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This paper is concerned with a multi-resolution tool for screening paper formation variations in various frequency regions on production line. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the series. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The tools developed are used for online paper formation monitoring in a paper mill. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions. [ABSTRACT FROM AUTHOR]

This paper describes a new method for removing geometric distortion in images of submerged objects observed from outside shallow water. We focus on the problem of analyzing video sequences when the water surface is disturbed by waves. The water waves will affect the appearance of the individual video frames such that no single frame is completely free of geometric distortion. This suggests that, in principle, it is possible to perform a selection of a set of low distortion sub-regions from each video frame and combine them to form a single undistorted image of the observed object. The novel contribution in this paper is to use a multi-stage clustering algorithm combined with frequency domain measurements that allow us to select the best set of undistorted sub-regions of each frame in the video sequence. We evaluate the new algorithm on video sequences created both in our laboratory, as well as in natural environments. Results show that our algorithm is effective in removing distortion caused by water motion. [ABSTRACT FROM AUTHOR]

In this paper, a complete theory of steerable filters is presented which shows that quadrature filters are only a special case of steerable filters. Although there has been a large number of approaches dealing with the theory of steerable filters, none of these gives a complete theory with respect to the transformation groups which deform the filter kernel. Michaelis and Sommer (Michaelis and Sommer, 1995) and Hel-Or and Teo (Teo and Hel-Or, 1996; Teo and Hel-Or, 1998) were the first ones who gave a theoretical justification for steerability based on Lie group theory. But the approach of Michaelis and Sommer considers only Abelian Lie groups. Although the approach of Hel-Or and Teo considers all Lie groups, their method for generating the basis functions may fail as shown in this paper. We extend these steerable approaches to arbitrary Lie groups, like the important case of the rotation group SO(3) in three dimensions. Quadrature filters serve for computing the local energy and local phase of a signal. Whereas for the one dimensional case quadrature filters are theoretically well founded, this is not the case for higher dimensional signal spaces. The monogenic signal (Felsberg and Sommer, 2001) based on the Riesz transformation has been shown to be a rotational invariant generalization of the analytic signal. A further generalization of the monogenic signal, the 2D rotational invariant quadrature filter (Köthe, 2003), has been shown to capture richer structures in images as the monogenic signal. We present a generalization of the rotational invariant quadrature filter based on our steerable theory. Our approach includes the important case of 3D rotational invariant quadrature filters but it is not limited to any signal dimension and includes all transformation groups that own a unitary group representation. [ABSTRACT FROM AUTHOR]

Painterly animation is a method that expresses images with a hand-painted appearance from a video, and the most crucial element for it is the coherence between frames. A motion map generation is proposed in this paper as a resolution to the issue of maintaining the coherence in the brush strokes between the frames. A motion map refers to the range of motion calculated by their magnitudes and directions between the frames with the edge of the previous frame as a point of reference. The different methods of motion estimation used in this paper include the optical flow method and the block-based method, and the method that yielded the biggest PSNR using the motion information (the directions and magnitudes) acquired by various methods of motion estimation has been chosen as the final motion information to form a motion map. The created motion map determined the part of the frame that should be re-painted. In order to maintain the temporal coherence, the motion information was applied to only the strong edges that determine the directions of the brush strokes. Also, this paper sought to reduce the flickering phenomenon between the frames by using the multiple exposure method and the difference map created by the difference between images of the source and the canvas. Maintenance of the coherence in the direction of the brush strokes was also attempted by a local gradient interpolation in an attempt to maintain the structural coherence. [ABSTRACT FROM AUTHOR]

Recently, almost uncontrolled technological progress allows so called high-throughput data collection for sophisticated and complex experimental biological systems analysis. Especially it concerns the whole cellular genome. Therefore it becomes more and more vital to suggest and elaborate gene network models, which can be used for more complete interpretation of large and complex data sets. The presented paper concerns modeling of yeast cell cycle. With the reference to the previously published papers concerning the same subject, our paper presents a significant improvement in calculation procedure leading to construction of an efficient algorithm for modeling of the mentioned cell cycle. Presented model concerns 12 selected genes and incorporates all possible types of interactions. [ABSTRACT FROM AUTHOR]

The uniqueness of a fingerprint allows to build systems that can recognize personal fingerprint feature automatically. In practice, fingerprint recognition and classification based on two techniques: the reference point location and the Gabor filtering or the fingerprint ridge characteristics (called minutiae). In this paper a new method of the reference point alignment has been presented. A new approach of reference point localization is based on so-called identification filters, which have been composed on the basis of analysis of biometric characteristic of human fingerprint. In this paper construction of such filters has been precisely explained. Proposed method with other methods was compared. [ABSTRACT FROM AUTHOR]

Paper is related to biometrics image compression (finger print and iris images) and refers to the earlier works related to the use of the interferometry images in non touch measurement techniques. The finger prints fringes have similar periodic nature similar to interfered waves, thus we applied a special kind of base functions to perform compression of exemplary images. The second stage of the paper deals with the iris decomposition i.e. a hybrid of Canny Edge detector stage and a Circular Hough Transform to establish iris boundaries and data encryption-compression by Gabor wavelets functions. To ensure correctness of the investigations - comparison to real criminals fingerprint database - searching and recognition case - has been tested. Eventually the comparison of iris templates on exemplary human eye datasets has been presented too. [ABSTRACT FROM AUTHOR]

This paper presents especially elaborated laboratory setup and computer software for visualizing the tongue surface movement and the changes of its shape using an ultrasonograph (USG). In the paper the acoustic properties of the tongue, the way of examination and the measurement set-up was described, as well. The elaborated setup allows us to computer-visualize and follow in real time the changes of the tongue shape in the process of vowel articulation, thus providing a visual feedback with the speaker. It can be one of the ways of treating speech defects and phoniatric rehabilitation in people with hearing disorders and pronunciation defects. [ABSTRACT FROM AUTHOR]

This paper presents the problem of diagnostic parameters quality control in the adaptive ECG interpretation system. The quality estimation is used for seamless adaptation of processing being a part of the optimization feedback. Therefore, quality control procedures - unlike in conventional systems - are embedded in the software. The proposed methodology is based on two principal concepts: diagnostic results priority depending on the patient status and the data convergence used as description of the adaptive system response to a transient in patient status. The paper includes an example of testing a surveillance network prototype with use of proposed parameters, demonstrating their high usability. [ABSTRACT FROM AUTHOR]

In the paper a new method of handwritten word recognition is described. In this method different probabilistic character language models (PCLMs) are used in order to improve the word recognition accuracy. These models consist of conditional probability distributions of characters given that preceding, succeeding or surrounding characters are known. The application of character succession and precedence in word recognition was explored in many earlier works. The novelty of the method proposed here consists in utilizing also the conditional probabilities based on character appearing of both sides of the letter being recognized and in combining such classifier with other ones based on character precedence and succession. This new classifier that uses two sided character neighborhood is realized as an iterative procedure which starts with character support factors evaluated independently for each character in the word by a soft character classifier. Then in each step of this iterative procedure the support factors are calculated as Bayes-like formula using needed probabilities. Next, the support factors obtained at the character level are used to calculate the support factors for complete words. Finally, the soft word recognition results obtained for classifiers based on three different PCLMs are combined. Experiments described in the paper show the superiority of the combined method over all its simpler components. [ABSTRACT FROM AUTHOR]

In the paper we present the method of the generalisation of a language sample for grammatical inference of quasi context-sensitive GDPLL(k) grammars. GDPLL(k) grammars and parsers have been developed as an efficient tool for syntactic pattern recognition: the grammars are characterised by very good discriminative properties and the parser for the grammars is of the linear computational complexity. Nevertheless, one of the main problems of practical application of GDPLL(k) grammars in syntactic pattern recognition systems consists in difficulties in defining the grammar from the sample of a pattern language. The method which we describe in the paper is an important element of the solution of this problem. [ABSTRACT FROM AUTHOR]

In syntactic pattern recognition a pattern is represented by abstract data, for instance a graph. The problem of recognition is to determine if a pattern, represented by the describing graph, belongs to a language L(G), generated by a graph grammar G. The so-called IE graphs can be used for a pattern description. They are generated by so-called ETPL(k) graph grammars. The purpose of this paper is to present an idea of a new approach to syntactic recognition of fuzzy patterns represented by fuzzy IE graphs, followed the example of random IE graphs. This methodology can be used for analysis of wider class of patterns and scenes than the one described by the classical syntactic methods. In this paper, apart from the presentation of the fuzzy-aided approach, it is also shown that the probabilistic-syntactic approach is a special case of the presented one. [ABSTRACT FROM AUTHOR]

Mathematical morphology is a widely known image processing technique. Morphology-based processing techniques can be easily implemented, thanks to conceptual simplicity of basic morphological operators. However, most morphological algorithms that are run on sequential processors are unable to meet real-time requirements. A way to solve this problem are the implementations using dedicated hardware, e.g. the FPGAs. This paper describes a complete FPGA architecture for real-time edge detection. The architecture consists of prefiltering, gradient image computation and non-maximum suppression blocks, allowing the extraction of a single-pixel-thin binary edge image of good quality. The described solution, although simple and compact, is capable of processing image data stream with the speed of hundreds of frames per second for a 512×512, 8-bit gray-scale image, while keeping the costs, size and power consumption at a low level. Description of implemented algorithm, resource usage summary, output images, as well as block diagrams of the architecture are included in the paper. [ABSTRACT FROM AUTHOR]

Colorization is a term used to describe a computerized process for adding color to black and white pictures, movies or TV programs by replacing a scalar value stored at each pixel of the gray scale image by a vector in a three dimensional color space with luminance, saturation and hue or simply RGB. Since different colors may carry the same luminance value but vary in hue and/or saturation, the problem of colorization has no inherently "correct" solution. Due to these ambiguities, human interaction usually plays a large role. In this paper we present a novel colorization method that utilizes the morphological distance transformation and changes of neighboring pixel intensities to propagate the color within the gray scale image. The proposed method frees the user of segmenting the image, as color is provided simply by scribbles which are next automatically propagated within the image. The effectiveness of the algorithm allows the user to work interactively and to obtain the desired results promptly after providing the color scribbles. In the paper we show that the proposed method allows for high quality colorization results for still images. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss a fusion of the two most popular image features - color and shape - in the aspect of content-based image retrieval. It is clear that these representations have their own advantages and drawbacks. Our suggestion is to combine them to achieve better results in various areas, e.g. pattern recognition, object representation, image retrieval, by using optimal variants of particular descriptors (both, color and shape) and utilize them in the same time. To achieve such goal we propose two general strategies (sequential and parallel) for joining elementary queries. They are used to construct a system, where each image is being decomposed into regions, basing on shapes with some characteristic properties - color and its distribution. In the paper we provide an analysis of this proposition as well as the initial results of application in Content Based Image Retrieval problem. The original contribution of the presented work is related to the fusion of several shape and color descriptors and joining them into parallel or sequential structures giving considerable improvements in content-based image retrieval. The novelty is based on the fact that many existing methods (even complex ones) work in the same domain (shape or color), while the proposed approach joins features from different areas. [ABSTRACT FROM AUTHOR]

This paper presents a model characterizing unsupervised learning from an information theoretic point of view. Under some hypothesis, it defines a theoretical quality criterion, which corresponds to the informational limit that bounds the learning ability of any clustering algorithm. This quality criterion depends on the information content of the learning set. It is relevant when examples are sparsely described, i.e. when most of the descriptors are missing. This theoretical limit of any unsupervised learning algorithm is then compared to the actual learning quality of different clustering algorithms (EM, COBWEB and PRESS). This empirical comparison is based on the use of artificial data sets, which are randomly degraded. Finally, the paper shows that the results of PRESS, an algorithm specifically designed to learn from sparsely described examples, are very closed to the theoretical upper bound quality. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of assessing the validity of the clusters found by a clustering algorithm. The determination of the "true" number of "natural" clusters has often been considered as the central problem of cluster validation. Many different stopping rules have been proposed in the research literature but most of them are applicable only to classical data (qualitative or quantitative). In this paper we investigate the problem of the determination of the number of clusters for symbolic objects described by interval variables. We consider five classical methods and two hypothesis tests based on the Poisson point process. We extend these methods to interval data. We apply them to the meteorological stations data set. [ABSTRACT FROM AUTHOR]

In 1987, Diday added a new dimension to data analysis with his fundamental paper introducing the notions of symbolic data and their analyses. He and his colleagues, among others, have developed innumerable techniques to analyse symbolic data; yet even more is waiting to be done. One area that has seen much activity in recent years involves the search for a measure of dependence between two symbolic random variables. This paper presents a covariance function for interval-valued data. It also discusses how the total, between interval, and within interval variations relate; and in particular, this relationship shows that a covariance function based only on interval midpoints does not capture all the variations in the data. While important in its own right, the covariance function plays a central role in many multivariate methods. [ABSTRACT FROM AUTHOR]

Obesity and lack of physical fitness are increasingly common in adults as well as children and can negatively affect health. Regular physical activity, such as jogging or training in a fitness center, is recommended by physiologists to fight obesity and improve one's fitness, but usually requires considerable motivation. Recently, researchers as well as companies have proposed a few fitness games, i.e. videogames where users play by performing physical exercises, in which game elements (such as graphics and gameplay) are used to encourage people to exercise regularly. This paper proposes a fitness game system which aims at combining arcade-style game graphics, physiological sensors (e.g. heart rate monitor, 3D accelerometer), and an adaptation engine. The adaptation engine considers personal information provided by the user (e.g., age and gender), her current heart rate and movements, and information collected during previous game sessions to adjust the required intensity of physical exercises through context-aware and user-adaptive dynamic adaptations of graphics and gameplay. Besides describing the general system, the paper presents two implemented games and a preliminary user evaluation, which also led us to introduce in the system a 3D virtual human. [ABSTRACT FROM AUTHOR]

We introduce in this paper an interface for modeling botanical trees that we term a sketch-and-spray interface. In using the interface, a user would sketch out various types of strokes to construct the main structures of a tree. A user could use a branch stroke to draw a single branch, a leaf stroke to draw a single leaf or a stem of leaves, or a copy stroke to draw a whole branch together with all its sub-branches and leaves. A user could also use various editing strokes to modify the tree structure. Finally, a user could spray particles upon it to ‘grow' the overall shape of the tree. We illustrate the utility of our interface using a number of examples throughout this paper. [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]

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]

This paper describes expression space generation technology that enables animators to control the expressions of 3-dimensional avatars in real-time by selecting a series of expressions from facial expression space. In this system, approximately 2400 facial expression frames are used to generate facial expression space. In this paper, distance matrixes that present distances between facial characteristic points are used to show the state of an expression. The set of these distance matrixes is defined as facial expression space. However, this facial expression space is not space that can be transferred to one space or another in a straight line, when one expression changes to another. In this technology, the route for moving from one expression to another is approximately inferred from captured facial expression data. First, it is assumed that two expressions are close to each other when the distance between distance matrixes that show facial expression states is below a certain value. When two random facial expression states are connected with the set of a series of adjacent expressions, it is assumed that there is a route between the two expressions. It is further assumed that the shortest path between two facial expressions is the path when one expression moves to the other expression. Dynamic programming is used to find the shortest path between two facial expressions. The facial expression space, which is the set of these distance matrixes, is multidimensional space. The facial expression control of 3-dimensional avatars is carried out in real-time when animators navigate through facial expression space. In order to assist this task, multidimensional scaling is used for visualization in 2-dimensional space, and animators are told to control facial expressions when using this system. This paper evaluates the results of the experiment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new gaze estimation method by analyzing the cornea surface model which is estimated through three dimensional analysis of human eye in HMD (Head Mounted Display) environments. This paper has four advantages over previous works. First, in order to obtain accurate gaze position, we use a cornea sphere model based on Gullstrand eye scheme. Second, we calculate the 3D position of the cornea sphere and a gaze vector by using a camera, three collimated IR-LEDs and one illuminated IR-LED. Third, three coordinates such as camera, monitor and eye coordinates are unified, which can simplify the complex 3D converting calculation and allow for calculation of the 3D eye position and gaze position on a HMD monitor. Fourth, a simple user dependent calibration method is proposed by gazing at one position of HMD monitor based on Kappa compensation. Experimental results showed that the average gaze estimation error of the proposed method was 0.89 degrees. [ABSTRACT FROM AUTHOR]

Non-Photorealistic Rendering (NPR) can offer increased concentration and familiarity to the watcher. For this reason, many media such as movies, games, and commercials currently use the NPR method to deliver information. In this paper, we suggest an interactive system for video cartooning based on the mean-shift segmentation of image and video. In order to solve the problems of time complexity and memory allocation, the conventional problems of video mean-shift segmentation, this paper proposes several techniques such as foreground object based segmentation and sequential segmentation. We also propose the interactive correction technique to get enhanced results. For more cartoonic representation, we used spline curve approximation of segment boundaries in the final rendering results. With our method, we can easily create cartoon rendering output using the video streams such like home video, which can be obtained easily in our daily life. [ABSTRACT FROM AUTHOR]

In certain classes of chemical reaction networks (CRN), there may be two stable states. The challenge is to find a model of the CRN such that the stability properties can be predicted. In this paper we consider the problem of building a P-system designed to simulate the CRN in an attempt to determine if the CRN is stable or bistable. We found that for the networks in [2] none of the bistable CRN would have a bistable P-system by stoichiometry alone. The reaction kinetics must be included in the P-system model; the implementation of which has been considered an open problem. In this paper we conclude that a P-system for a CRN in m reactants and n products has at most 2(m2 + mn) membranes and 6(m2 + mn) rules. This suggests that P-system models of a chemical reaction network, including both stoichiometry and reaction kinetics can be built. [ABSTRACT FROM AUTHOR]

A comprehensive view of speech and voice technologies is now demanding better and more complex tools amenable of extracting as much knowledge about sound and speech as possible. Many knowledge-extraction tasks from speech and voice share well-known procedures at the algorithmic level under the point of view of bio-inspiration. The same resources employed to decode speech phones may be used in the characterization of the speaker (gender, age, speaking group, etc.). Based on these facts the present paper examines a hierarchy of sound processing levels at the auditory and perceptual levels on the brain neural paths which can be translated into a bio-inspired audio-processing architecture. Through this paper its fundamental characteristics are analyzed in relation with current tendencies in cognitive audio processing. Examples extracted from speech processing applications in the domain of acoustic-phonetics are presented. These may find applicability in speaker's characterization, forensics, and biometry, among others. [ABSTRACT FROM AUTHOR]

Scale space interest points capture important photometric and deep structure information of an image. The information content of such points can be made explicit using image reconstruction. In this paper we will consider the problem of combining multiple types of interest points used for image reconstruction. It is shown that ordering the complete set of points by differential (quadratic) TV-norm (which works for single feature types) does not yield optimal results for combined point sets. The paper presents a method to solve this problem using canonical sets of scale space features. Qualitative and quantitative analysis show improved performance over simple ordering of points using the TV-norm. [ABSTRACT FROM AUTHOR]

This paper investigates a production planning problem in which the inventory is deteriorating at a constant rate, demand is price dependent and back-order is allowed. It is assumed in most previous works that back-ordering customers may not cancel their orders. However, in reality, many customers may withdraw or cancel their orders before receiving their orders. Tacking the cancellation phenomenon into account, this paper develops a continuous-time model to simultaneously determine the production decision and the selling price. A simple algorithm is used to obtain the optimal solutions. Numerical examples are also used to illustrate the solution searching procedure and the characteristics of the optimal decisions. [ABSTRACT FROM AUTHOR]

In this paper, we handle a negotiation method in which a main negotiation consists of multiple sub-negotiations. In items allocation for commerce, there are some risks on the trade because the market balance is determined by supply and demand. The result of main negotiation is also determined by the order of sub-negotiations and agents' behaviors since agents' budgets have limitations on the actual commercial trading. However, it is difficult to decide the order of negotiations, such as simultaneous decisions and rotations. In this paper, we give the trading model in such cases, that is, agents purchase items by pooled buying. In actual trading as pooled buying, items are sold by the volume discount. Concretely, we discuss joint-stock company and private limited partnership on the web. In the negotiation phase, an agent proposes pooled buying based on the number of items and their prices considering their budgets. The degree of risks is calculated. All agents can see the risks with each item. Agents cooperate to the proposing agent based on the degree of risks. In this paper, we give two scenarios for trading. One is to avoid free riders who get surplus without risks. Another one is to promote agents' participation to increase social surplus. For risk aversion and promoting cooperation, we employ the side-payment policy, that is, cooperative agents' risks are preserved to a minimum. Further, we give some discussions where agents must pay the negotiation costs and charge for storage. [ABSTRACT FROM AUTHOR]

This paper presents a new philosophy to model the behavior of a Student in a Tutorial-like system using Learning Automata (LA). The model of the Student in our system is inferred using a higher-level of LA, referred to Meta-LA, which attempt to characterize the learning model of the Students (or Student Simulators), while the latter use the Tutorial-like system. To our knowledge, this is the first published result which attempts to infer the learning model of an LA when it is treated externally as a black box, whose outputs are the only observable quantities. Additionally, our paper represents a new class of Multi-Automata systems, where the Meta-LA communicate with the Students, also modelled using LA, in a synchronous scheme of interconnecting automata. [ABSTRACT FROM AUTHOR]

A novel neural filter and its application to active control of nonlinear noise are proposed in this paper. This method helps to avoid the premature saturation of backpropagation algorithm; meanwhile, guarantees the system convergence by the proposed self-tuning method. The comparison between the conventional filtered-X least-mean-square (FXLMS) algorithm and proposed method for nonlinear broadband noise in active noise cancellation (ANC) system is also made in this paper. The proposed design method is very easy to be implemented and versatile to the other applications. Several simulation results show that the proposed method can effectively cancel the narrowband and nonlinear broadband noise in a duct. [ABSTRACT FROM AUTHOR]

Performance of a genetic algorithm for function optimization, often appeared in real-world applications, depends on its crossover operator strongly. Existing crossover operators are designed for intensive search in certain promising regions. This paper, first, discusses where the promising search regions are on the basis of some assumptions about the fitness landscapes of objective functions and those about a state of a population, and this discussion reveals that existing crossover operators intensively search some of the promising regions but not all of them. Then, this paper designs a new crossover operator for searching all of the promising regions. For utilizing the advantageous features of this crossover operator, a new selection model considering characteristic preservation is also introduced. Several experiments have shown the proposed method has worked effectively on various test functions. [ABSTRACT FROM AUTHOR]

Assembly design decision making is to provide a solution of currently violating design by evaluating assembly design alternatives with the consideration of the assembly design decision (ADD) criteria and of the causal interactions with manufacturing-environmental factors. Even though existing assembly design support systems have a systematic mechanism for determining the decision-criterion weight, the system still has a limitation to capture the interactions between manufacturing-environmental factors and ADD criteria. Thus, we introduce in this paper, Bayesian belief networks (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. BBN has a sound mathematical foundation and reasoning capability. It also has an efficient evidence propagation mechanism and a proven track record in industry-scale applications. However, it is less friendly and flexible, when used for knowledge acquisition. In this paper, we propose a methodology for the indirect knowledge acquisition, using fuzzy cognitive maps, and for the conversion of the representation into BBN. [ABSTRACT FROM AUTHOR]

This paper proposes a context-based image retrieval system based on color, area, and perimeter intercepted lengths of segmented objects in an image. It characterizes the shape of an object by its area and the intercepted lengths obtained by intercepting the object perimeter by eight lines with different orientations passing through the object center, and the object color by its mean and standard deviation (STD). Recently, we reported that the color-shape based method (CSBM) is better than conventional color histogram (CCH) and fuzzy color histogram (FCH) in retrieving computer-generated images. However, its performance is only fair in the retrieval of natural images. For CSBM, object color is treated as uniform by reducing the number of colors in an image to only 27 colors. In this paper, we improve the performance by representing the color features of an object with its mean and STD. During the image retrieval stage, t-value is calculated based on the color features of two images, one in the query and the other in the database. The result shows that the proposed method achieves better performance in retrieving natural images compared to CCH, FCH, and CSBM. In the future, the proposed technique will be applied for the retrieval of digitized museum artifacts. [ABSTRACT FROM AUTHOR]

This paper reports the results obtained by analysing some of the most well-known features used in Computer Vision to describe and classify shapes in an appealing real application. We aim to demonstrate the applicability of shape descriptors to classify muscles on Magnetic Resonance Imaging (MRI). The mechanized classification of ham muscles could help the industries to automate the ripening process for Iberian ham. The excellent classification percentages obtained in our experiments suggest the real viability of the feature vector developed in this paper to recognize and classify muscles. [ABSTRACT FROM AUTHOR]

In traffic sign recognition systems, one of the normal approaches is the identification of the shape of the sign prior to the recognition itself. Normally, the recognition process needs an accurate localization of the sign for a good performance. If we are dealing with triangular, rectangular and circular signs, this means the accurate localization of the vertices of the triangle and the rectangle, or the parameters of the ellipse. In this paper we have developed a system which searches the above mentioned parameters from the signature of the blob using techniques of algebraic-distance minimization. Comparisons with previous works show good improvements in the localization of the shape, especially in the presence of slight occlusions. This work is part of a traffic sign recognition system, and in this paper we focus on the shape localization step. [ABSTRACT FROM AUTHOR]

This paper deals with a compact catadioptric omnidirectional stereovision system based on a single camera and multi-mirrors (at least two mirrors). Many configurations were empirically designed in previous works with the aim to obtain a good 3D reconstruction accuracy. In this paper, we propose to use optimization techniques for omnidirectional catadioptric stereovision design, by using a stochastic local search method in order to find a good sensor (number, relative positions and sizes of mirrors). We explain principles of our approach and provide automatically designed sensors with a number of mirrors from two to nine. We finally simulate the 3D-reconstruction of a real environment modeled under a ray-tracing software with some of these sensors. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reconstructing non- overlapping transparent and opaque surfaces from multiple view images. The reconstruction is attained through progressive refinement of an initial 3D shape by minimizing the error between the images of the object and the initial 3D shape. The challenge is to simultaneously reconstruct both the transparent and opaque surfaces given only a limited number of images. Any refinement methods can theoretically be applied if analytic relation between pixel value in the training images and vertices position of the initial 3D shape is known. This paper investigates such analytic relations for reconstructing opaque and transparent surfaces. The analytic relation for opaque surface follows diffuse reflection model, whereas for transparent surface follows ray tracing model. However, both relations can be converged for reconstruction both surfaces into texture mapping model. To improve the reconstruction results several strategies including regularization, hierarchical learning, and simulated annealing are investigated. [ABSTRACT FROM AUTHOR]

An essential functionality for advanced driver assistance systems (ADAS) is road segmentation, which directly supports ADAS applications like road departure warning and is an invaluable background segmentation stage for other functionalities as vehicle detection. Unfortunately, road segmentation is far from being trivial since the road is in an outdoor scenario imaged from a mobile platform. For instance, shadows are a relevant problem for segmentation. The usual approaches are ad hoc mechanisms, applied after an initial segmentation step, that try to recover road patches not included as segmented road for being in shadow. In this paper we argue that by using a different feature space to perform the segmentation we can minimize the problem of shadows from the very beginning. Rather than the usual segmentation in a color space we propose segmentation in a shadowless image which is computable in real-time using a color camera. The paper presents comparative results for both asphalted and non-asphalted roads, showing the benefits of the proposal in presence of shadows and vehicles. [ABSTRACT FROM AUTHOR]

This paper outlines a neural network model based on the Fuzzy Cognitive Maps (FCM) framework for solving the automatic image change detection problem. Each pixel in the reference image is assumed to be a node in the network. Each node has associated a fuzzy value, which determines the magnitude of the change. Each fuzzy value is updated by a trade-off between the influences received from the fuzzy values from other neurons and its own fuzzy value. Classical approaches in the literature have been designed assuming that the mutual influences between two nodes are symmetric. The main finding of this paper is the assumption that mutual influences could not be symmetric. This non symmetric relationship can be embedded by the FCM paradigm. The performance of the proposed method is illustrated by comparative analysis against some recent image change detection methods. [ABSTRACT FROM AUTHOR]

This paper proposes an incremental method for feature selection, aimed at identifying attributes in a dataset that allow to buid good classifiers at low computational cost. The basis of the approach is the minimal-redundancy-maximal-relevance (mRMR) framework, which attempts to select features relevant for a given classification task, avoiding redundancy among them. Relevance and redundancy have been popularly defined in terms of information theory concepts. In this paper a modification of the mRMR framework is proposed, based on a more proper quantification of the redundancy among features. Experimental work on discrete-valued datasets shows that classifiers built using features selected by the proposed method are more accurate than the ones obtained using original mRMR features. [ABSTRACT FROM AUTHOR]

Several papers have already shown the interest of using multiple classifiers in order to enhance the performance of biometric person authentication systems. In this paper, we would like to argue that the core task of Biometric Person Authentication is actually a multiple classifier problem as such: indeed, in order to reach state-of-the-art performance, we argue that all current systems , in one way or another, try to solve several tasks simultaneously and that without such joint training (or sharing), they would not succeed as well. We explain hereafter this perspective, and according to it, we propose some ways to take advantage of it, ranging from more parameter sharing to similarity learning. [ABSTRACT FROM AUTHOR]

Traditional boosting method like adaboost, boosts a weak learning algorithm by updating the sample weights (the relative importance of the training samples) iteratively. In this paper, we propose to integrate feature re-weighting into boosting scheme, which not only weights the samples but also weights the feature elements iteratively. To avoid overfitting problem caused by feature re-weighting on a small training data set, we also incorporate relevance feedback into boosting and propose an interactive boosting called i.Boosting. It merges adaboost, feature re-weighting and relevance feedback into one framework and exploits the favorable attributes of these methods. In this paper, i.Boosting is implemented using Adaptive Discriminant Analysis (ADA) as base classifiers. It not only enhances but also combines a set of ADA classifiers into a more powerful one. A feature re-weighting method for ADA is also proposed and integrated in i.Boosting. Extensive experiments on UCI benchmark data sets, three facial image data sets and COREL color image data sets show the superior performance of i.Boosting over AdaBoost and other state-of-the-art projection-based classifiers. [ABSTRACT FROM AUTHOR]

Pulmonary embolism (PE) is a very serious condition causing sudden death in about one-third of the cases. Treatment with anti-clotting medications is highly effective but not without complications, while diagnosis has been missed in about 70% of the cases. A major clinical challenge, particularly in an Emergency Room, is to quickly and correctly diagnose patients with PE and then send them on to therapy. Computed tomographic pulmonary angiography (CTPA) has recently emerged as an accurate diagnostic tool for PE, but each CTPA study contains hundreds of CT slices. The accuracy and efficiency of interpreting such a large image data set is complicated by various PE look-alikes and also limited by human factors, such as attention span and eye fatigue. In response to this challenge, in this paper, we present a fast yet effective approach for computer aided detection of pulmonary embolism in CTPA. Our proposed approach is capable of detecting both acute and chronic pulmonary emboli with a distinguished feature of incrementally reporting any detection immediately once becoming available during searching, offering real-time support and achieving 80% sensitivity at 4 false positives. This superior performance is contributed to our novel algorithms (concentration oriented tobogganing and multiple instance classification) introduced in this paper for candidate detection and false positive reduction. [ABSTRACT FROM AUTHOR]

In this paper we describe a new method for quantifying metabolic asymmetry modulo structural hemispheric differences. The study of metabolic asymmetry in Alzheimer's disease (AD) serves as a driving application. The approach is based on anatomical atlas construction by large deformation diffeomorphic metric mapping (LDDMM) first introduced in [1]. Using invariance properties of the LDDMM, we define a structurally symmetric coordinate frame in which metabolic asymmetries between the left and the right hemispheres can be studied. This structurally symmetric coordinate system of each subject provides the correspondence between left and right hemispheric structures in an individual brain. These correspondences are used for measuring metabolic asymmetry modulo structural asymmetry. Again using the atlas construction framework, we build a common symmetric coordinate system of a entire population. The metabolic asymmetry maps of individuals in a population under study are mapped into the common structurally symmetric coordinate frame, allowing for a statistical description of the populations metabolic asymmetry. In this paper we prove certain invariance properties of the LDDMM atlas construction framework that make the definition of structurally symmetric coordinate systems possible. We present results from applying the methodology to images from the Alzheimer's Disease Neuroimaging Initiative (ADNI)[2]. [ABSTRACT FROM AUTHOR]

This paper presents a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wider range of problems than existing methods, including nonmanifold surfaces and objects of arbitrary topology. The proposed method is to construct a point-based sampling of the shape ensemble that simultaneously maximizes both the geometric accuracy and the statistical simplicity of the model. Surface point samples, which also define the shape-to-shape correspondences, are modeled as sets of dynamic particles that are constrained to lie on a set of implicit surfaces. Sample positions are optimized by gradient descent on an energy function that balances the negative entropy of the distribution on each shape with the positive entropy of the ensemble of shapes. We also extend the method with a curvature-adaptive sampling strategy in order to better approximate the geometry of the objects. This paper presents the formulation; several synthetic examples in two and three dimensions; and an application to the statistical shape analysis of the caudate and hippocampus brain structures from two clinical studies. [ABSTRACT FROM AUTHOR]

In this paper an algorithm for atlas-to-image non-rigid registration based on regional entropy minimization is presented. Tissue class probabilities in the atlas are registered with the intensities in the target image. The novel aspect of the paper consists in using tissue class probability maps that include the three main regions (for the brain, white matter, gray matter and csf) and a further partitioning thereof. For example, gray matter is further subdivided into basal ganglia (each of them defining its own class) and the rest (of gray matter). This guarantees a regional entropy minimization instead of just a global one.In other words, the local labels in the atlas will be adjusted in order to obtain the best explanation for the intensity distribution in the corresponding subregion of the target image. [ABSTRACT FROM AUTHOR]

This paper presents a novel statistical fuzzy-segmentation method for diffusion tensor (DT) images and magnetic resonance (MR) images. Typical fuzzy-segmentation schemes, e.g. those based on fuzzy-C-means (FCM), incorporate Gaussian class models which are inherently biased towards ellipsoidal clusters. Fiber bundles in DT images, however, comprise tensors that can inherently lie on more-complex manifolds. Unlike FCM-based schemes, the proposed method relies on modeling the manifolds underlying the classes by incorporating nonparametric data-driven statistical models. It produces an optimal fuzzy segmentation by maximizing a novel information-theoretic energy in a Markov-random-field framework. For DT images, the paper describes a consistent statistical technique for nonparametric modeling in Riemannian DT spaces that incorporates two very recent works. In this way, the proposed method provides uncertainties in the segmentation decisions, which stem from imaging artifacts including noise, partial voluming, and inhomogeneity. The paper shows results on synthetic and real, DT as well as MR images. [ABSTRACT FROM AUTHOR]