bluuebunny/crossref_metadata_2025_split huggingface dataset:
Obstacle detection by disparity prediction stereopsis method
Authors: Watanabe Mutsumi , Onoguchi Kazunori , Kuno Yoshinori , Asada Haruo 
Published: July 2007

AbstractThe authors devised a method (disparity prediction Stereopsis method) which calculates the range of the disparity which corresponds to the space where a robot is due to move and predicts the existence or non‐existence of the object by matching the edge segment extracted from the stereo images with the range of the disparity. The method is applied to the obstacle detection in the navigation of the mobile robot. The object to be detected is a still object standing on a floor.First, the existence of the object is predicted by the stereo matching of vertical edges. The remaining edges are examined only if the matched vertical edge exists. As a result, the efficiency is improved. In this method, the range of search is restricted and local matching is tried. Consequently, an incorrect match (false match) is produced easily in the background, in the environment with low contrast.To remedy this adverse effect, the floor region extraction process is executed by assuming the spatial continuity and the similarity of the gray‐level distribution in the near region. If the lower end of the matched edge is contained in the floor region, the object is decided as a candidate for the obstacle. By this decision scheme, the probability of misdetection of the object which does not interfere with the space where the robot is due to move such as the object far and high above the viewpoint and the object hanging on the front wall can be reduced. This improves the reliability. By the indoor evaluation experiment, such as the scene of laboratory, the usefulness of the method is verified. The motion control of the robot is attempted in an environment simulating a nuclear power plant.
A realtime learning algorithm for recurrent neural networks
Authors: Uchiyama Tadasu , Shimohara Katsunori 
Published: July 2007

AbstractIn recent years, recurrent networks have been attracting attention as a processing mechanism of spatiotemporal patterns by neural networks. Although learning algorithms based on the steepest descent method have been proposed by Williams and Zipser and by Pearlmutter, that of Williams and Zipser has a problem of increased computational complexity for large‐scale networks, and Pearlmutter1s has a flaw in that learning cannot be completed in a real time.In this paper, gradients of objective functionals are derived by variational calculus and a real‐time computation method of gradients is proposed. With this method, it is expected that the real‐time learning, requiring less computational complexity compared to conventional learning algorithms, will become possible.
A point‐inclusion algorithm for a domain with boundary composed of algebraic curve segments
Authors: Ying Jiang‐Qian , Sugie Noboru 
Published: July 2007

AbstractIn this paper an algorithm is given which decides, for a domain with boundary composed of planar algebraic curve segments, whether an arbitrary point is inside or external to the domain. The algebraic curve segments of the algorithm are assumed to be contained in rectangles or other polygons (which may not contain other pieces of the curve). We discuss which curve segments have this representation. When the curves are conic sections, we give some simple rules for constructing the representation.
Texture Discrimination Using Self‐Organized Multiresolution Filtering
Authors: Sakou Hiroshi , Matsushima Hitoshi , Ejiri Masakazu 
Published: July 2007

AbstractIn texture discrimination the basic image processes are considered to choose effective resolution, to determine representative features and to unify subregions having the same representative features. To realize these processes, this paper presents an improved method for discriminating higher‐order features by using a self‐organized multiresolutional filter.First, we make a local transformation function with the maximum discrimination ability by a limited‐size neighborhood operation. Next, extending the method to the multiresolutional case, we let neighbor pixels of resolution which is optimal for discrimination be chosen automatically. Moreover, we realize unification of texture regions by connecting features represented by these pixel values and by obtaining macro‐features.In this paper we also describe examples in which this method is applied to extract title characters from printed pages and to extract a specific part from a map image, and show that this method using higher‐order features possess sufficient discrimination ability for real texture images.
Photometric stereo under illumination from unknown zenith angles
Authors: Iwahori Yuji , Sugie Hidezumi , Ishii Naohiro 
Published: July 2007

AbstractAs a photometric stereo method, this paper proposes a new algorithm for reconstructing a surface shape under the condition that the information of zenith angles of three light source directions is unknown.The object has a uniform Lambertian surface which has whole gradients. An illuminating equation consisting of the parameters of light source directions is derived from the information of three shading images. The simultaneous equations can be held at multiple surface elements and be solved by the method of least squares. This method makes it possible to extract the information of zenith angles of three lighting directions and optimized surface gradient distribution of the object.When the method of least squares is applied, a method to set the initial values for three zenith angles of lighting directions is described, and a method to select surface elements which construct the objective function is proposed. The effectiveness and the validity are confirmed by computer experiments.While the conventional photometric stereo treats the information of zenith angles and azimuth angles of three lighting directions as known constants, this algorithm can make it possible to reconstruct the surface shape from the information of only the azimuth angles of lighting directions.
Efficient algorithms for realizing a layout of complete binary trees
Authors: Fukushima Yumi , Igarashi Yoshihide 
Published: July 2007

AbstractThis paper discusses an efficient algorithm for realizing a layout of complete binary trees. This algorithm is an improvement of Ullman's layout method for complete binary trees. This layout is composed of H‐trees, vertical trees, and a horizontal tree. The vertical trees and a horizontal tree have the same structure.First, a simple algorithm based on the pre‐order of a horizontal tree is given. This pre‐order algorithm realized the layout by Ullman, and the area and the maximum edge length of a complete binary tree by this method attain the respective lower bounds.Next, an improved algorithm based on the post‐order of a horizontal tree is presented. The area of a complete binary tree by this method is two‐thirds that of the previous method when the height of the tree is sufficiently large. Also, the maximum edge length is the same as that by Ullman's method.
Neural network model for realtime adaptation to rapidly changing environments
Authors: Okamoto Yoshinori 
Published: July 2007

AbstractWe propose a new neural network model called the paradigm model (PDM), which adapts to rapidly changing, nonstationary environments in real time. The creation and destruction of cells play an important role in PDM training, and we show by simulation that for an application in a noisy and rapidly changing environment, the performance of PDM exceeds that of existing, parametric neural network models. PDM is an extension of the existing RCE (restricted Coulomb energy) model which does not use iterative computation, is easily retrained and can be implemented using integer arithmetic. The PDM model solves the problem that in noisy and nonstationary environments, the number of cells in the middle layer of the RCE model increases without bound. In addition, we show the influence of the PDM training parameters on the recognition rate, number of cells in the middle layer, and the number of cells eliminated from the middle layer.
A drawing editor key 3 with capabilities of defining figure parts and preserving connection/inclusion relationships
Authors: Matsuura Toshio , Naota Hajime , Nakamura Makoto 
Published: July 2007

AbstractA WYSIWYG style drawing editor Key 3 was designed and implemented with the following special functions: (1) Key 3 which allows users to define figure parts by selecting figures directly on the screen and also by describing figures in a language Keyfig designed by the authors in which formal parameters may be used; (2) Key 3 which recognizes topological relationships such as connection and inclusion between figures. Such relationships can be preserved when objects are moved or resized. The drawing time and the CPU time for some typical examples are measured. The experiment shows that the foregoing functions are useful and that the authors' implementation is reasonable.
Automatic shape model acquisition based on a generalization of convex/concave structure
Authors: Ueda Naonori , Suzuki Satoshi 
Published: July 2007

AbstractThis paper proposes a novel method for acquiring a shape model from shape samples of the same class. In conventional approaches, a priori knowledge and unique parts segmentation are needed because they perform generalization of predefined symbolic concepts. Moreover, they cannot form visual shape models because the model is represented as parts and parts relationships in the form of graph models such as semantic networks and multilevel graphs.The approach herein can directly acquire a visual model from a few samples without a priori knowledge because it is based on the generalization of multiscale convex/concave structure of a class of shapes.First, an optimal scale convex/concave segment structure common to shape samples using a multiscale segment matching method is extracted. Then the extracted segments are integrated to create generalized shapes. Finally, an importance measure based on the optimal scale is assigned to each convex/concave segment of the generalized shapes. That is, the proposed shape models consist of conjunctive descriptions as for the convex/concave segment structures, disjunctive descriptions as for the convex/concave segment forms, and the importance measure assigned to each segment of the shape model. A shape recognition method using the acquired models also is presented. Experimental results show the usefulness of the proposed method.
A universal literal circuit and its application to quaternary logic networks
Authors: Zukeran Chotei , Nagata Yasunori , Afuso Chushin , Honda Makoto , Nagamine Yutaka 
Published: July 2007

AbstractA new type of universal literal circuit, in which two thresholds of the circuit are variable and controlled by external signals, is proposed herein. The circuit is designed with CMOS‐FETs, and both computer simulation by FSPICE and experiments on bread‐board with discrete elements confirm its correct operation.A new type of basic quaternary operator, quaternary U‐gate, is also introduced. The U‐gate comprises a pair of CMOS pass transistors driven by the output of the universal literal circuit. Mathematical properties of the U‐gate are derived, and synthesis procedures of quaternary logic functions using U‐gates are given. The quaternary U‐gate and quaternary constants form a complete set of quaternary operators and its mathematical properties implicate those of CP‐gates and SCP‐gates.
Surface reconstruction from a contour line image by regularization
Authors: Muraki Shigeru , Yokoya Naokazu , Yamamoto Kazuhiko 
Published: July 2007

AbstractThe regularization method, which recovers three‐dimensional (3‐D) information from an image by solving the problem of minimizing an energy functional, has recently been applied to various problems in computer vision.This paper proposes an algorithm for reconstructing a detailed 3‐D shape from a contour line image and a sparse set of altitude data by adding a new constraint that points on the same contour line have the same altitude to surface reconstruction by the traditional regularization method.The problem of minimizing an energy functional defined in this paper is reduced to a problem in a very systematic way. This method is applied in practice to contour lines extracted from a real topographic map and geographic numerical information to verify that terrain surface reconstruction from a topographic map is possible.
A hand gesture recognition method and its application
Authors: Takahashi Tomoichi , Kishino Fumio 
Published: July 2007

AbstractIf information concerning speech and hand gestures is handled in an integrated way, a more user‐friendly interface will be realized. These are defined here as the “hand gesture” as the “shape and motion of hands to transmit the intention of the speaker.” They aim at the realization of the hand gesture interface.Using the hand shape input device Data GloveTM, we investigate the coding of the hand shape/motion information, a series of hand shapes in a continuous motion and the recognition method for the hand motion. For the case of hand shape/motion in the alphabet gesture in the sign language, a recognition experiment is executed. It is verified that some 20 hand gestures can be recognized, including the dynamic hand gestures. As the interface for the map guidance system, the instruction by speech and hand gesture is realized.
An asynchronous parallel branch‐and‐bound algorithm
Authors: Kawaguchi Tsuyoshi , Masuyama Hiroshi , Maeda Tamotsu 
Published: July 2007

AbstractThis paper presents a parallel branch‐and‐bound algorithm which is applicable to a loosely coupled multiprocessor with nonhierarchical interconnection network such as torus or hypercube. This algorithm is asynchronous and processing elements (PEs) start evaluation of nodes without being synchronized. Thus, when each PE finishes evaluation of one node, it can immediately start the evaluation of the next node.First, we compared the performance for parallel branch‐and‐bound algorithms which have almost the same communication overhead. From the simulation results it was confirmed that when the proposed algorithm is applied to a torus machine, an efficiency (speed‐up rate divided by the number of PEs) is obtained which is about twice better than that of the existing synchronous algorithm for the improved tree machine and about 1.4 times better than that of the synchronous algorithm for a torus machine proposed by the authors. Interconnection networks among PEs suitable for parallelization of branch‐and‐bound algorithms were investigated by applying the algorithm in this paper to several kinds of interconnection networks among PEs.As the results, it was found that even in the case in which communication time between adjacent PEs has very little effect on the execution time of an algorithm, a hypercube machine realizes almost the same efficiency as that of an interconnection network with a larger degree of PEs.
Edge feature determination by using neural networks based on a blurred edge model
Authors: Naruse Hiroshi , Ide Atsushi , Tateda Mitsuhiro , Nomura Yoshihiko 
Published: July 2007

AbstractThis paper proposes a new model fit‐type edge feature measurement method. The characteristic of the proposed method is that it introduces a blurred edge model which matches well with a gray‐level pattern of an edge in an image actually observed. The blurred edge model is constructed by using not only edge features, which are the edge position and orientation within the pixel, but also the point spread function which expresses the image degradation during the image recording process as parameters. By using this model, the gray level of the multiple pixels near the edge for the various edge feature values is calculated, and a map is obtained from the edge features to the gray‐level pattern.Next, the inverse map which obtains edge features from a gray‐level pattern is obtained in advance through learning by using error backpropagation‐type neural networks consisting of three layers. By using the obtained inverse map, the edge features are determined from the gray‐level pattern of the actually observed image.Conventionally, since it was necessary to obtain this inverse map analytically, the edge model that could be used was restricted to the step‐edge type. On the other hand, with the method being proposed which utilizes the neural networks, an arbitrary optimal edge model for an individual image recording device can be used. For this reason, edge features can be determined precisely with this method even from local information. Many measurement experiments which changed the edge position and orientation were performed and the effectiveness of this method was confirmed.
A new information criterion combined with cross‐validation method to estimate generalization capability
Authors: Wada Yasuhiro , Kawato Mitsuo 
Published: July 2007

AbstractNeural network learning processes use only a limited number of examples of a given problem. Thus, generally speaking, it is not necessarily theoretically guaranteed that the trained network can give correct answers for unknown examples. A new method of selecting the optimal neural network structure with maximum generalization capability is proposed. In statistical mathematics, several information criteria, such as AIC (Akaike's information criterion), BIC (Bayesian information criterion), and MDL (minimum description length), are used widely to select a suitable model. Applications of these criteria were quite successful, especially for linear models. These criteria assume that the model parameters are estimated correctly by using the maximum likelihood method. Unfortunately, however, this assumption does not hold for conventional iterative learning processes such as backpropagation in multilayer perceptrons or Boltzmann machine learning. Thus, we should not apply AIC directly to the selection of the optimal neural network structure.In this paper, by expanding AIC, a new information criterion is proposed that can estimate generalization capability without the maximum likelihood estimator of synaptic weights. The cross‐validation method is used to calculate the new information criterion. By computer simulation, we show that the proposed information criterion can accurately predict the generalization capability of multilayer perceptrons, and thus the optimal number of hidden units can be determined.
Simple parallel algorithms to compute interval maxima
Authors: Nakano Koji , Tokura Nobuki , Masuzawa Toshimitsu 
Published: July 2007

AbstractThe interval maxima problem is a problem to compute each interval maximum max{in(si), …, in(ti)} for given n data in(O), …, in(n ‐ 1) and a number of intervals [s1, t1], [s2, t2]. The following result is already known:.(1) On the CRCW‐PRAM, each interval maximum can be computed using a single processor in O(1) time, after preprocessing using n/log log n processors in O(log log n) time.The following two algorithms are shown in this paper.(2) On the CRCW‐PRAM, each interval maximum can be computed using a single processor in O(log log n) time, after preprocessing using n/log log n processors in O(log log n) time.(3) On the CRCW‐PRAM, each interval maximum can be computed using a single processor O(1) time, after preprocessing using n1ϵ processors in O(1) time for every fixed ϵ > 0.These two algorithms are simpler than the algorithm (1). The preprocessing time in algorithm (3) is shorter than in (1), although a larger number of processors are employed.
Noise immunity learning for word‐spotting speech recognition
Authors: Takebayashi Yoichi , Kanazawa Hiroshi 
Published: July 2007

AbstractThis paper discusses noise immunity learning in speech recognition by word‐spotting. In the proposed method, the learning speech data with superposed noise is constructed by adding the noise data to the clean speech data collected beforehand. Then the recognition dictionary for word‐spotting is trained to improve noise immunity. In the learning, the recognition by word‐spotting is attempted for the artificially synthesized learning data while gradually increasing the ratio of the contained noise. The recognition is executed using the learning word feature vector automatically extracted based on the similarity.The noise immunity is realized by the simulation in the noisy environment and the automatic learning. The recognition and learning use the word as the unit and the multiple similarity method, which can cope with a wide range of pattern deformation. An evaluation experiment is executed using 13‐word speech data (plus noise in a railway station). For the case of 96‐dimensional word feature vector (eight dimensions for frequency and 12 dimensions for time), and S/N ratio of 10 dB, the recognition rate is improved from 85.5 percent in the word‐spotting method without learning to 94.1 percent with learning. This indicates the usefulness of the proposed method.
A method of acquiring strategies and classifying knowledge from trace data
Authors: Kobayashi Satoshi , Ohsuga Setsuo , Hori Kohichi , Yamauchi Toshiyuki 
Published: July 2007

AbstractIt is important for a knowledge processing system to have a meta‐level inference mechanism for reducing problem search space. The authors have developed such a mechanism for KAUS (Knowledge Acquisition and Utilization System) and applied it to control system design, chemical structure design, etc. However, it is difficult for the domain experts to give meta‐level knowledge. This is partly because the object knowledge is not well structured in their minds.This paper proposes a method to classify knowledge and to acquire strategies for improving system performance from trace data. Results of two experiments are given to show the effectiveness of the proposed method.
A new multiversion concurrency control algorithm for partial update transactions and bulk retrieval transactions
Authors: Kataoka Ryoji , Satoh Tetsuji , Inoue Ushio 
Published: July 2007

AbstractWith the advanced use of databases, on‐line and batch transactions are executed concurrently. Then a demand arose for the concurrency control method which can realize the excellent on‐line transaction processing independently of the batch transactions. The multiversion concurrency control is suitable for such a requirement.This paper proposes a new multiversion concurrency control algorithm which has a high concurrency between the partial update transactions, which is a typical on‐line transaction, and the bulk retrieval transactions, which is a typical batch transaction. The algorithm is based on a mixture of an improved two‐version, two‐phase locking and the multiversion time stamp ordering. It is shown as simulation results that the proposed algorithm is better than the traditional ones in terms of the completion ratio of the partial update and the completion ratio of the bulk retrieval, independently of the number of partial updates executed concurrently. It is shown also that the algorithm is better than the traditional ones in such an environment since the data contention between partial update transactions is high.
Gaze point detection system allowing head motion
Authors: Iida Muneo , Tomono Akira 
Published: July 2007

AbstractSince human eye movement easily follows one's intention, it is possible to use this to instruct a computer through a display screen, e.g., selection of a menu and control of a cursor. This paper proposes a method to detect a gaze point on a display screen independently of the movement of an operator's head. The method uses an eye tracker based on the limbus boundary method and a three‐dimensional (3‐D) magnetic sensor which determines the position of an operator's head in a fixed coordinate. Experiments show that position‐detection errors are about 1°, and this is enough for this application. To overcome an effect caused by a small involuntary movement of eyes around a target, a mouse is used at the same time. Experimental comparison between the proposed method (with a mouse) and a conventional operation (a mouse alone) shows that the former is faster when a target is relatively separated from the center, i.e., the method is effective.