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.
In this paper, we present a feedforward training algorithm using Regularized Logistic Regression and Neural Networks to recognize handwritten objects. Furthermore, we intend to consider the effect of Gaussian noise in this procedure in order to examine the versatility of our approach. We might intend to transmit the image of our digits through an AWGN channel to a certain destination and then do the...
Due to the data diversity and complexity in industrial system, the accuracy of data-based modeling might be largely affected by such a series of issues. Aiming at the energy system in steel industry, this study proposes a fuzzy modeling based on Gaussian membership expression. First, in the stage of sample selection, the industrial data set is divided into a number of clusters, from which the representative...
Traditionally, NMF algorithms consist of two separate stages: a training stage, in which a generative model is learned; and a testing stage in which the pre-learned model is used in a high level task such as enhancement, separation, or classification. As an alternative, we propose a task-supervised NMF method for the adaptation of the basis spectra learned in the first stage to enhance the performance...
This paper investigates recently proposed Stranded Gaussian Mixture acoustic Model (SGMM) for Automatic Speech Recognition (ASR). This model extends conventional hidden Markov model (HMM-GMM) by explicitly introducing dependencies between components of the observation Gaussian mixture densities. The main objective of the paper is to experimentally study, how useful SGMM can be for dealing with data,...
In this paper, we explore the possibility to solve a commonly-known digital image forensics problem, the Source Camera Identification (SCI) problem, using a distributed approach. The SCI problem requires to recognize the camera used to acquire a given digital image, distinguishing even among cameras of the same brand and model. The solution we present is based on the algorithm by Lukas Fridrich, as...
In this paper we are interested in exploiting self-similarity information for discriminative image denoising. Towards this goal, we propose a simple yet powerful denoising method based on transductive Gaussian processes, which introduces self-similarity in the prediction stage. Our approach allows to build a rich similarity measure by learning hyper parameters defining multi-kernel combinations. We...
We address the problem of sound representation and classification and present results of a comparative study in the context of a domestic robotic scenario. A dataset of sounds was recorded in realistic conditions (background noise, presence of several sound sources, reverberations, etc.) using the humanoid robot NAO. An extended benchmark is carried out to test a variety of representations combined...
Binning of IC circuits after volume fabrication is widely used to separate tested circuits in different classes depending on different degrees of specifications compliance. When the specifications are directly measured, the boundaries of the classes are usually linear functions in the specification space. For alternate testing strategies the indirect measures generate more complicated regions in the...
In the paper, a regularized correntropy criterion (RCC) for radial basis function neural network (RBFNN) is proposed. In RCC, the Gaussian kernel function is used to replace the Eculidean norm of the sum-squared-error (SSE) criterion. Replacing SSE by RCC can improve the anti-noise ability of RBFNN. Moreover, the optimal weights and the optimal bias terms can be iteratively obtained by the half-quadratic...
n this paper, we introduce a three-phase transmis- sion scheme for cloud radio networks against eavesdropper. We study the security and reliability performance of this scheme in the form of intercept probability and outage probability, respectively. Channel estimation error is considered in our study instead of assumption of perfect channel state information. It is found that the security-reliability...
Historical Chinese character recognition has been a challenging topic in pattern recognition field because of large character set, various writing styles and lack of training samples. In this paper, we adopted Style Transfer Mapping (STM) method to historical Chinese character recognition. Optimal selection of parameters was discussed. Two sets of experiments were conducted. The first set of experiment...
The more complete the training set of an optical character recognition platform, the greater the chances of obtaining a better precision in transcription. The development of a database for such purpose is a task of paramount effort as it is performed manually and must be as extensive as possible in order to potentially cover all words in a language. Dealing with historic documents either handwritten,...
This paper addresses the problem of automatic learning of statistical models of clicks for odontocete species classifications, particularly focusing on improving accuracy of the classifier by iteratively identifying click-like sounds that are likely to be noise and removing these from the model training set. The algorithm is weakly supervised in that no hand-labeled click regions are available, but...
Macromolecular structure determination using cryo-electron tomography requires large amount of subtomograms depicting the same molecule, which are averaged. In this paper, we propose a novel automatic particle picking and classification method for cryo-electron tomograms. The workflow comprises two stages: detection and classification. The detection method consists of a template-free picking procedure...
Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image...
Speech processing is now an emerging technology of signal processing. Some research areas of speech processing are recognition of speech, speaker identification (SI), speech synthesis etc. Speaker identification is important research area of speech processing. SI means identifying the speaker based on his spoken speech. The main use of SI is to recognize the speech owner based on the speaking style...
In this paper we propose a neural net based characters recognition scheme for Bangla printed text books. There are a lot of scientific literature, novels, magazines and books etc that are written in Bangla language. More than 400 million people use Bangla language. Most of the library and educational institutions want to keep copy of the books in a digital format. For storing those books in digital...
We present a Sparse Representation-based Classifier (SRC) that provides superior performance in terms of high Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) in classifying benign and malignant breast lesions captured in ultrasound images. Although such a classifier was proposed for face recognition, it has been proposed in medical diagnosis from ultrasonic images in this work for...
Silhouette-based gait analysis is a well-established biometric approach for human identification. Over the years researchers have proposed a number of gait recognition approaches based on the entire silhouette of human body. These approaches are proven to give good recognition accuracies. However, the feature vector generation and subsequent classification depend on information extracted from the...
This paper presents a new approach to off-line handwritten digit recognition based on structural features which is not required thinning operation and size normalization technique. In this paper uses four different types of structural features namely, number of holes, water reservoirs in four directions, maximum profile distances in four directions, and fill-hole density for the recognition of digits...
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.