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Text detection is a difficult task due to the significant diversity of the texts appearing in natural scene images. In this paper, we propose a novel text descriptor, SPP-net, extracted by equipping the Convolutional Neural Network (CNN) with spatial pyramid pooling. We first compute the feature maps from the original text lines without any cropping or warping, and then generate the fixed-size representations...
Automated classification of HEp-2 cell images is crucial for fast and accurate detection of autoimmune diseases. Recent competitions resulted in high classification rates on publicly available datasets. However, performance on low-resolution HEp-2 images typically lagged behind that of high-resolution images due to the blurring and sub-sampling of fine cellular details. Direct interpolation of low-resolution...
While data-dependent dimensionality reduction has dominated in many applications of hyperspectral imagery, there is increasing interest in data-independent strategies—such as random projections—due to their promise for reduced computational complexity as well as their demonstrated ability to preserve application-important information. Such random-projection-based dimensionality reduction is investigated...
Electroencephalographic (EEG)-based emotion recognition has attracted increasing attention from the field of human-computer interaction (HCI). However, there are a number of challenges for machines to correctly recognize human emotional states. One problem is how to generalize the emotion model across time, since the brain may show different patterns of EEG for the same emotion experience at different...
Movement classification from electromyography (EMG) signals is a promising vector for improvement of human computer interaction and prosthetic control. Conventional work in this area typically makes use of expert knowledge to select a set of movements a priori and then design classifiers based around these movements. The disadvantage of this approach is that different individuals might have different...
In this paper we describe a simple and very fast method of data acquisition, feature extraction and feature space creation for epileptic seizure detection. The scalp electroencephalogram (EEG) dataset [1, 2] collected at the Children's Hospital Boston from 22 pediatric patients having 192 intractable seizures (available as CHB-MIT database) is used to assess this simple approach against existing ones...
The rapid growth of web source has changed language learning behavior. More and more people utilized web sources instead of paper books. However, the problem now is that it is overwhelming to find useful information. In addition, when considering using different words, good example sentences demonstrating nuance among words are extremely helpful but learners can hardly find them as most web dictionaries...
Traditional distinction and recognition methods of electromagnetic radiation source have some shortcomings with highly error and long time. Therefore, Wilson algorithm, Lee algorithm and near field wave impedance theory have been proposed to analyze radiation source characteristics. And it presented common-mode radiation noise testing method of poor-average algorithm and differential mode noise testing...
According to the operation of the automaton transient impact, nonlinear, non-stationary signal, a method which is based on the time-frequency characteristics and PCA-SVM automaton fault diagnosis is proposed. Firstly, this paper uses statistical analysis and overall empirical mode decomposition method to construct high dimensional mixed domain initial feature vector from the characteristics of different...
This paper presents Indonesian text emotion detection and evaluates the performances of four different classification methods: Naive Bayes (NB), J48, K-Nearest Neighbor (KNN) and Support Vector Machine-Sequential Minimal Optimization (SVM-SMO). The experiment uses Indonesian text corpus, containing 1000 sentences which consists of six emotion classes: anger, disgust, fear, joy, sadness, and surprise...
Text classification deals with allocating a text document to a predetermined class. Generally, this involves learning about a class from representations of documents belonging to that class. In this paper, we propose a classifier combination that uses a Multinomial Naïve Bayesian (MNB) classifier along with Bayesian Networks (BN) classifier. The results of two classifiers are combined by taking an...
In this work, the core objective is to implement an automatic and reliable system for the classification of plants into two plant categories named as monocotyledonous and dicotyledonous using the microscopic images of plant stem cross sections. The system can be used for the classification of plants when a large number of new plant species are discovered and it can be applied in plant disease detection...
We propose in this paper a new writer-independent off-line handwritten signature verification (HSV) system using only genuine signatures. This system is based on a combination of two off-line individual HSV systems through the plausible and paradoxical reasoning theory of Dezert-Smarandache (DSmT). Firstly, we propose to evaluate the performances of both off-line HSV systems through using one-class...
Label-deficient semi-supervised learning is a challenging setting in which there is an abundance of unlabeled data but a dearth of labeled data. We propose a method for applying Gaussian process latent variable models (GPLVM) in a label-deficient setting, a method in which the discriminative GPLVM objective function trains a back-constraining neural network followed by a transformation into a semi-supervised...
Support vector regression (SVR) has become one of the most promising methods for function approximation and regression estimation. However, SVR has a time complexity of O(N3) and a space complexity of O(N2). When dealing with very large sizes of training sets, SVR takes a lot computational time. To solve this problem, a method called heuristic sample reduction (HSR) is proposed for obtaining a reduced...
This paper addresses the problem of hyperspectral image classification with the low-rank representation (LRR) which has been widely applied in computer vision and pattern recognition. As is known, it has been proved to be effective in subspace segmentation under the assumption that all the subspaces are mutually independent. Nevertheless, in practical applications, this assumption could hardly be...
Support vector machines (SVM), originally introduced as powerful binary classifiers, can also be used for multi-class recognition with the help of creative meta-learning strategies such as commonly used one-vs-rest, one-vs-one and majority voting. In this paper, we explore the potential of creating informed nested dichotomies based on clustering pseudo-labels and probability estimates generated a...
In this paper a new vulnerability detecting method is proposed to detect buffer boundary violations. The main idea is to use the metric of array index manipulation rather than using any heuristic method. We employ a SVM-based classifier to classify the vulnerable functions and innocent functions. Then the vulnerable functions are fed to function call graph guided symbolic execution to precisely determine...
With increasing complexity of today's automotive combustion engines, end-of-line (EOL) testing has become an important method to test assembled engines for production faults. Several hundered measurement signals are evaluated for every EOL test, up to 100% of production volume. The difficulty of finding and maintaining accurate test limits makes EOL testing of complex products interesting as a machine...
Efforts have been made in financial markets to deal with price movement predicting. Recent studies have shown that the market can be outperformed by methodologies with the aid of science. In other words, it has been shown that methods based on computational intelligence can be more profitable than a buy-and-hold strategy. This paper proposes a probabilistic and dynamic chart pattern recognition hybrid...
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