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Protein-protein interactions play a pivotal role in modern molecular biology. Identifying the protein-protein interaction sites is great scientific and practical interest for predicting protein-protein interactions. In this study, we proposed a Gaussian Evolutionary Method (GEM) to optimize 18 features, including ten atomic solvent and eight protein 2nd structure features, for predicting protein-protein interaction sites. The training set consists of 104 unbound proteins selected from PDB and the predicted successful rate is 65.4% (68/104) proteins in the training dataset. These 18 parameters were then applied to a test set with 50 unbound proteins. Based on the threshold obtained from the training set, our method is able to predict the binding sites for 98% (49/50) proteins and yield 46% successful prediction and 42.3% average specificity. Here, a binding-site prediction is considered successful if 50% predicted area is indeed located in protein-protein interface (i.e. the specificity is more than 0.5). We believe that the optimized parameters of our method are useful for analyzing protein-protein interfaces and for interfaces prediction methods and protein-protein docking methods. [ABSTRACT FROM AUTHOR]

The annotation of transcription binding sites in new sequenced genomes is an important and challenging problem. We have previously shown how a regression model that linearly relates gene expression levels to the matching scores of nucleotide patterns allows us to identify DNA-binding sites from a collection of co-regulated genes and their nearby non-coding DNA sequences. Our methodology uses Bayesian models and stochastic search techniques to select transcription factor binding site candidates. Here we show that this methodology allows us to identify binding sites in nearby species. We present examples of annotation crossing from Schizosaccharomyces pombe to Schizosaccharomyces japonicus. We found that the eng1 motif is also regulating a set of 9 genes in S. japonicus. Our framework may have an effective interest in conveying information in the annotation process of a new species. Finally we discuss a number of statistical and biological issues related to the identification of binding sites through covariates of genes expression and sequences. [ABSTRACT FROM AUTHOR]

The Bio-basis Function Neural Network (BBFNN) is a novel neural architecture for peptide classification that makes use of amino acid mutation matrices and a similarity function to model protein peptide data without encoding. This study presents an Evolutionary Bio-basis network (EBBN), an extension to the BBFNN that uses a self adapting Evolution Strategy to optimise a problem specific substitution matrix for much improved model performance. The EBBN is assessed against BBFNN and multi layer perceptron (MLP) models using three datasets covering cleavage sites, epitope sites, and glycoprotein linkage sites. The method exhibits statistically significant improvements in performance for two of these sets. [ABSTRACT FROM AUTHOR]

"Quorum sensing" has been described as "the most consequential molecular microbiology story of the last decade" [1][2]. The purpose of this paper is to study the mechanism of quorum sensing, in order to obtain a deeper understanding of how and when this mechanism works. Our study focuses on the use of an Individual-based Modeling (IbM) method to simulate this phenomenon of "cell-to-cell communication" incorporated in bacterial foraging behavior, in both intracellular and population scales. The simulation results show that this IbM approach can reflect the bacterial behaviors and population evolution in time-varying environments, and provide plausible answers to the emerging question regarding to the significance of this phenomenon of bacterial foraging behaviors. [ABSTRACT FROM AUTHOR]

The ability to measure the transcriptional response after a stimulus has drawn much attention to the underlying gene regulatory networks. Here, we evaluate the application of methods to reconstruct gene regulatory networks by applying them to the SOS response of E. coli, the budding yeast cell cycle and in silico models. For each network we define an a priori validation network, where each interaction is justified by at least one publication. In addition to the existing methods, we propose a SVD based method (NSS). Overall, most reconstruction methods perform well on in silico data sets, both in terms of topological reconstruction and predictability. For biological data sets the application of reconstruction methods is suitable to predict the expression of genes, whereas the topological reconstruction is only satisfactory with steady-state measurements. Surprisingly, the performance measured on in silico data does not correspond with the performance measured on biological data. [ABSTRACT FROM AUTHOR]

In human mesenchymal stem cells the envelope surrounding the nucleus, as visualized by the nuclear lamina, has a round and flat shape. The lamina structure is considerably deformed after activation of cell death (apoptosis). The spatial organization of the lamina is the initial structural change found after activation of the apoptotic pathway, therefore can be used as a marker to identify cells activated for apoptosis. Here we investigated whether the spatial changes in lamina spatial organization can be recognized by machine learning algorithms to classify normal and apoptotic cells. Classical machine learning algorithms were applied to classification of 3D image sections of nuclear lamina proteins, taken from normal and apoptotic cells. We found that the Evolutionary-optimized Support Vector Machine (SVM) algorithm succeeded in the classification of normal and apoptotic cells in a highly satisfying result. This is the first time that cells are classified based on lamina spatial organization using the machine learning approach. We suggest that this approach can be used for diagnostic applications to classify normal and apoptotic cells. [ABSTRACT FROM AUTHOR]

Directed search methods and probabilistic approaches have been used as two alternative ways for computational protein design. This paper presents a hybrid methodology that combines features from both approaches. Three estimation of distribution algorithms are applied to the solution of a protein design problem by minimization of contact potentials. The combination of probabilistic models able to represent probabilistic dependencies with the use of information about residues interactions in the protein contact graph is shown to improve the efficiency of search for the problems evaluated. [ABSTRACT FROM AUTHOR]

Although important contributions have been made in recent years within the field of bioprocess model development and validation, in many cases the utility of even relatively good models for process optimization with current state-of-the-art algorithms (mostly offline approaches) is quite low. The main cause for this is that open-loop fermentations do not compensate for the differences observed between model predictions and real variables, whose consequences can lead to quite undesirable consequences. In this work, the performance of two different algorithms belonging to the main groups of Evolutionary Algorithms (EA) and Differential Evolution (DE) is compared in the task of online optimisation of fed-batch fermentation processes. The proposed approach enables to obtain results close to the ones predicted initially by the mathematical models of the process, deals well with the noise in state variables and exhibits properties of graceful degradation. When comparing the optimization algorithms, the DE seems the best alternative, but its superiority seems to decrease when noisier settings are considered. [ABSTRACT FROM AUTHOR]

A common way to characterise important and conserved signals in nucleotide sequences, such as transcription factor binding sites, is via the use of so-called consensus sequences or consensus patterns. A well-known example is the so-called "TATA-box" commonly found in eukaryotic core promoters. Such patterns are valuablein that they offer an insight into basic molecular biology processes, and can support reasoning regarding the understanding, design and control of these processes. However it is rare for such patterns to be accurate; instead they represent a very approximate characterisation of the signal under study. At the opposite extreme, we may instead characterise such a signal via a neural network, or a high-order Markov model, and so on. These have better sensitivity and specificity, but are unreadable, and consequently unhelpful for conveying an understanding of the underlying molecular biology processes that could support insight or reasoning. We describe a simple pattern language, called crisp hypermotifs (CHMs), that leads to highly readable patterns that can support understanding and reasoning, yet achieve greater sensitivity and specificity than the commonly used approaches to crisply characterise a signal. We use evolutionary computation to discover high-performance CHMs from data, and we argue that CHMs be used in place of classical consensus motifs, and justify that by presenting examples derived from a large dataset of mammalian core promoters. We provide CHM alternatives to the well-known core promoter TATA-box and Initiator patterns that have better sensitivity and specificity than their classical counterparts. [ABSTRACT FROM AUTHOR]

Many heuristics used for finding biclusters in microarray data use the mean squared residue as a way of evaluating the quality of biclusters. This has led to the discovery of interesting biclusters. Recently it has been proven that the mean squared residue may fail to identify some interesting biclusters. This motivates us to introduce a new measure, called Virtual Error, for assessing the quality of biclusters in microarray data. In order to test the validity of the proposed measure, we include it within an evolutionary algorithm. Experimental results show that the use of this novel measure is effective for finding interesting biclusters, which could not have been discovered with the use of the mean squared residue. [ABSTRACT FROM AUTHOR]

Inverse protein folding or protein design stands for searching a particular amino acids sequence whose native structure or folding matches a pre specified target. The problem of finding the corresponding folded structure of a particular sequence is, per se, a hard computational problem. We use a genetic algorithm for searching the space of potential sequences, and the fitness of each individual is measured with the output of a second GA performing a minimization process in the space of structures. Using an off-lattice protein-like 2D model, we show how the implemented techniques are able to obtain a variety of sequences attaining the target structures proposed. [ABSTRACT FROM AUTHOR]

The Human Immunodeficiency Virus (HIV) encodes an enzyme, called HIV protease, which is responsible for the generation of infectious viral particles by cleaving the virus polypeptides. Many efforts have been devoted to perform accurate predictions on the HIV-protease cleavability of peptides, in order to design efficient inhibitor drugs. Over the last decade, linear and nonlinear supervised learning methods have been extensively used to discriminate between protease-cleavable and non cleavable peptides. In this paper we consider four different proteins encoding schemes and we apply a discrete variant of linear support vector machines to predict their HIV protease-cleavable status. Empirical results indicate the effectiveness of the proposed method, that is able to classify with the highest accuracy the cleavable and non cleavable peptides contained in two publicly available benchmark datasets. Moreover, the optimal classification rules generated are characterized by a strong generalization capability, as shown by their accuracy in predicting the HIV protease cleavable status of peptides in out-of-sample datasets. [ABSTRACT FROM AUTHOR]

Knowledge of protein-protein interaction sites is vital to determine proteins' function and involvement in different pathways. Support Vector Machines (SVM) have been proposed over the recent years to predict protein-protein interface residues, primarily based on single amino acid sequence inputs. We investigate the features of amino acids that can be best used with SVM for predicting residues at protein-protein interfaces. The optimal feature set was derived from investigation into features such as amino acid composition, hydrophobic characters of amino acids, secondary structure propensity of amino acids, accessible surface areas, and evolutionary information generated by PSI-BLAST profiles. Using a backward elimination procedure, amino acid composition, accessible surface areas, and evolutionary information generated by PSI-BLAST profiles gave the best performance. The present approach achieved overall prediction accuracy of 74.2% for 77 individulal proteins collected from the Protein Data Bank, which is better than the previously reported accuracies. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) form a large functional family of small noncoding RNAs and play an important role as posttranscriptional regulators, by repressing the translation of mRNAs. Recently, the processing mechanism of miRNAs has been reported to involve Drosha/DGCR8 complex and Dicer, however, the exact mechanism and molecular principle are still unknown. We thus have tried to understand the related phenomena in terms of the tertiary structure of pre-miRNA. Unfortunately, the tertiary structure of RNA double helix has not been studied sufficiently compared to that of DNA double helix. The tertiary structure of pre-miRNA double helix is determined by 15 types of dinucleotide step (d-step) parameters for three classes of angles, i.e., twist, roll, and tilt. In this study, we estimate the 45 d-step parameters (15 types by 3 classes) using an evolutionary algorithm, under several assumptions inferred from the literature. Considering the trade-off among the four objective functions in our study, we deployed a multi-objective evolutionary algorithm, NSGA-II, to the search for a nondominant set of parameters. The performance of our method was evaluated on a separate test dataset. Our study provides a novel approach to understanding the processing mechanism of pre-miRNAs with respect to their tertiary structure and would be helpful for developing a comprehensible prediction method for pre-miRNA and mature miRNA structures. [ABSTRACT FROM AUTHOR]

An important goal of human genetics is the identification of DNA sequence variations that are predictive of who is at risk for various common diseases. The focus of the present study is on the challenge of detecting and characterizing nonlinear attribute interactions or dependencies in the context of a genome-wide genetic study. The first question we address is whether the ReliefF algorithm is suitable for attribute selection in this domain. The second question we address is whether we can improve ReliefF for selecting important genetic attributes. Using simulated genetic datasets, we show that ReliefF is significantly better than a naïve chi-square test of independence for selecting two interacting attributes out of 103 candidates. In addition, we show that ReliefF can be improved in this domain by systematically removing the worst attributes and re-estimating ReliefF weights. Our simulation studies demonstrate that this new Tuned ReliefF (TuRF) algorithm is significantly better than ReliefF. [ABSTRACT FROM AUTHOR]

The effective reverse engineering of biochemical networks is one of the great challenges of systems biology. The contribution of this paper is two-fold: 1) We introduce a new method for reverse engineering genetic regulatory networks from gene expression data; 2) We demonstrate how nonlinear gene networks can be inferred from steady-state data alone. The reverse engineering method is based on an evolutionary algorithm that employs a novel representation called Analog Genetic Encoding (AGE), which is inspired from the natural encoding of genetic regulatory networks. AGE can be used with biologically plausible, nonlinear gene models where analytical approaches or local gradient based optimisation methods often fail. Recently there has been increasing interest in reverse engineering linear gene networks from steady-state data. Here we demonstrate how more accurate nonlinear dynamical models can also be inferred from steady-state data alone. [ABSTRACT FROM AUTHOR]

Systems biology is the ever-growing field of integrating molecular knowledge about biological organisms into an understanding at the systems level. For this endeavour, automatic network reconstruction tools are urgently needed. In the present contribution, we show how the applicability of evolutionary algorithms to systems biology can be improved by a domain-specific representation and algorithmic extensions, especially a separation of network structure evolution from evolution of kinetic parameters. In a case study, our presented tool is applied to a model of the mitotic spindle checkpoint in the human cell cycle. [ABSTRACT FROM AUTHOR]

A path diagram relates observed, pairwise, variable correlations to a functional structure which describes the hypothesized causal relations between the variables. Here we combine path diagrams, heuristics and evolutionary search into a system which seeks to improve existing gene regulatory models. Our evaluation shows that once a correct model has been identified it receives a lower prediction error compared to incorrect models, indicating the overall feasibility of this approach. However, with smaller samples the observed correlations gradually become more misleading, and the evolutionary search increasingly converges on suboptimal models. Future work will incorporate publicly available sources of experimentally verified biological facts to computationally suggest model modifications which might improve the model's fitness. [ABSTRACT FROM AUTHOR]

In some areas of bioinformatics (like protein folding or sequence alignment) the full alphabet of amino acid symbols is not necessary. Often, better results are received with simplified alphabets. In general, simplified alphabets are as universal as possible. In this paper we show that this concept may not be optimal. We present a genetic algorithm for alphabet simplifying and we use it in a method based on global sequence alignment. We demonstrate that our algorithm is much faster and produces better results than the previously presented genetic algorithm. We also compare alphabets constructed on the base of universal substitution matrices like BLOSUM with our alphabets built through sequence alignment and propose a new coefficient describing the value of alphabets in the sequence alignment context. Finally we show that our simplified alphabets give better results in a sequence classification (using k-NN classifier), than most previously presented simplified alphabets and better than full 20-letter alphabet. [ABSTRACT FROM AUTHOR]

Understanding the self-regulatory mechanisms controlling the spatial and temporal structure of multicellular organisms represents one of the major challenges in molecular biology. In the context of plants, shoot apical meristems (SAMs), which are populations of dividing, undifferentiated cells that generate organs at the tips of stems and branches throughout the life of a plant, are of particular interest and currently studied intensively. Here, one key goal is to identify the genetic regulatory network organizing the structure of a SAM and generating the corresponding spatial gene expression patterns. This paper addresses one step in the design of SAM models based on ordinary differential equations (ODEs): parameter estimation for spatial pattern formation. We assume that the topology of the genetic regulatory network is given, while the parameters of an ODE system need to be determined such that a particular stable pattern over the SAM cell population emerges. To this end, we propose an evolutionary algorithm-based approach and investigate different ways to improve the efficiency of the search process. Preliminary results are presented for the Brusselator, a well-known reaction-diffusion system. [ABSTRACT FROM AUTHOR]

Classification of microarray data requires the selection of subsets of relevant genes in order to achieve good classification performance. This article presents a genetic embedded approach that performs the selection task for a SVM classifier. The main feature of the proposed approach concerns the highly specialized crossover and mutation operators that take into account gene ranking information provided by the SVM classifier. The effectiveness of our approach is assessed using three well-known benchmark data sets from the literature, showing highly competitive results. [ABSTRACT FROM AUTHOR]

Biomedical research has been revolutionized by high-throughput techniques and the enormous amount of biological data they are able to generate. The interest shown over network models and systems biology is rapidly raising. Genetic networks arise as an essential task to mine these data since they explain the function of genes in terms of how they influence other genes. Many modeling approaches have been proposed for building genetic networks up. However, it is not clear what the advantages and disadvantages of each model are. There are several ways to discriminate network building models, being one of the most important whether the data being mined presents a static or dynamic fashion. In this work we compare static and dynamic models over a problem related to the inflammation and the host response to injury. We show how both models provide complementary information and cross-validate the obtained results. [ABSTRACT FROM AUTHOR]

A critical challenge of the postgenomic era is to understand how genes are differentially regulated in and between genetic networks. The fact that such co-regulated genes may be differentially regulated suggests that subtle differences in the shared cis-acting regulatory elements are likely significant, however it is unknown which of these features increase or reduce expression of genes. In principle, this expression can be measured by microarray experiments, though they incorporate systematic errors, and moreover produce a limited classification (e.g. up/down regulated genes). In this work, we present an unsupervised machine learning method to tackle the complexities governing gene expression, which considers gene expression data as one feature among many. It analyzes features concurrently, recognizes dynamic relations and generates profiles, which are groups of promoterssharing common features. The method makes use of multiobjective techniques to evaluate the performance of profiles, and has a multimodal approach to produce alternative descriptions of same expression target. We apply this method to probe the regulatory networks governed by the PhoP/PhoQ two-component system in the enteric bacteria Escherichia coli and Salmonella enterica. Our analysis uncovered profiles that were experimentally validated, suggesting correlations between promoter regulatory features and gene expression kinetics measured by green fluorescent protein (GFP) assays. [ABSTRACT FROM AUTHOR]

Protein translocation, the transport of newly synthesized proteins out of the cell, is a fundamental mechanism of life. We are interested in understanding how cells recognize the proteins that are to be exported and how the necessary information is encoded in the so called "Signal Sequences". In this paper, we address these problems by building a physico-chemical model of signal sequence recognition, using experimental data. This model was built using decision trees. In a first phase the classifier were built from a set of features derived from the current knowledge about signal sequences. It was then expanded by feature generation with genetic algorithms. The resulting predictors are efficient, achieving an accuracy of more than 99% with our wild-type proteins set. Furthermore the generated features can give us a biological insight about the export mechanism. Our tool is freely available through a web interface. [ABSTRACT FROM AUTHOR]

We propose a feature selection method for multiclass classification. The proposed method selects features in backward elimination and computes feature ranking scores at each step from analysis of weight vectors of multiple two-class linear Support Vector Machine classifiers from one-versus-one or one-versus-all decomposition of a multi-class classification problem. We evaluated the proposed method on three gene expression datasets for multiclass cancer classification. For comparison, one filtering feature selection method was included in the numerical study. The study demonstrates the effectiveness of the proposed method in selecting a compact set of genes to ensure a good classification accuracy. [ABSTRACT FROM AUTHOR]

In liquid chromatography-mass spectrometry (LC-MS) based expression proteomics, samples from different groups are analyzed comparatively in order to detect differences that can possibly be caused by the disease under study (potential biomarker detection). To this end, advanced computational techniques are needed. Peak alignment and detection are two key steps in the analysis process of LC-MS datasets. In this paper we propose an algorithm for LC-MS peak detection and alignment. The goal of the algorithm is to group together peaks generated by the same peptide but detected in different samples. It employs clustering with a new weighted similarity measure and automatic selection of the number of clusters. Moreover, it supports parallelization by acting on blocks. Finally, it allows incorporation of available domain knowledge for constraining and refining the search for aligned peaks. Application of the algorithm to a LC-MS dataset generated by a spike-in experiment substantiates the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Analysis of medical datasets has some specific requirements not always fulfilled by standard Machine Learning methods. In particular, heterogeneous and missing data must be tolerated, the results should be easily interpretable. Moreover, with genetic data, often the combination of two or more attributes leads to non-linear effects not detectable for each attribute on its own. We present a new ML algorithm, HCS, taking inspiration from learning classifier systems, decision trees and statistical hypothesis testing. We show the results of applying this algorithm to a well-known benchmark dataset, and to HNSCC, a dataset studying the connection between smoke and genetic patterns to the development of oral cancer. [ABSTRACT FROM AUTHOR]

Computational methods allowing reliable pharmacokinetics predictions for newly synthesized compounds are critically relevant for drug discovery and development. Here we present an empirical study focusing on various versions of Genetic Programming and other well known Machine Learning techniques to predict Median Oral Lethal Dose (LD50) and Plasma Protein Binding (%PPB) levels. Since these two parameters respectively characterize the harmful effects and the distribution into human body of a drug, their accurate prediction is essential for the selection of effective molecules. The obtained results confirm that Genetic Programming is a promising technique for predicting pharmacokinetics parameters, both from the point of view of the accurateness and of the generalization ability. [ABSTRACT FROM AUTHOR]

This study uses object-extracting technique to extract thumb, index, middle, ring, and small fingers. The algorithm developed in this study can locate the precise locations of fingertips and finger-to-finger-valleys. The extracted fingers contain many useful geometry features. One can use these features to identify fingers. The geometry descriptor is used to transfer geometry features of fingers to another feature-domains. Fingers are scaled to allow fingers possess more salient features. Finger is also magnified by the basis of "distance multiplying gray level". After finger magnifying, finger will possess more salient feature. Image subtraction is used to examine the difference of the two fingers. [ABSTRACT FROM AUTHOR]

In this paper we present a method for fast energy minimization of virtual garments. Our method is based upon the idea of multi-resolution particle system. When garments are approximately positioned around a virtual character, their spring energy may be high, which will cause instability or at least long execution time of the simulation. An energy minimization algorithm is needed; if a fixed resolution is used, it will require many iterations to reduce its energy. Even though the complexity of each iteration is O(n), with a high resolution mass-spring system, this minimization process can take a whole day. The hierarchical method presented in this paper is used to reduce significantly the execution time of the minimization process. The garments are firstly discretized in several resolutions. Once the lowest resolution particles system is minimized (in a short time), a higher resolution model is derived, then minimized. The procedure is iterated up to the highest resolution. But at this stage, the energy to minimize is already much lower so that minimization takes a reasonable time. [ABSTRACT FROM AUTHOR]

Chinese calligraphy is a precious Chinese art. It is pictographic and each calligraphist has his own writing style. Even people whose native language is Chinese will have difficulties in writing a demanded beautiful calligraphy style, not to say people who know little about Chinese calligraphy. In order to help people enjoy the art of calligraphy writing and find out the process how it was written, we implement a new approach to animate its writing process by 3D visualization method. In the paper two novel algorithms are also presented to extract the intrinsic feature which is needed to rebuild the writing process: 1) extract strokes order from an offline Chinese calligraphic handwriting; 2) estimate varied stroke's thickness. Finally, experiment result is given to demonstrate the application. [ABSTRACT FROM AUTHOR]

We present a novel FPGA-accelerated architecture for fast collision detection among rigid bodies. This paper describes the design of the hardware architecture for several primitive intersection testing components implemented on a multi-FPGA Xilinx Virtex-II prototyping system. We focus on the acceleration of ray-triangle intersection operation which is the one of the most important operations in various applications such as collision detection and ray tracing. Our implementation result is a hardware-accelerated ray-triangle intersection engine that is capable of out-performing a 2.8 GHz Xeon processor, running a well-known high performance software ray-triangle intersection algorithm, by up to a factor of seventy. In addition, we demonstrate that the proposed approach could prove to be faster than current GPU-based algorithms as well as CPU based algorithms for ray-triangle intersection. [ABSTRACT FROM AUTHOR]

This paper describes a novel discrimination method of lung cancers based on statistical analysis of thoracic computed tomography (CT) scans. Our previous Computer-Aided Diagnosis (CAD) system can detect lung cancers from CT scans, but, at the same time, yields many false positives. In order to reduce the false positives, the method proposed in the present paper uses a relationship between lung cancers, false positives and image information on CT scans. The trend of variation of the relationships is acquired through statistical analysis of a set of CT scans prepared for training. In testing, by use of the trend, the method predicts the appearance of lung cancers and false positives in a CT scan, and improves the accuracy of the previous CAD system by modifying the system's output based on the prediction. The method is applied to 218 actual thoracic CT scans with 386 actual lung cancers. Receiver operating characteristic (ROC) analysis is used to evaluate the results. The area under the ROC curve (Az) is statistically significantly improved from 0.918 to 0.931. [ABSTRACT FROM AUTHOR]

We describe a novel eye detection method that is robust to the obstacles such as surrounding illumination, hair, and eye glasses. The obstacles above a face image are constraints to detect eye position. These constraints affect the performance of the face applications such as face recognition, gaze tracking, and video indexing systems. To overcome this problem, the proposed method for eye detection consists of three steps. First, the self quotient images are applied to the face images by rectifying illumination. Then, unnecessary pixels for eye detection are removed by using the symmetry object filter. Next, the eye candidates are extracted by using the gradient descent which is a simple and a fast computing method. Finally, the classifier, which has trained by using AdaBoost algorithm, selects the eyes from all of the eye candidates. The usefulness of the proposed method has been demonstrated in an embedded system with the eye detection performance. [ABSTRACT FROM AUTHOR]

In this paper we present an overview of our novel line segment based watershed segmentation algorithm. Most of the existing watershed algorithms use the region label image as the main data structure for its ease of use. These type of watershed algorithms have a relatively large memory footprint and need unbounded memory access. For our new watershed algorithm we replaced the traditional region label image with a data structure that stores the regions in linked line segments. Consequently, the new algorithm has a much smaller memory footprint. Using the new data structure also makes it possible to create an efficient algorithm that only needs one scan over the input image and that only needs the last 3 lines and a small part of the data structure in memory. [ABSTRACT FROM AUTHOR]

This paper presents a new content based image retrieval approach based on histogram of pattern orientations, namely pattern orientation histogram (POH) method. POH represents the spatial distribution of five different pattern orientations: vertical, horizontal, diagonal down/left, diagonal down/right and non-orientation. In this method, a given image is first divided into image-blocks and frequency of each type of patterns is determined in each image-block. Then local pattern histograms for each of these image-blocks are computed. As a result, local pattern histograms are obtained for each image. We compared our method with one of the texture descriptors MPEG-7 standard. Experimental results demonstrate that POH leads to better precision and recall rates than edge histogram descriptor (EHD) of MPEG-7 standard for search and retrieval of digital imagery. [ABSTRACT FROM AUTHOR]

Digital 3D models of patients' organs or tissues are often needed for surgical planning and outcome evaluation, or to select prostheses adapted to patients' anatomy. Tissue classification is one of the hardest problems in automatic model generation from raw data. The existing solutions do not give reliable estimates of the accuracy of the resulting model. We propose a simple generative model using Gaussian Mixture Models (GMMs) to describe the likelihood functions involved in the computation of posterior probabilities. Multiscale feature descriptors are used to exploit the surrounding context of each element to be classified. Supervised learning is carried out using datasets manually annotated by expert radiologists. 3D models are generated from the binary volumetric models, obtained by labelling cortical bone pixels according to maximal likelihoods. [ABSTRACT FROM AUTHOR]

We describe an algorithm for fitting a Catmull-Clark subdivision surface model to an unstructured, incomplete and noisy data set. We complete the large missing data regions with the a-priori shape information and produce a smooth, compact and structured data description. The result can be used for further data manipulation, compression, or visualisation. Our fitting algorithm uses a quasi-interpolation technique which manipulates the base mesh of the subdivision model to achieve better approximation. We extend the approach designed for scientific visualisation and animation to deal with incomplete and noisy data and preserve prior shape constraints where data is missing. We illustrate the algorithm on range and stereo data with a set of different subdivision models and demonstrate the applicability of the method to the problem of novel view synthesis from incomplete stereo data. [ABSTRACT FROM AUTHOR]

In this paper, we have presented a fully automatic technique for detection and classification of the six basic facial expressions from nearly frontal face images. Facial expressions are communicated by subtle changes in one or more discrete features such as tightening the lips, raising the eyebrows, opening and closing of eyes or certain combinations of them. These discrete features can be identified through monitoring the changes in muscles movement (Action Units) located near about the regions of mouth, eyes and eyebrows. In this work, we have used eleven feature points that represent and identify the principle muscle actions as well as provide measurements of the discrete features responsible for each of the six basic human emotions. A multi-detector approach of facial feature point localization has been utilized for identifying these points of interests from the contours of facial components such as eyes, eyebrows and mouth. Feature vector composed of eleven features is then obtained by calculating the degree of displacement of these eleven feature points from a non-changeable rigid point. Finally, the obtained feature sets are used for training a K-Nearest Neighbor Classifier so that it can classify facial expressions when given to it in the form of a feature set. The developed Automatic Facial Expression Classifier has been tested on a publicly available facial expression database and on an average 90.76% successful classification rate has been achieved. [ABSTRACT FROM AUTHOR]

Color-based particle filter for object tracking has been an active research topic in recent years. Despite great efforts of many researchers, there still remains to be solved the problem of contradiction between efficiency and robustness. The paper makes an attempt to partially solve this problem. Firstly, the Integral Histogram Image is introduced by which histogram of any rectangle region can be computed at negligible cost. However, straightforward application of the Integral Histogram Images causes the problem of "curse of dimensionality". In addition, traditional histogram is inefficient and inaccurate. Thus we propose to adaptively determine histogram bins based on K-Means clustering, which can represent color distribution of object more compactly and accurately with as a small number of bins. Thanks to the Integral Histogram Images and the clustering based color histogram, we finally achieve a fast and robust particle filter algorithm for object tracking. Experiments show that the performance of the algorithm is encouraging. [ABSTRACT FROM AUTHOR]

This paper presents a method to suppress the bias artifact, also known as RF-inhomogeneity, in Magnetic Resonance Imaging (MRI). This artifact produces illumination variations due to magnetic field fluctuations of the device. In the latest years many works have been devoted to face this problem. In this work we present the 3D version of a new approach to bias correction, which is called Exponential Entropy Driven Homomorphic Unsharp Masking (E2D − HUM). This technique has been already presented by some of the authors for the 2D case only. The description of the whole method is detailed, and some experimental results are reported. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method that measures the height and stagger of an overhead electric-railway line using the stereo images. Two 1624 X 1236 pixel area scanner CCD cameras are used. To quickly and accurately extract, from a photographed image, the area of the overhead line on which the line laser is shone, we consider the established fact that the overhead line is the lowest among the electric wires. And to precisely measure the height and stagger in low resolution, sub-pixel and line fitting methods are used. Also, because of the different pixel resolution of the camera according to the overhead line position, we compensate the measurement result through camera calibration. We aimed for a measurement accuracy of 1mm error and indeed our experimental results show that the proposed method achieves that. [ABSTRACT FROM AUTHOR]

This paper presents a model-based approach to monocular tracking of human body using a non-calibrated camera. The tracking in monocular images is realized using a particle filter and an articulated 3D model with a cylinder-based representation of the body. In modeling the visual appearance of the person we employ appearance-adaptive models. The predominant orientation of the gradient combined with ridge cues provides strong orientation responses in the observation model of the particle filter. The phase that is measured using the Gabor filter contributes towards strong localization of the body limbs. The potential of our approach is demonstrated by tracking of the human body on real videos. [ABSTRACT FROM AUTHOR]

This paper presents a simple method for segmenting text image on the basis of color components. It is shown how segmentation can benefit from splitting color signals into chromatic and achromatic components and separately smoothing them by proposed clustering method. We analyze and compare the performance of several color components in terms of segmentation of the text regions from color natural scenes. We also perform a fast 1-dimensional k-means clustering algorithm. Therefore we can perform accurate object segmentation using both H and I components. And then, the effectiveness and reliability of proposed method are demonstrated through various natural scene images. The experimental results have proven that the proposed method is effective. [ABSTRACT FROM AUTHOR]

The following paper presents a method that allows for a parallel implementation of the most computationally expensive element of the deformable template paradigm, which is a grid-matching procedure. Cellular Neural Network Universal Machine has been selected as a framework for the task realization. A basic idea of deformable grid matching is to guide node location updates in a way that minimizes dissimilarity between an image and grid-recorded information, and that ensures minimum grid deformations. The proposed method provides a parallel implementation of this general concept and includes a novel approach to grid's elasticity modeling. The method has been experimentally verified using two different analog hardware environments, yielding high execution speeds and satisfactory processing accuracy. [ABSTRACT FROM AUTHOR]

For computation of the epipolar geometry from central- omni-directional images, the use of the spherical camera model is essential. This is because the central-omnidirectional cameras are universally expressed as the spherical camera model when the intrinsic parameters of the cameras are calibrated. Geometrically, for corresponding points between two spherical images, there exists the same epipolar constraint as the conventional pinhole-camera model. Therefore, it is possible to use the conventional eight-point algorithm for recovering camera motion and 3D objects from two spherical images. In this paper, using the geometric properties on rotation of the spheres, we propose a method of the accurate computation based on the rectification of the spherical-camera images via the conventional eight-point algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an automatic technique for natural image matting is proposed. We use visual characteristics of the background and foreground regions for matting. Through the Locally Linear Embedding (LLE) of high-dimensional features, we estimate foreground and background color of unknown pixels. We use gradient information and a hierarchical model to enhance the proposed matting technique. Instead of a user interaction for tri-map generation, we propose an automatic technique that obtains a tri-map using a multi-view camera. A reliability map for the depth of a scene facilitates the generation of a tri-map. It is proven that feature sets obtained from training images can be applied to similar images or video frames. [ABSTRACT FROM AUTHOR]

This paper presents a computerized bone age assessment method using discrete cosine transform (DCT) and Fisher's linear discriminant analysis (FLD or LDA). Bone age assessment using a radiograph of the left hand is a common procedure in pediatric radiology. In the proposed method, DCT and LDA are applied to the epiphyseal regions segmented from a radiograph of the left hand. The extracted LDA coefficients are compared with features stored in the database, and then the bone age of the given radiograph is estimated. In experiments on 396 radiographs of the left hand collected at Hanyang University Medical Center, the proposed method shows an average error of 0.6 years and an accuracy of 89.71%. [ABSTRACT FROM AUTHOR]

We present the design of face identification system that can run in real time environment. We use multiple contexts to optimize the face recognition performance in real time. Initially different illumination environments are modeled as context using unsupervised learning and accumulated as context knowledge. Optimization parameters for each context are learned using Genetic Algorithm (GA).GA search the optimization parameter so as to minimize the effect of illumination variation. These weight parameters are used during similarity match of face images in real time recognition. Gabor wavelet is used for facial feature representation. Experiment is done using real time face database containing images taken under various illumination conditions. The proposed context aware method has been shown to provide superior performance than the method without using context awareness. [ABSTRACT FROM AUTHOR]

The fixed mean shape that is built from the statistical shape model produces an erroneous feature extraction result when ASM is applied to multi-pose faces. To remedy this problem the mean shape vector which is similar to an input face image is needed. In this paper, we propose the adaptive mean shape to extract facial features accurately for non frontal face. It indicates the mean shape vector that is the most similar to the face form of the input image. Our experimental results show that the proposed method obtains feature point positions with high accuracy and significantly improving the performance of facial feature extraction over and above that of the original ASM. [ABSTRACT FROM AUTHOR]