visual perception scales with changes in the visual stimulus, or contrast, irrespective of background illumination. However, visual perception is challenged when adaptation is not fast enough to deal with sudden declines in overall illumination, for example, when gaze follows a moving object from bright sunlight into a shaded area. Here, we show that the visual system of the fly employs a solution by propagating a corrective luminance-sensitive signal. We use in vivo 2-photon imaging and behavioral analyses to demonstrate that distinct OFF-pathway inputs encode contrast and luminance. Predictions of contrast-sensitive neuronal responses show that contrast information alone cannot ex…
Full-automatic computer aided system for stem cell clustering using content-based microscopic image analysis
2017
Abstract Stem cells are very original cells that can differentiate into other cells, tissues and organs, which play a very important role in biomedical treatments. Because of the importance of stem cells, in this paper we propose a full-automatic computer aided clustering system to assist scientists to explore potential co-occurrence relations between the cell differentiation and their morphological information in phenotype. In this proposed system, a multi-stage Content-based Microscopic Image Analysis (CBMIA) framework is applied, including image segmentation, feature extraction, feature selection, feature fusion and clustering techniques. First, an Improved Supervised Normalized Cuts (IS…