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Humans are trying to interact with the computer via touch screen, smart-phones, audio and video. A computer get information from the human via an interface and likewise, a human recognize an information from the computer via an interface. Facial expression recognition is a key element in a human communication. In order to promote the man and machine interaction, a framework is proposed for the facial...
Induction machines are omnipresent in industry because of their sturdiness and their ease of implementation. Nevertheless, these electrical motors still concede failures [e.g., interturn short circuit (ITSC) and broken rotor bar], which may lead to unplanned shutdowns. Consequently, manufacturing industries invest significant resources to avoid them with maintenance. Some studies have been achieved...
This paper presents a Face Detection System with Expression Recognition using Artificial Neural Networks. It is an automated vision system designed and implemented using MATLAB. The Face Detection with Expression Recognition system accomplishes facial expression recognition through two phases. The captured image is processed first to detect the face, and then the facial expression is recognized. These...
The purpose of this study is to show how n-grams are used for author recognition in the Azerbaijani language. As attribute vectors for analyzing of authorship are taken monogram and digram. We have developed a new approach to the determination of the attribute vectors for recognition of the author of an unknown text.
In order to identify a large number of very similar objects, a novel recognition approach is proposed by mean of combination of two dynamic grouping algorithms, the visual processing mechanism, PCA and multi-pathway SVM. The samples have been segmented to appropriate groups by grouping features, and then features with rotation invariance and translation invariance of each group are extracted. Finally,...
This paper proposes a method to identify and assess different levels of anger from the speech utterances. Unlike the existing methods which only detect the emotion from speech, the proposed method not only detects but also labels the level of an emotion. A 75 dimensional feature vector has been extracted from each audio clip and is used for training and testing. For classification and assessment the...
This paper presents the development of a Machine learning model through implementation of two algorithms namely Logistic Regression and Artificial Neural Networks to recognize handwritten digits from 0 to 9. The Training efficiency of both the algorithms is compared at the end of implementation. Logistic Regression is generally used for binary classification however; multiclass classification has...
Visually Impaired Malaysians find great difficulty in recognizing bank notes as there are no commercially available aid for her currency and not much research done in the area. A very low cost, high efficient system has been proposed to help visually impaired Malaysians identify her bank notes. This system is built around the unique color of her bank note. The system features a sound module, color...
To employ and develop the performance of the dimensionality reduction for microarray data there is need of good dimension reduction technique. High-dimensional data bring great challenges in terms of computational complexity and classification performance. Therefore, it is necessary to effectively compress in a low-dimensional feature space from high dimensional feature space to design a learner with...
Traffic signs automatic recognition was researched in this paper. Traffic signs image preprocessing methods was introduced firstly. Secondly, feature extraction algorithm of traffic signs based on SIFT was elaborated, then a fast SIFT algorithm based on PCA dimensionality reduction was presented to extract the characteristics of traffic signs. Finally, the SVM classifier was studied. A large number...
This paper aims that analysing neural network method in pattern recognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The proposed solutions focus on applying Adaptive Resonance Theory model for pattern recognition. The primary function of which is to retrieve in a pattern stored in memory, when an incomplete...
In the Brain-computer interface, classification and recognition technology plays an important role, especially the EEG classification and recognition for the movement imagery. In this paper, we use a new type of sensors to collect EEG signals. According to imagine the movement of left or right hand to identify two types of thinking, we proposed a new recognition method based on AR(auto-regressive)...
Currently, ontology plays an important role in semantic web technology. Ontology learning approach is to distinguish the type of input such as text, dictionary, knowledge, policies, schemes and semi-structured schemes relations. Ontology learning can be explained as information extraction subtask and its objectives are to dig the relevant concepts and relationships from the corpus or a particular...
A novel approach was developed to recognize vowels from continuous tongue and lip movements. Vowels were classified based on movement patterns (rather than on derived articulatory features, e.g., lip opening) using a machine learning approach. Recognition accuracy on a single-speaker dataset was 94.02% with a very short latency. Recognition accuracy was better for high vowels than for low vowels....
In this paper we address the issue of recognizing nonstandard Malaysian car license plates. These plates contain nonstandard characters such as italic, cursive and connected letters, which most plate recognition systems are unable to recognize. We propose a technique using stroke extraction and analysis to recognize these nonstandard characters. The proposed technique first extracts the contour of...
Evolutionary algorithms for selecting support vector machine (SVM) parameter values which are based on genetic algorithm and particle swarm optimization algorithm are researched in this paper, these algorithms have been successfully applied to the real underwater echo target recognition. Experimental comparison and analysis show that the evolutionary algorithms can identify optimal or near optimal...
In this paper, a new approach of synthetic aperture radar (SAR) image target recognition based on non-negative matrix factorization (NMF) feature extraction and Bayesian decision fusion is presented for recognizing ground vehicles in MSTAR database. First, feature vectors are extracted from image chips by NMF algorithm. Support vector machine (SVM) is used to classify the feature vectors. After multiple...
Human posture recognition is gaining increasing attention in the fields of artificial intelligence and computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more comprehensive problem of video sequence...
We present a framework to recognize objects in images based on their silhouettes. In previous work we developed translation and rotation invariant classification algorithms for textures based on Fourier transforms in the polar space followed by dimensionality reduction. Here we present a new approach to recognizing shapes by following a similar classification step with a "soft" retrieval...
Visual Surveillance in dynamic scenes is one of the most active research areas. In this paper an algorithm has been proposed to detect human behaviours for visual surveillance. This method gives an efficient face recognition technique in dynamic scenario using Principal Component Analysis and Minimum distance classifier.
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