Catálogo de publicaciones - libros
Advances in Natural Computation: 1st International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I
Lipo Wang ; Ke Chen ; Yew Soon Ong (eds.)
En conferencia: 1º International Conference on Natural Computation (ICNC) . Changsha, China . August 27, 2005 - August 29, 2005
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision; Computation by Abstract Devices; Algorithm Analysis and Problem Complexity; Pattern Recognition; Evolutionary Biology
Disponibilidad
Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
---|---|---|---|---|
No detectada | 2005 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-3-540-28323-2
ISBN electrónico
978-3-540-31853-8
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2005
Información sobre derechos de publicación
© Springer-Verlag Berlin Heidelberg 2005
Tabla de contenidos
doi: 10.1007/11539087_120
Dynamic Inputs and Attraction Force Analysis for Visual Invariance and Transformation Estimation
Tomás Maul; Sapiyan Baba; Azwina Yusof
This paper aims to tackle two fundamental problems faced by multiple object recognition systems: invariance and transformation estimation. A neural normalization approach is adopted, which allows for the subsequent incorporation of invariant features. Two new approaches are introduced: dynamic inputs (DI) and attraction force analysis (AFA). The DI concept refers to a cloud of inputs that is allowed to change its configuration in order to latch onto objects thus creating object-based reference frames. AFA is used in order to provide clouds with transformation estimations thus maximizing the efficiency with which they can latch onto objects. AFA analyzes the length and angular properties of the correspondences that are found between stored-patterns and the information conveyed by clouds. The solution provides significant invariance and useful estimations pertaining to translation, scale, rotation and combinations of these. The estimations provided are also considerably resistant to other factors such as deformation, noise, occlusion and clutter.
- Cognitive Science | Pp. 893-902
doi: 10.1007/11539087_122
Robust Face Recognition from One Training Sample per Person
Weihong Deng; Jiani Hu; Jun Guo
This paper proposes a Gabor-based PCA method using Whiten Cosine Similarity Measure (WCSM) for Face Recognition from One training Sample per Person. Gabor wavelet representation of face images first derives desirable features, which is robust to the variations due to illumination, facial expression changes. PCA is then employed to reduce the dimensionality of the Gabor features. Whiten Cosine Similarity Measure is finally proposed for classification to integrate the virtues of the whiten translation and the cosine similarity measure. The effectiveness and robustness of the proposed method are successfully tested on CAS-PEAL dataset using one training sample per person, which contains 6609 frontal images of 1040 subjects. The performance enhancement power of the Gabor-based PCA feature and WCSM is shown in term of comparative performance against PCA feature, Mahalanobis distance and Euclidean distance. In particular, the proposed method achieves much higher accuracy than the standard Eigenface technique in our large-scale experiment.
- Cognitive Science | Pp. 915-924
doi: 10.1007/11539087_124
Modeling Human Learning as Context Dependent Knowledge Utility Optimization
Toshihiko Matsuka
Humans have the ability to flexibly adjust their information processing strategy according to situational characteristics. However, such ability has been largely overlooked in computational modeling research in high-order human cognition, particularly in learning. The present work introduces frameworks of cognitive models of human learning that take contextual factors into account. The framework assumes that human learning processes are not strictly error minimization, but optimization of knowledge. A simulation study was conducted and showed that the present framework successfully replicated observed psychological phenomena.
- Cognitive Science | Pp. 933-946
doi: 10.1007/11539087_125
Automatic Text Summarization Based on Lexical Chains
Yanmin Chen; Xiaolong Wang; Yi Guan
The method of lexical chains is the first time introduced to generate summaries from Chinese texts. The algorithm which computes lexical chains based on the HowNet knowledge database is modified to improve the performance and suit Chinese summarization. Moreover, the construction rules of lexical chains are extended, and relationship among more lexical items is used. The algorithm constructs lexical chains first, and then strong chains are identified and significant sentences are extracted from the text to generate the summary. Evaluation results show that the performance of the system has a notable improvement both in precision and recall compared to the original system.
- Cognitive Science | Pp. 947-951
doi: 10.1007/11539087_126
A General fMRI Linear Convolution Model Based Dynamic Characteristic
Hong Yuan; Hong Li; Zhijie Zhang; Jiang Qiu
General linear model (GLM) is a most popularly method of functional magnetic imaging (fMRI) data analysis. The key of this model is how to constitute the design-matrix to model the interesting effects better and separate noises. In this paper, the new general linear convolution model is proposed by introducing dynamic characteristic function as hemodynamic response function for the processing of the fMRI data. The method is implemented by a new dynamic function convolving with stimulus pattern as design-matrix to detect brain active signal. The efficiency of the new method is confirmed by its application into the real-fMRI data. Finally, real- fMRI tests showed that the excited areas evoked by a visual stimuli are mainly in the region of the primary visual cortex.
- Cognitive Science | Pp. 952-955
doi: 10.1007/11539087_127
A KNN-Based Learning Method for Biology Species Categorization
Yan Dang; Yulei Zhang; Dongmo Zhang; Liping Zhao
This paper presents a novel approach toward high precision biology species categorization which is mainly based on KNN algorithm. KNN has been successfully used in natural language processing (NLP). Our work extends the learning method for biological data. We view the DNA or RNA sequences of certain species as special natural language texts. The approach for constructing composition vectors of DNA and RNA sequences is described. A learning method based on KNN algorithm is proposed. An experimental system for biology species categorization is implemented. Forty three different bacteria organisms selected randomly from EMBL are used for evaluation purpose. And the preliminary experiments show promising results on precision.
- Neuroscience Informatics, Bioinformatics, and Bio-medical Engineering | Pp. 956-964
doi: 10.1007/11539087_129
Nonlinear Kernel MSE Methods for Cancer Classification
L. Shen; E. C. Tan
Combination of kernel PLS (KPLS) and kernel SVD (KSVD) with minimum-squared-error (MSE) criteria has created new machine learning methods for cancer classification and has been successfully applied to seven publicly available cancer datasets. Besides the high accuracy of the new methods, very fast training speed is also obtained because the matrix inversion in the original MSE procedure is avoided. Although the KPLS-MSE and the KSVD-MSE methods have equivalent accuracies, the KPLS achieves the same results using significantly less but more qualitative components.
- Neuroscience Informatics, Bioinformatics, and Bio-medical Engineering | Pp. 975-984
doi: 10.1007/11539087_131
A New Algorithm of Multi-modality Medical Image Fusion Based on Pulse-Coupled Neural Networks
Wei Li; Xue-feng Zhu
In this paper, a new multi-modality medical image fusion algorithm based on pulse-coupled neural networks (PCNN) is presented. Firstly a multi-scale decomposition on each source image is performed, and then the PCNN is used to combine these decomposition coefficients. Finally an inverse multi-scale transform is taken upon the new fused coefficients to reconstruct fusion image. The new algorithm utilizes the global feature of source images because the PCNN has the global couple and pulse synchronization characteristics. Series of experiments are performed about multi-modality medical images fusion such as CT/MRI, CT/SPECT, MRI/PET, etc. The experimental results show that the new algorithm is very effective and provides a good performance in fusing multi-modality medical images.
- Neuroscience Informatics, Bioinformatics, and Bio-medical Engineering | Pp. 995-1001
doi: 10.1007/11539087_133
Prediction Rule Generation of MHC Class I Binding Peptides Using ANN and GA
Yeon-Jin Cho; Hyeoncheol Kim; Heung-Bum Oh
A new method is proposed for generating rules to predict peptide binding to class I MHC proteins, from the amino acid sequence of any protein with known binders and non-binders. In this paper, we present an approach based on artificial neural networks (ANN) and knowledge-based genetic algorithm (KBGA) to predict the binding of peptides to MHC class I molecules. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution. Experimental results show that the method could generate new rules for MHC class I binding peptides prediction.
- Neuroscience Informatics, Bioinformatics, and Bio-medical Engineering | Pp. 1009-1016
doi: 10.1007/11539087_135
Automatic Liver Segmentation of Contrast Enhanced CT Images Based on Histogram Processing
Kyung-Sik Seo; Hyung-Bum Kim; Taesu Park; Pan-Koo Kim; Jong-An Park
Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of neighboring organs in the abdomen. In this paper, an automatic liver segmentation method using histogram processing is proposed for overcoming randomness of CE-CT images and removing other abdominal organs. Forty CE-CT slices of ten patients were selected to evaluate the proposed method. As the evaluation measure, the normalized average area and area error rate were used. From the results of experiments, liver segmentation using histogram process has similar performance as the manual method by medical doctor.
- Neuroscience Informatics, Bioinformatics, and Bio-medical Engineering | Pp. 1027-1030