The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major step in developing Brain Computer Interfaces (BCIs). Features from a small spatial region are approximated by a sparse linear combination of few atoms from a multi-class dictionary constructed from the features of the electroencephalography (EEG) training signals for each class...
Wild animal detection is an active research area since last many decades among wildlife researchers to study and analyze wild animals and their behavior. This paper presents sparse representation based wild animal detection system using Discriminative Feature-oriented Dictionary Learning (DFDL). DFDL extracts discriminative class-specific features and shows a low complexity method for animal detection...
Traditional block compressed sensing (BCS) of images uses the same measurement rate to measure each block, while different image blocks contain different structural features, the number of measurements needs varies as well. In this paper, a V-AMRS (Adaptive Measurement Rate Setting Method Based on Variance of Classified Image Blocks) method is proposed. According to the variances, the image blocks...
The primary objective of this paper is to explore the applicability of sparse representation based classification (SRC), particularly at the fingerprint recognition problem. This paper proposes sparse proximity based fingerprint matching methodology. The sparse representation based classification problem can be solved as representing the test sample in terms of training set with some sparse residual...
In recent years, SRC has received many attentions for classification and identification tasks. This paper attempts to introduce a sparse representation based classification of EEG signal features and identification of associated activities or tasks. It uses wavelet and ICA processing of EEG signal for feature selection and dictionary training. Multiple dictionaries are trained and used for EEG signal...
Hyper-Spectral Images (HSI) have high dimensional data and low number of training samples. Hence classification of these images is an ill posed problem. Existence of inescapable noise makes it more difficult to distinguish between members of each classes. To overcome this problem extracting both spectral and spatial features in a more effective method can raise the accuracy of classifier. For classification...
Automatic supervision of crowd behavior with the aim of detecting abnormal movements has become important in the field of public places' security and protection. Crowd congestion is not fixed in public places, thus we need an algorithm that can perform powerfully in high and low crowd congestions. Generally, there are two different methods for analyzing crowd behavior: the method which is based on...
One of the main challenges of histological image analysis is the high dimensionality of the images. This can be addressed via summarizing techniques or feature engineering. However, such approaches can limit the performance of subsequent machine learning models, particularly when dealing with highly heterogeneous tissue samples. One possible alternative is to employ unsupervised learning to determine...
Diabetic Retinopathy (DR) is one of the leading causes of blindness worldwide. Detecting DR and grading its severity is essential for disease treatment. Convolutional neural networks (CNNs) have achieved state-of-the-art performance in many different visual classification tasks. In this paper, we propose to combine CNNs with dictionary based approaches, which incorporates pathology specific image...
To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image quality. In this paper, we propose a noise reduction method for low-dose CT via deep neural network without accessing original projection data. A deep convolutional neural network is trained to transform low-dose CT images...
There are several challenges while building Automatic Speech Recognition (ASR) system for low resource languages such as Indic languages. One problem is the access to large amounts of training data required to build Acoustic Models (AM) from scratch. In the context of Indian English, another challenge encountered is code-mixing as many Indian speakers are multilingual and exhibit code-mixing in their...
Sparse representation based classification has gained popularity with geospatial image analysis in general and hyperspectral image analysis in particular. A central idea with such classification approaches is that a test pixel (spectral reflectance vector) can be sparsely represented in a training dictionary of pixels from all classes - in particular, only training pixels in the dictionary that bear...
A novel Hierarchical Structured Dictionary Learning (HSDL) algorithm is proposed in this paper. It aims to learn classs-pecific dictionaries for all classes simultaneously in a hierarchical structure. A discriminative term based on Fisher discrimination criterion is jointly considered for both the classs-pecific dictionaries in the lower level and the shared dictionaries in the upper level to enhance...
Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training. Subword classes in speech recognition systems correspond to context-dependent tied states or senones. The present work addresses some limitations of GMM-HMM senone alignments for DNN training. We hypothesize...
In this paper, we propose a blind motion deblurring method based on sparse representation and structural self-similarity from a single image. The priors for sparse representation and structural self-similarity are explicitly added into the recovery of the latent image by means of sparse and multi-scale nonlocal regularizations, and the down-sampled version of the observed blurry image is used as training...
In this paper, we propose a new supervised monaural source separation based on autoencoders. We employ the autoencoder for the dictionary training such that the nonlinear network can encode the target source with high expressiveness. The dictionary is trained by each target source without the mixture signal, which makes the system independent from the context where the dictionaries will be used. In...
The last decade of John Cozzens's tenure at the NSF witnessed the advent of theory and methods at the heart of modern data science. These advances include (but are not limited to) compressed sensing, sparse coding, inference methods robust to outliers and missing data, and convex optimization tools that facilitate a host of novel inference methods. This paper describes how these methods evolved from...
In many event detection applications, training data may contain tags with multiple, simultaneous events. This is particularly likely when the definition of “event” is broad and includes events that can persist for an extended period of time. Decomposing a mixed signal into signals corresponding to individual events is non-trivial. In this paper, we propose a non-negative matrix factorization (NMF)...
We consider stochastic nonparametric regression problems in a reproducing kernel Hilbert space (RKHS), an extension of expected risk minimization to nonlinear function estimation. Popular perception is that kernel methods are inapplicable to online settings, since the generalization of stochastic methods to kernelized function spaces require memory storage that is cubic in the iteration index (“the...
Densely sampled dynamic geophysical data are often modeled using principal components analysis (PCA, a.k.a. empirical orthogonal function or EOF analysis) to provide constraints for their inversion with remote sensing techniques. We show that overcomplete sparsifying dictionaries, generated using dictionary learning, provide a more informative basis for geophysical signal representation. Relative...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.