A negative gradient of the image energy is a driving force, which controls the movement of an active contour. We might say that the final shape depends most on how well the image energy is defined. Traditional image e...
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A negative gradient of the image energy is a driving force, which controls the movement of an active contour. We might say that the final shape depends most on how well the image energy is defined. Traditional image energy models produce a limited range of the force and a poor vector filed definition for concave regions. These limitations have been resolved by a gradient vector flow whose major disadvantage is computational time. However, we have proposed a novel modulus maximums image energy that is based on the wavelet transform. It is defined as a cubic spatial interpolation between adjacent modulus maxima. Its negative gradient has a large capture range and forces active contours also into a concave shape. At the end an example of active contour driven by the negative gradient of modulus maximum image energy is presented
An architecture design of the intelligent agent for speech recognition and translation is presented in this paper. The design involves the agent architecture and the method of the agent is used. The architecture desig...
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The study on speech recognition and understanding has been done for many years. In this paper, we propose a fully-connected hidden layer between the input and state nodes and the output. Besides that, we also investig...
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ISBN:
(纸本)0780385934
The study on speech recognition and understanding has been done for many years. In this paper, we propose a fully-connected hidden layer between the input and state nodes and the output. Besides that, we also investigate and show that this hidden layer makes the learning of complex classification tasks more efficient. We also investigate difference between LPCC and MFCC in feature extraction process. The aim of the study was to observe the difference of Arabic's alphabet like "alif" until "ya". The purpose of this research is to upgrade the people's knowledge and understanding on Arabic's alphabet or word by using Fully-Connected Recurrent Neural Network (FCRNN) and Backpropagation through Time (BPTT) learning algorithm. 6 speakers (a mixture of male and female) are trained in quiet environment. Neural Network is well-known as a technique that has the ability to classified nonlinear problem. Today, lots of researches have been done in applying Neural Network towards the solution of speech recognition [1] such as Arabic. The Arabic language offers a number of challenges for speech recognition [2]. Even though positive results have been obtained from the continuous study, research on minimizing the error rate is still gaining lots of attention. This research utilizes Recurrent Neural Network, one of Neural Network technique to observe the difference of alphabet "alif" until "ya".
An architecture design of the intelligent agent for speech recognition and translation is presented in this paper. The design involves the agent architecture and the method of the agent is used. The architecture desig...
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An architecture design of the intelligent agent for speech recognition and translation is presented in this paper. The design involves the agent architecture and the method of the agent is used. The architecture design shows the relationship between the intelligent agent and speech recognition also translation. The intelligent agent for speech recognition is called S-AGENT and T-AGENT for translation. The purpose of the S-AGENT is to facilitate for transmitting the speech data via Internet or network. The S-AGENT is acting as a data transmit control to ensure the transmitted speech data is securely delivered. The task of the T-AGENT is different from the S-AGENT. The T-AGENT is acting as information retrieval. It processes the output from the speech recognition and translates the output based on its information memory database. If the information cannot be found on its memory, it searches the information required from the database dictionary provided. At the same time, it learns the information and saves the information to its memory for the future purpose.
Speech recognition and understanding have been studied for many years. The neural network is well-known as a technique that is able to classify nonlinear problems. Much research has been done in applying neural networ...
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Speech recognition and understanding have been studied for many years. The neural network is well-known as a technique that is able to classify nonlinear problems. Much research has been done in applying neural networks to solving the problem of recognizing speech such as Arabic. Arabic offers a number of challenges to speech recognition. We propose a fully-connected hidden layer between the input and state nodes and the output. We also investigate and show that this hidden layer makes the learning of complex classification tasks more efficient. We also investigate the difference between LPCC (linear predictive cepstrum coefficients) and MFCC (Mel-frequency cepstral coefficients) in the feature extraction process. The aim of the study was to observe the differences in the 29 letters of the Arabic alphabet from "alif" to "ya". The purpose of this research is to upgrade the knowledge and understanding of Arabic alphabet or words using a fully-connected recurrent neural network (FCRNN) and backpropagation through time (BPTT) learning algorithm. Six speakers (a mixture of male and female) in a quiet environment are used in training.
In this paper we present the structure of the simulator which would allow diving beginners to experience the effect of buoyancy control mechanisms before actually entering the *** believe such training would be less s...
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