Feature vector

Character recognition is an active field of research. Applications include point of sale systems, tablet computers, personal digital assistants (PDAs), smart phones, and military applications. Recognizing Asian characters has been pursued since 1984, and difficulties exist in Japanese due to the complexity and numbers of Kanji, Hiragana, and Katakana characters. It is further complicated by differences in size, translation, and

In this paper an efficient scheme for automatic organization of stereo-captured video sequences is presented, which exploits foreground VOP information of frames. More specifically after shot cut detection, for each frame of a shot, a fast, unsupervised foreground VOP extraction algorithm is applied, based on depth information and normalized Motion Geometric Spaces. Then for each frame, a feature vector is

Abstract—In this paper, we introduce a novel age estimation technique that combines Active Appearance Models (AAMs) and Support Vector Machines (SVMs), to dramatically improve the accuracy of age estimation over the current state-of-the-art techniques. In this method, ...

In this work, a new on-line signature verification system is proposed. Firstly, the pen-position parameters of the online signature are decomposed into multiscale signals by using the wavelet transform technique. A TESPAR DZ based method is employed to code the approximation and details coefficients. Thus, for each analyzed time function, a fixed dimension feature vector is obtained. Experimental results were

In statistical HMM-based text-to-speech systems (STTS), speech feature dynamics is modeled by first- and second-order feature frame differences, which, typically, do not satisfactorily represent frame to frame feature dynamics present in natural speech. The reduced dynamics results in over-smoothing of speech features, often sounding as muffled synthesized speech. In this correspondence, we propose a method to enhance a baseline STTS system by introducing a segment-wise model representation with a norm constraint. The segment-wise representation provides additional degrees of freedom in speech feature determination. We exploit these degrees of freedom for increasing the speech feature vector norm to match a norm constraint. As a result, statistically generated speech features are less over-smoothed, resulting in more natural sounding speech, as judged by listening tests.

In automatic speech recognition system a diagonal GMM based CDHMM modeling is commonly used. So there is a need to use reasonable feature transformation to decorrelate input feature vectors to satisfy diagonal GMM assumption. In this paper, we introduce the utilization of the several supervised linear feature transformation in speech recognition tasks. Specially each of these methods has particular projection properties. We show that the proposed OLPP based feature transformation method with preserving local ...

An expression recognition technique is proposed based on the hidden Markov models (HMM) ability to deal with time sequential data and to provide time scale invariability as well as a learning capability. A feature vector sequence is used for this purpose, which relies on optical flow extraction, as well as directional filtering of the motion field. Segmentation and identification of

Abstract A new neural network based method for multichannel image segmentation is introduced. The segmentation result possesses also some classification capabilities. The segmentation and classification are based on a texture map. The texture map is created by a self-...

Abstract—The aim of this paper is to develop a method for low-cost and accurate classification of highways and rural ways image pixels for lane detection. The method uses three main components: adaptive/predefined image splitting, subimage level classification and class ...

In this paper, we provide an automatic unsupervised recognition technique for Web community user reputations that uses a special nonlinear metric. First we describe the general framework for reputation systems. Then, we propose a feature extraction approach for the reputation system users. The resulting feature vectors (reputations) are clustered with an unsupervised classification algorithm using a nonlinear distance, derived from the Hausdorff metric for sets.

Continuous seismic monitoring plays a key role for surveillance of Mt Etna volcano. Besides earthquakes, which often herald eruptive episodes, the persistent background signal, known as volcanic tremor has proven to provide extremely important information on the status of the volcano as changes in the regimes of activity are usually concurrent with variations of tremor characteristics. This strict relationship is useful for monitoring volcanic activity in any moment and in whatever condition (such as day-night, meteo). As continuous recording leads rapidly to the accumulation of large data masses, parameter extraction and automated processing becomes crucial. We therefore developed a software package which allows automatic unsupervised classification near-online. The software package is based on Self Organizing Maps and Fuzzy Clustering, and displays the results of both approaches in a synoptic way. The concept has proven its efficiency during various phases of volcanic unrest in 20...

Abstract— In this paper, we utilize the frequency domain representation of electrocardiogram (ECG) signals for the training of auto-associative neural networks. Since ECG signals, when taken over shorter duration, are almost periodic; so they can be considered to be short ...

Artificial Intelligence (AI) has found broad applications in volcano observatories worldwide with the aim of reducing volcanic hazard. The need to process larger and larger quantity of data makes indeed AI techniques appealing for monitoring purposes. Tools based on Artificial Neural Networks and Support Vector Machine have proved to be particularly successful in the classification of seismic events and volcanic tremor changes heralding eruptive activity, such as paroxysmal explosions and lava fountaining at Stromboli and Mt Etna, Italy (e.g., Falsaperla et al., 1996; Langer et al., 2009). Moving on from the excellent results obtained from these applications, we present KKAnalysis, a MATLAB based software which combines several unsupervised pattern classification methods, exploiting routines of the SOM Toolbox 2 for MATLAB (http://www.cis.hut.fi/projects/somtoolbox). KKAnalysis is based on Self Organizing Maps (SOM) and clustering methods consisting of K-Means, Fuzzy C-Means, and a ...

Applications of wavelet analysis are widespread and cover many fields of scientific research including image processing, classification and recognition. In addition, the mathematical concept of moments has been used for many years in pattern recognition and image processing. We present a new discovered family of splines, named fractional B-splines which we used as mother wavelet functions. The resulted fractional B-spline

Recently, an emphasis has been placed on the content based Information Retrieval Systems (IRS). Finding documents based on content similarity using background knowledge is becoming an increasingly important task. This paper aims for two main tasks in order to high quality document retrieval; first, we present our formulation of fuzzy ontology and then, by this formulation, propose a method which

We develop a new neural network architecture for projective clustering of data sets that incorporates adaptive transmission delays and signal transmission information loss. The resultant selective output signaling mechanism does not require the addition of multiple hidden layers but instead is based on the assumption that the signal transmission velocity between input processing neurons and clustering neurons is proportional to the similarity between the input pattern and the feature vector (the top-down weights) of the clustering neuron. The mathematical model governing the evolution of the signal transmission delay, the short-term memory traces, and the long-term memory traces represents a new class of large-scale delay differential equations where the evolution of the delay is described by a nonlinear differential equation involving the similarity measure already noted. We give a complete description of the computational performance of the network for a wide range of parameter va...