A camera has been widely used in practical fields with a diversity of purposes recently. There is a variety purpose of photography: images for memory, medical images for diagnosis, images for object recognition, surve...
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ISBN:
(纸本)9781509049172
A camera has been widely used in practical fields with a diversity of purposes recently. There is a variety purpose of photography: images for memory, medical images for diagnosis, images for object recognition, surveillance images, and so on. In case of images for object recognition, the clarity of images is necessary to analyze the images which are obtained using vision sensors. However, a brightness of the image highly depends on the intensity of illumination in the certain environment. Therefore, we propose a method to solve the problems mentioned above by adjusting brightness automatically by utilizing CIE L*a*b* color space and fuzzy inference system. At first, the proposed method adjusts the brightness of a given image by considering both RGB component and L component of CIE L*a*b* color space. Secondly, the proposed method applies the fuzzy inference system to determine adjustment coefficients of each pixel for adjusting brightness of the image. Through the processes as mentioned above, we can obtain the result which is adjusted its brightness. To verify the proposed method, we compare the result image with two different images, a reference image, and an adjusted image by using offset. It is confirmed that the proposed method can adjust a given image efficiently and automatically.
This paper presents a novel method for patternrecognition problem in terms of linear regression. Normally, patterns from a single-object class lie on a linear subspace. Using this concept, we develop a linear model r...
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ISBN:
(纸本)9781479949816
This paper presents a novel method for patternrecognition problem in terms of linear regression. Normally, patterns from a single-object class lie on a linear subspace. Using this concept, we develop a linear model representing a probe image as a linear combination of class-specific galleries. Linear Regression Classification (LRC) algorithm for patternrecognition belongs to the category of nearest subspace classification. This algorithm is extensively evaluated on several standard digit and English character databases and our own Tamil character database. A comparative study with different databases and methods clearly reflects the efficiency of LRC approach for patternrecognition.
Real-time monitoring of human movements can be easily envisaged as a useful tool for many purposes and future applications. This paper presents the implementation of a real-time classification system for some basic hu...
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ISBN:
(纸本)9783642024801
Real-time monitoring of human movements can be easily envisaged as a useful tool for many purposes and future applications. This paper presents the implementation of a real-time classification system for some basic human movements using a conventional mobile phone equipped with an accelerometer. The aim of this study was to check the present capacity of conventional mobile phones to execute in real-time all the necessary patternrecognition algorithms to classify the corresponding human movements. No server processing data is involved in this approach, so the human monitoring is completely decentralized and only an additional software will be required to remotely report the human monitoring. The feasibility of this approach opens a new range of opportunities to develop new applications at a reasonable low-cost.
Education field is rich of data, and machine learning used in this field are increasing lately. Based on their first semester result, using machine learning techniques, the student's final year result (GPA) can be...
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ISBN:
(纸本)9781538694220
Education field is rich of data, and machine learning used in this field are increasing lately. Based on their first semester result, using machine learning techniques, the student's final year result (GPA) can be predicted. The data used in this experiment are from the computer science subjects, 6 subjects, 1 laboratories results and the GPA on their graduation year. The techniques used in this experiment are Generalized Linear Model, Deep Learning and Decision Tree. From this result, what are the important factors that impact on the result can be extracted to help the students prepared themselves earlier.
In this paper the fusion of artificial neural networks, granular computing and learning automata theory is proposed and we present as a final result ANLAGIS, an adaptive neuron-like network based on learning automata ...
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In this paper the fusion of artificial neural networks, granular computing and learning automata theory is proposed and we present as a final result ANLAGIS, an adaptive neuron-like network based on learning automata and granular inference systems. ANLAGIS can be applied to both patternrecognition and learning control problems. Another interesting contribution of this paper is the distinction between presynaptic and post-synaptic learning in artificial neural networks. Tc illustrate the capabilities of ANLAGIS some experiments on knowledge discovery in data mining and machine learning are presented. The main, novel contribution of ANLAGIS is the incorporation of Learning Automata Theory within its structure;the paper includes also a novel learning scheme for stochastic learning automata. (C) 2010 Elsevier B.V. All rights reserved.
The aim of this investigation is to describe a way of grey incidence analysis to achieve vibration fault patternrecognition for hydraulic generator units. In order to recognise the fault, the collected fault data are...
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ISBN:
(纸本)9781424441051
The aim of this investigation is to describe a way of grey incidence analysis to achieve vibration fault patternrecognition for hydraulic generator units. In order to recognise the fault, the collected fault data are analyzed and then standard fault pattern sets of typical vibration faults for hydraulic generator units are founded. Fault patternrecognition is performed by calculating the degree of grey incidence between the pending pattern characteristics and the standard fault pattern sets, according to the criterion for maximum degree of incidence. Results are presented which demonstrate the effectiveness of the proposed method and it can be used as an online diagnosis tool for hydraulic generator units.
The service triggering mechanism of current IMS not only leads to heavy load of the S-CSCF entities, but also limits the provision of feasible service which makes use of context information. Thus, a ubiquitous-computi...
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ISBN:
(纸本)9783642279652
The service triggering mechanism of current IMS not only leads to heavy load of the S-CSCF entities, but also limits the provision of feasible service which makes use of context information. Thus, a ubiquitous-computing enabled service triggering and executing pattern in converged network is introduced. Via analyzing service pattern of current IMS architecture, a novel mechanism that introduces P2P like service triggering based on terminal side coordination is introduced, which results in the conversion from central service control to the negotiation service triggering pattern based on context information among terminals. In this new service pattern, context aware triggering is supported. Moreover, service can be executed in a distributed way which means service logic can be composed of terminal and server parts. A detail scenario is also discussed in this paper.
Coverage is a kind of method to cover points of same class samples in feature space, which is based on Biomimetic patternrecognition. The mathematical description of coverage is given and the discriminant boundary of...
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ISBN:
(纸本)9783540725299
Coverage is a kind of method to cover points of same class samples in feature space, which is based on Biomimetic patternrecognition. The mathematical description of coverage is given and the discriminant boundary of coverage is shown. Coverage is tested in face recognition on ORL database. Both the COVERAGE and SVM networks are used for covering. The results show that COVERAGE act better than SVM in generalization, especially for small sample set, which are consonant with the result of the applications of BPR.
This paper presents a technique for speckle reduction in SAR images by using the interscale multiplication in Mallat wavelet transform (MWT) and stationary wavelet transform (SWT). The edge and non-edge regions can be...
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ISBN:
(纸本)9781424422388
This paper presents a technique for speckle reduction in SAR images by using the interscale multiplication in Mallat wavelet transform (MWT) and stationary wavelet transform (SWT). The edge and non-edge regions can be detected from median of squared amplitude in each scale. Applying this technique, the large wavelet coefficients generated by edge region and the small wavelet coefficients or speckle noises are then suppressed by using soft thresholding in all high frequency hands. The well-known threshold estimation for soft thresholding based on SimpleShrink, NormalShrink, VisuShrink, SureShrink, and BayesShrink. The despeckled image is then obtained by reconstruction from the resulted coefficients. The assessment of the proposed method, the experiment has conducted using SAR images from JERS-1 satellite. The experimental result shows that the proposed method yields noise suppression and preserves the detail feature of the images, as well as perceptual image quality.
We proposed a method which can solve the low speech recognition rate problem under noisy environment. In our system, the method is GAS-based Speech recognition using Two Dimensional cepstrum. Two dimensional cepstrum ...
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ISBN:
(纸本)9780769547633
We proposed a method which can solve the low speech recognition rate problem under noisy environment. In our system, the method is GAS-based Speech recognition using Two Dimensional cepstrum. Two dimensional cepstrum (TDC) can simultaneously represent several kinds of information contained in the speech waveform: static and dynamic features, as well as global and fine frequency structures. From analysis, an utterance only some TDC coefficients will be selected to form a feature vector. Hence, it has the advantages of soft computation and less storage space. However, it is quite sensitive to background noise. In order to solve this problem, we propose the GAS-based M-TDC method in our system to improve the performance of TDC under noisy condition. From the experiments with five noise types, we found that the GAS-based M-TDC have better recognition results than the TDC under the noisy environments.
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