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We discuss matching measures (scores and residuals) for comparing image patches under unknown affine photometric (=intensity) transformations. In contrast to existing methods, we derive a fully symmetric matching measure which reflects the fact that both copies of the signal are affected by measurement errors ('noise'), not only one. As it turns out, this evolves into an Eigen system problem, however...
In mathematical expression recognition, symbol classification is a crucial step. Numerous approaches for recognizing handwritten math symbols have been published, but most of them are either an online approach or a hybrid approach. There is an absence of a study focused on offline features for handwritten math symbol recognition. Furthermore, many papers provide results difficult to compare. In this...
This paper researches the alliance generation algorithm with emotional factors on the basis of multiple robots pursuit-evader problem. Firstly, this paper constructs an emotional model for pursuit robots: we not only apply the basic emotion method to the emotional expression, but also simulate the process of emotional transfer with Hidden Markov Model (HMM). Secondly, we determine the cooperation...
With the explosion of newly found proteins, it is necessary and urgent to develop automated computational methods for protein sub cellular location prediction. In particular, the problem of predictor construction for multi-location proteins is challenging. Considering the main limitations of the existing methods, we propose a hierarchical multi-label learning model FHML for both single-location proteins...
Trajectory classification has been extensively investigated in recent years, however, problems remain when processing incomplete trajectories of noises and local variations. In this paper, we propose a Locality-constrained Sparse Reconstruction (LSR) approach that explores both sparsity and local adaptability for robust trajectory classification. A trajectory dictionary with locality constrains is...
ADFA-LD is a recently released data set for evaluating host-based anomaly detection systems, aiming to substitute the existing benchmark data sets which have failed to reflect the characteristics of modern computer systems. In a previous work, we had attempted to evaluate ADFA-LD with a highly efficient frequency model but the performance is inferior. In this paper, we focus on the other typical technical...
Speech enhancement based on hidden Markov model (HMM) and the minimum mean square error (MMSE) criterion in Mel-frequency domain is generally considered as a weighted-sum filtering of the noisy speech. The weights of filters are often estimated by the HMM of noisy speech, and the estimation of filters usually requires an inverse operation from the Mel-frequency to the spectral domain which often causes...
This paper addresses the problem of silhouette-based human action segmentation and recognition in monocular sequences. Motion History Images (MHIs), used as 2D templates, capture motion information by encoding where and when motion occurred in the images. Inspired by codebook approaches for object and scene categorization, we first construct a codebook of temporal motion templates by clustering all...
Cloud computing significantly increased the security threats because intruders can exploit the large amount of cloud resources for their attacks. However, most of the current security technologies do not provide early warnings about such attacks. This paper presents a Finite State Hidden Markov prediction model that uses an adaptive risk approach to predict multi-staged cloud attacks. The risk model...
In this paper, we describe a novel method for handwriting style identification. A handwriting style can be common to one or several writer. It can represent also a handwriting style used in a period of the history or for specific document. Our method is based on Gaussian Mixture Models (GMMs) using different kind of features computed using a combined fixed-length horizontal and vertical sliding window...
Human control strategy has been considered as a robust control method in mastering the complex and dynamic skill. In this paper, we present the problem of how human control strategy can be represented as a learning based approach and how a human strategy controller can be used in controlling dynamically stable while statically unstable, Wheeled Inverted Pendulum (WIP). The controller is designed by...
In this paper, we propose a method based on the SVM algorithm to recognize dynamic hand gestures. The information of motion trajectory is captured by a leap motion in three-dimension space. A new methodology of feature extracting is proposed to guarantee the length of samples being the same. The elements of feature vectors are ranged according to two different criteria: one is the amplitude of the...
In this paper, we propose a Wi-Fi positioning method based on Deep Learning (DL). To deal with the variant and unpredictable wireless signals, the positioning is casted in a four-layer Deep Neural Network (DNN) structure that is capable of learning reliable features from a large set of noisy samples and avoids the need for hand-engineering. Also, to maintain the temporal coherence, a Hidden Markov...
Motion detection is a basis step for video processing. Previous works of motion detection based on deep learning need clean foreground or background images which always do not exist in practice. To address this challenge, a novel and practical method is proposed based on auto-encoder neural networks. First, the approximate background images are obtained via an auto-encoder network (called Reconstruction...
Our real-time continuous gesture recognition system addresses problems that have previously been neglected: handling both gestures that are characterized by distinct paths and gestures characterized by distinct hand poses; and determining how and when the system should respond to gestures. Our probabilistic recognition framework based on hidden Markov models (HMMs) unifies the recognition of the two...
Speech reconstruction is a key issue in speech coding. In this paper, we propose an extended least-squares estimate, inverse short-time Fourier transforms magnitude (LSE-ISTFTM) speech reconstruction algorithm for MFCC-based low bit-rate speech coding. The proposed extended LSE-ISTFTM algorithm initializes speech with a specific signal rather than white noise, reconstructs voiced and unvoiced frames...
A new method of diagnosis prediction based on adaptive filtering and HMM is proposed. Firstly, the feature extraction of time-sequence about collected diagnosis information is conducted, achieving time-sequence status information, and then on the basis of the results the future device status information vectors are obtained by means of adaptive filtering. Secondly, the HMMs for all diagnosis statuses...
This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded...
This paper presents a novel algorithm for restoration of the missing bandwidth of narrowband speech signals. The proposed algorithm improved the performance of the traditional line spectral frequencies (LSF) based extension algorithm by exploiting a Hidden Markov Model (HMM) to indicate the proper representatives of different frames, and by applying a minimum mean square criterion to estimate the...
Some audio files' formats contain metadata such as musical genre. This facilitates the usability of musical devices. However there are still many audio files' formats in which no metadata about the sound can be found. Our goal is to provide to users the same comfort in these conditions by computing the genres automatically. Various techniques have been proposed to determine a song's genre among N...
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