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This paper presents the design of a convolutional neural network architecture using the MatConvNet library for MATLAB in order to achieve the recognition of 2 classes of hand gestures: ”open” and ”closed”. Six architectures were implemented to which their hyperparameters and depth were varied to observe their behavior through the validation error in the training and accuracy in the estimation of each...
This research proposes a reliable machine learning based computational solution for human detection. The proposed model is specifically applicable for illumination-variant natural scenes in big data video frames. In order to solve the illumination variation problem, a new feature set is formed by extracting features using histogram of gradients (HoG) and linear phase quantization (LPQ) techniques,...
Most of the intrusion detection systems analyze all network traffic features to identify intrusions with different classification techniques. Any intrusion detection model developed has to provide maximum accuracy with minimal false alarms. Identifying the optimal feature subset for classification is an important task for improved classification. In this paper, consistency based feature selection...
Single-ISA heterogeneous multicore processors have gained increasing popularity with the introduction of recent technologies such as ARM big.LITTLE. These processors offer increased energy efficiency through combining low power in-order cores with high performance out-of-order cores. Efficiently exploiting this attractive feature requires careful management so as to meet the demands of targeted applications...
Aiming at the characteristics of SIFT (Scale Invariant Feature Transform) algorithm which has large amount of calculation and can be highly paralleled, we propose an optimized FPGA implementation so that it can be accelerated on hardware. In this method, we firstly simplify the process of filtering image and generating Gaussian pyramids through selecting appropriate parameters and hardware structure,...
This paper reviews the comparative performance of Support Vector Machine (SVM) using four different kernels, i.e., Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid. Overall accuracy (OA), Kappa Index Analysis (KIA), Receiver Operating Characteristic (ROC) and Precision (P) have been considered as evaluation parameters in order to assess the predictive accuracy of SVM. Both high resolution...
In this paper we consider sources of enhancement for the Smoothed Particle Hydrodynamics method in approximating a function and its derivatives. It is well known that the standard formulation is usually poor when scattered data distribution is considered or when the approximation near the boundary occurs. In this paper studies on the accuracy are provided and assessed with gridded and scattered data...
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one...
For hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted feature extraction method (KNWFE) have been shown that...
We propose a superpixel-based composite kernel framework for hyperspectral image (HSI) classification. Composite kernel methods can utilize both the spectral and the spatial information for the HSI classification. However, setting the optimal spatial neighborhood for different spatial structures is a non-trivial issue. In order to adaptively exploit the spatial contextual information, we utilize superpixel...
Kernel methods constitute a family of powerful machine learning algorithms, which have found wide use in remote sensing and geosciences. However, kernel methods are still not widely adopted because of the high computational cost when dealing with large scale problems, such as the inversion of radiative transfer models. This paper introduces the method of random kitchen sinks (RKS) for fast statistical...
Hyperspectral image processing has been a very dynamic area in remote sensing and other applications since last decades. Hyperspectral images provide abundant spectral information to identify and distinguish spectrally similar materials. Recent advances in kernel machines promote the novel use of Gaussian processes (GP) for classifying hyper-spectral images. Many sophisticated kernel functions have...
Big Data Analytics methods take advantage of techniques from the fields of data mining, machine learning, or statistics with a focus on analysing large quantities of data (aka ‘big datasets’) with modern technologies. Big data sets appear in remote sensing in the sense of large volumes, but also in the sense of an ever increasing amount of spectral bands (i.e., high-dimensional data). The remote sensing...
The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for urban land cover in Wuhan, Hubei province of China. Firstly, radiation rectification, normalization processing and geometry registration are made between the bi-temporal images. Secondly, SVM approach is used in...
Flood is the most frequent disaster in the world, which can do harm to agriculture and threat to food security. Using kernel based supervised classifier to execute change detection for multi-temporal remote sensing data is a common method for flood disaster monitoring and assessment, and kernel Fisher's discrimination analysis (KFDA) is one of them. Choosing training sample by visual interpretation...
Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification...
Multiple kernel learning (MKL) combines multiple base kernels and is becoming more and more popular in machine learning. The choice of kernels is crucial importance for classification performance. In this paper, we propose a new RMKL (RMKBoost) framework for classification in hyperspectral images. The classification is performed in separate two steps. The key boosting strategy is embedded in the first...
The Spatial Pyramid Matching approach has become very popular to model images as sets of local bag-of-words. The image comparison is then done region-by-region with an intersection kernel. Despite its success, this model presents some limitations: the grid partitioning is predefined and identical for all images and the matching is sensitive to intra- and inter-class variations. In this paper, we propose...
It is estimated that 80% of crashes and 65% of near collisions involved drivers inattentive to traffic for three seconds before the event. This paper develops an algorithm for extracting characteristics allowing the cell phones identification used during driving a vehicle. Experiments were performed on sets of images with 100 positive images (with phone) and the other 100 negative images (no phone),...
Texture classification, texture synthesis, or similar tasks are an active topic in computer vision and pattern recognition. This paper aims to present a novel texture dissimilarity measure based on textons, namely the Local Texton Dissimilarity (LTD), inspired from (Dinu et al., 2012). Textons are represented as a set of features extracted from image patches. The proposed dissimilarity measure shows...
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