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This papers deals with supervised texture classification. The extracted features are the image second and third order moments. The number of possible moment lags for 2-D signals increases rapidly with the order of the moment even for small lag neighbourhoods. The paper focuses on the selection of moment lags that optimise classification performance. Lag selection also serves another purpose: it waives...
Features defined on the cortical surface derived from magnetic resonance imaging provide important information to diagnosis the Alzheimer's disease (AD) and its premonitory symptoms Mild Cognitive Impairment (MCI). In general, the methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise...
In this paper we proposed a new method for pedestrian detection in images and videos. Our method uses a sliding window to search through images. In order to extract the features, each window is divided into overlapping cells and features are extracted from them. The feature that we extracted to describe each window is based on analysis of gradient distribution of each cell. After gradient distribution...
This paper presents a online multi-font numeral recognition method, whose main aim is to recognize overlaid time numeral from video. The portion of the video frame containing the time text is binarized and segmented. Minimum rectangular bounding box is inserted over the isolated numeral images. Euler number of numeral images is found out to initially differentiate into three groups. Then, the numerals...
In order to make voice conversion (VC) robust to noise, we propose VC using GA-based informative feature (GIF), by adding an extraction process of GIF to a conventional VC. GIF is proposed as a feature that can be applied not only in pattern recognition but also in relative tasks. In speech recognition, furthermore, GIF could improve recognition accuracy in noise environment. We evaluated the performances...
A medical staff needs to check sputum accumulation in patient's respiratory tract by lung sounds auscultation at any time, and it is the big burden for the staff. This paper aims to develop a system which notifies appropriate timing for the tracheal suction for the medical staff by analyzing lung sounds of the patients. We present a novel framework about automatic sputum detection from lung sounds...
This paper, deals with classification of Anteroseptal Myocardial Infarction and normal subjects. Multiresolution approach based extraction of diagnostic pathological features from V1–V4 chest leads is proposed. Mahalanobish distance based classification is used for classification and generation of discriminant function.The digitized ECG signals is subjected to DWT based denoising before applying feature...
A decision tree support vector machine (SVM) classification method based on the construction of ship-radiated noise multidimension feature vector is proposed in this paper. Aimed at three kinds of ship targets (class I submarine, class II warship and class III merchant ship) radiated noise, the subband distribution feature vectors of their 1½-spectrum and 2½-spectrum, and scale-energy feature vector...
Endpoint detection is the key technology in system of speech identification. As an object of experiment and research, Mongolian speech attracts more and more researchers. It is significant for the development of Mongolian speech identification technology to apply endpoint detection to Mongolian speech. Support Vector Machine (SVM) is a kind of new technology in the field of Data Mining, this paper...
Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. This biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based...
Today's advanced muscular sensing and processing technologies have made the acquisition of electromyography (EMG) signal which is valuable. EMG signal is the measurement of electrical potentials generated by muscle cells which is an indicator of muscle activity. Other than rehabilitation engineering and clinical applications, EMG signals can also be employed in the field of human computer interaction...
Tibetan feature extraction algorithm is the most critical link in Tibetan speech recognition system. According to Tibetan Lhasa Dialect phonetic and pronunciation features, the Mel frequency cepstrum coefficient (MFCC) feature extraction algorithm is established in this paper based on the simulation of human auditory system, and extracted feature data are compressed through the LDA information compression...
According to the symmetric characteristics of bispectrum, a novel feature extraction scheme, which includes the summation-at-every-column feature vector, the summation-at-every-row feature vector and their combination in a triangle area, one of the 12 symmetric areas of bispectrum, is proposed. By using One-against-One (OAO) method of multi classification of Support Vector Machine (SVM), the mean...
Measured radar data assisted in the successful development and implementation of Specific Emitter Identification (SEI) signal processing algorithms. The aim of the algorithm is the identification of a specific emitter within a single class of emitters. The processes developed are pulse extraction, feature calculation, dimensionality reduction and classification. A pulse is detected whenever the phase...
A Myocardial Ischemia Detection Algorithm (MIDA) is presented that analyses echocardiography sequences automatically in order to detect the presence of heart muscle damage. MIDA involves an image enhancement, fuzzy multi resolution edge detection to obtain the heart wall boundaries, composite motion image creation using the heart wall boundaries, followed by statistical pattern recognition and classification...
The main objective of this paper is to explore the effectiveness of perceptual features for performing isolated digits and continuous speech recognition. The proposed perceptual features are captured and training models are developed by K-means clustering procedure. Speech recognition system is evaluated on clean and noisy test speeches and the experimental results reveal the performance of the proposed...
The paper provides a novel approach to emotion recognition from facial expression and voice of subjects. The subjects are asked to manifest their emotional exposure in both facial expression and voice, while uttering a given sentence. Facial features including mouth-opening, eye-opening, eyebrow-constriction, and voice features including, first three formants: F1, F2, and F3, and respective powers...
The paper investigates the feasibility of implementing an intelligent classifier for noise sources in the ocean, with the help of artificial neural networks, using higher order spectral features. Non-linear interactions between the component frequencies of the noise data can give rise to certain phase relations called Quadratic Phase Coupling (QPC), which cannot be characterized by power spectral...
We present a novel method for object recognition in noise free and noisy environments, based on modified invariant moments and minimum norm. First, the modified invariant moments of different objects are extracted by using invariant moments. Then the norms of feature vectors are computed by using norm theory of functional analysis. Finally, classification and recognition object are accomplished according...
Emotion recognition from electrocardiography (ECG) signal has become an important research topic in the field of affective computing. In the current work, ECG signals from multiple subjects were collected when film clips shown to them, and then feature sets were extracted from precise location of P-QRS-T wave by continuous wavelet transform (CWT). Hybrid particle swarm optimization (HPSO) was utilized...
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