artificial Intelligence has a large number of applications related to pattern recognition. Speech and image recognition are the most used in artificial intelligence applications because of its utility in the developme...
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ISBN:
(纸本)9781601321190
artificial Intelligence has a large number of applications related to pattern recognition. Speech and image recognition are the most used in artificial intelligence applications because of its utility in the development of real time interface user applications. Despite the fact that just a few studies have been developed with an intention of mixing Speech and image recognition as two different types of data for audiovisual applications, methodologies as AV-ASR (Audio Visual Speech Recognition) have been deeply studied. In this paper is introduced a methodology of integration of both face detection using rapid object detection using a boosted cascade of simple features and speech recognition system using soft computing techniques. In addition, the results obtained from the experience of using an autonomous system of audiovisual automatic assistance are presented too.
In this paper we are apply artificial Metaplasticity MLP (MMLPs) to Breast Cancer Classification. artificial Metaplasticity is a novel ANN training algorithm that gives more relevance to less frequent training pattern...
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ISBN:
(纸本)9783642022661
In this paper we are apply artificial Metaplasticity MLP (MMLPs) to Breast Cancer Classification. artificial Metaplasticity is a novel ANN training algorithm that gives more relevance to less frequent training patterns and subtract relevance to the frequent ones during training phase, achieving a much more efficient training, while at least maintaining the Multilayer Perceptron performance. Wisconsin Breast Cancer Database (WBCD) was used to train and test MMLPs. WBCD is a well-used database in machine learning, neuralnetworks and signal processing. Experimental results show that MMLPs reach better accuracy than any other recent results.
Geospatial information we gather through different sensors and from the concepts of the geographic objects, is generally vague, imprecise and uncertain. Also, the imprecision becomes obvious due to the multi-granular ...
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ISBN:
(纸本)9781424447107
Geospatial information we gather through different sensors and from the concepts of the geographic objects, is generally vague, imprecise and uncertain. Also, the imprecision becomes obvious due to the multi-granular structure of the multisensor satellite images and that leads to error accumulation at every stage in geo-processing. It has been observed that the ground truth data, forming a prime decision system, an essential ingredient for a supervised learning, may itself contain redundant / inconsistent / conflicting information. Moreover, there may be superfluous attributes that warrants a fast mechanism to identify & discard them and at the same time keep the information content compatible to the original data set. Recently the Rough Set Theory - proposed by Zdzislaw Pawlak, has emerged as an effective measure to resolve imprecise knowledge, analysis of conflicts, evaluation of data dependencies and generating rules. In this study, we have applied the Rough Set Theory, to handle the imprecision due to granularity of the structure of the satellite image. The objective is how the decision system required for any supervised classification, is made consistent and free from superfluous attributes. We compared the results of performing land cover classification of the LISS-iiI image pertaining to Alwar (Rajasthan) area by the rough set, artificialneuralnetworks, and rough-fuzzy theory. Our findings are that, in the era of internet GIS, time and accuracy is the prime requirement in classification and interpretation of images for any critical application. Rough set and rough-fuzzy theory offer a better and transparent choice to have faster, comparable and effective results.
In recent years, some of the most emerging applications in multimedia data processing are wireless/mobile multimedia systems and streaming content over the Internet. Both applications require flexible image data compr...
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Non-parametric Bayesian techniques are considered for learning dictionaries for sparse image representations, with applications in denoising, inpainting and com-pressive sensing (CS). The beta process is employed as a...
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ISBN:
(纸本)9781615679119
Non-parametric Bayesian techniques are considered for learning dictionaries for sparse image representations, with applications in denoising, inpainting and com-pressive sensing (CS). The beta process is employed as a prior for learning the dictionary, and this non-parametric method naturally infers an appropriate dictionary size. The Dirichlet process and a probit stick-breaking process are also considered to exploit structure within an image. The proposed method can learn a sparse dictionary in situ;training images may be exploited if available, but they are not required. Further, the noise variance need not be known, and can be non-stationary. Another virtue of the proposed method is that sequential inference can be readily employed, thereby allowing scaling to large images. Several example results are presented, using both Gibbs and variational Bayesian inference, with comparisons to other state-of-the-art approaches.
This article presents a distributed agent-based architecture that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an in...
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ISBN:
(纸本)9783642024771
This article presents a distributed agent-based architecture that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an intelligent environment for location and identification within dependent environments that merge with other types of technologies. Vision algorithms are very costly and take a lot of time to respond, which is highly inconvenient if we consider that many applications can require action to be taken in real time. An agent architecture can automate the process of analyzing images obtained by cameras, and optimize the procedure.
The proceedings contain 40 papers. The topics discussed include: wavelets and their applications past and future;improved radon based imaging using the shearlet transform;converting data into functions for continuous ...
ISBN:
(纸本)9780819476098
The proceedings contain 40 papers. The topics discussed include: wavelets and their applications past and future;improved radon based imaging using the shearlet transform;converting data into functions for continuous wavelet analysis;grouping individual independent BOLD effects: a new way to ICA group analysis;denoising using adaptive thresholding and higher order statistics;neural network approach for mobile bay water quality mapping with spaceborne measurements;classification of biological and non-biological fluvial particles using imageprocessing and artificialneural network;development of minimally invasive surgery for intractable epilepsy;systems approaches in molecular and cell biology: making sense out of data and providing meaning to models;and predictive data modeling of human type ii diabetes related statistics.
A Hopfield-like neural network, called SALU-SIR, whose system weight matrix is symmetric is presented with its mathematical analysis in [7]. However, what happens if the system matrix is unsymmetric? Is the system sti...
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ISBN:
(纸本)9781424451661
A Hopfield-like neural network, called SALU-SIR, whose system weight matrix is symmetric is presented with its mathematical analysis in [7]. However, what happens if the system matrix is unsymmetric? Is the system still stable in the unsymmetric case? In this paper, we address these important questions, whose answer is paramount especially when the system is to be implemented in practice. The underlying linear system of the proposed network is x(k + 1) = Ax(k) + b where A is any real square unsymmetric matrix with linearly independent eigenvectors whose largest eigenvalue is real and its norm is larger than I, and vector b is constant. Our investigations in this paper show that i) the unsymmetric case is also stable;ii) the unsymmetric case yields state-specific ultimate SIRs as compared to the system-specific ultimate SIR in the symmetric case [7], which allows us to design more complex systems. iii) the ultimate "SIR"s in the investigated unsymmetric matrix A case are equal to a(ii)/lambda(max)-a(ii), i = 1, 2, ... , N, where N is the number of states, a(ii) is the diagonal elements of matrix A, and lambda(max) is the (single or multiple) eigenvalue with maximum norm. Possible applications include binary associative memory systems, image restoration, etc in the area of artificial intelligence and cognition.
Breast cancer is one of the leading causes to women mortality in the world. Cluster of Microcalcifications (MCC) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and...
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ISBN:
(纸本)9783642022661
Breast cancer is one of the leading causes to women mortality in the world. Cluster of Microcalcifications (MCC) in mammograms can be an important early sign of breast cancer, the detection is important to prevent and treat the disease. In this paper, we present a novel method for the detection of MCC in mammograms which consists of image enhancement by histogram adaptive equalization technique, MCC edge detection by Coordinate Logic Filters (CLF), generation, clustering and labelling of suboptimal features vectors by means of Self Organizing Map (SOM) neural Network. Like comparison we applied an unsupervised clustering K-means in the stage of labelling of our method. In the labelling stage, we obtain better results with the proposed SOM neural Network compared with the k-means algorithm. Then, we show that the proposed method can locate MCCs in an efficient way.(1)
DNA computing is a new computing paradigm which uses biomolecules as information storage media and biochemical tools as information processing operators. This field has shown many successful and promising results for ...
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ISBN:
(纸本)9783642024801
DNA computing is a new computing paradigm which uses biomolecules as information storage media and biochemical tools as information processing operators. This field has shown many successful and promising results for various applications. Since DNA reactions are probabilistic in nature, different result could be produced even in the same Situations, which can be regarded as errors in computing. In order to overcome the drawbacks, many works have focused on the design or error-minimized DNA sequence to improve the reliability of DNA computing. Although the design of DNA sequences is dependent on the protocol of biological experiments, it is highly required to establish a method for the systematic design of DNA sequences, which could be applied to various design constraints. In the previous paper, Ant System approach has been proposed to solve the DNA sequence optimization problem. In this paper, the optimized parameters of Ant System approach are searched to improve the performance of the Ant System for DNA sequence optimization.
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