In this paper, we present a robust small-object detection method, which we call “Frequency Pattern Emphasis Subtraction (FPES)”, for wide-area surveillance such as that of harbors, rivers, and plant premises. For achieving robust detection under changes in environmental conditions, such as illuminance level, weather, and camera vibration, our method distinguishes target objects from background and noise based on the differences in frequency components between them. The evaluation results demonstrate that our method detected more than 95% of target objects in the images of large surveillance areas ranging from 30-75 meters at their center.

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Sickness behavior is a series of behavioral and psychological changes that develop in those stricken with cancers and inflammatory diseases. The etiological mechanism of sickness behavior is not known in detail, and consequently there are no established standard therapies. Kamikihito (KKT), a Kampo (traditional Japanese herbal) medicine composed of 14 herbs, has been used clinically to treat psychiatric dysfunction. Previously, we found that KKT ameliorated sickness behavior in mice inoculated with murine colon 26 adenocarcinoma cells. In this study, we examined the effects of KKT on bacterial endotoxin lipopolysaccharide (LPS)-induced sickness behavior in mice. The administration of LPS caused the emotional aspects of sickness behavior, such as loss of object exploration, social interaction deficit, and depressive-like behavior. LPS also induced mRNA expression for cyclooxygenase (COX)-2, interleukin (IL)-1β and IL-6, and increased the number of c-Fos immunopositive cells in the hypothalamus and amygdala. KKT ameliorated the behavioral changes and reversed the increases in c-Fos immunopositive cells in the two brain regions, but did not influence the mRNA expression. These results suggest that KKT ameliorates sickness behavior via the suppression of neural activation without anti-inflammatory effects, and that KKT has the potential to treat sickness behavior.

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Using query-by-sketch we propose an application to efficiently create collages with some user interaction. Using rough color strokes that represent the target collage, images are automatically retrieved and segmented to create a seamless collage. The database is indexed using simple geometrical and color features for each region, and histograms that represent these features for each image. The image collection is then queried by means of a simple paint tool. The individual segments retrieved are added to the collage using Poisson image editing or alpha matting. The user is able to modify the default segmentations interactively, as well as the position, scale, and blending options for each object. The resulting collage can then be used as an input query to find other relevant images from the database.

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One of the approaches to improve program understanding is to extract what kinds of design pattern are used in existing object-oriented software. This paper proposes a technique for efficiently and accurately detecting occurrences of design patterns included in source codes. We use both static and dynamic analyses to achieve the detection with high accuracy. Moreover, to reduce computation and maintenance costs, detection conditions are hierarchically specified based on Pree's meta patterns as common structures of design patterns. The usage of Prolog to represent the detection conditions enables us to easily add and modify them. Finally, we have implemented an automated tool as an Eclipse plug-in and conducted experiments with Java programs. The experimental results show the effectiveness of our approach.

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Because of its simplicity and intuitive approach, point-based rendering has been a very popular research area. Recent approaches have focused on hardware-accelerated techniques. By applying a deferred shading scheme, both high-quality images and high-performance rendering have been achieved. However, previous methods showed problems related to depth-based visibility computation. We propose an extended point-based rendering method using a visibility map. In our method we employ a distance-based visibility technique (replacing depth-based visibility), an averaged position map and an adaptive fragment processing scheme, resulting in more accurate and improved image quality, as well as improved rendering performance.

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We present an interactive system for cosmetic makeup of a point-based face model acquired by 3D scanners. We first enhance the texture of a face model in 3D space using low-pass Gaussian filtering, median filtering, and histogram equalization. The user is provided with a stereoscopic display and haptic feedback, and can perform simulated makeup tasks including the application of foundation, color makeup, and lip gloss. Fast rendering is achieved by processing surfels using the GPU, and we use a BSP tree data structure and a dynamic local refinement of the facial surface to provide interactive haptics. We have implemented a prototype system and evaluated its performance.

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In this paper, we present a new three-dimensional (3D) virtual environment (3DVE) system named “QuViE/P”, which can enhance quality of service (QoS), that users actually feel, as good as possible when resources of computers and networks are limited. To realize this, we focus on characteristics of user's perceptual quality evaluation on 3D objects. We propose an effective QoS control scheme for QuViE/P by introducing relationships between system's internal quality parameters and user's perceptual quality parameters. This scheme can appropriately maintain the QoS of the 3DVE system and it is expected to improve convenience when using 3DVE system where resources are insufficient. We designed and implemented a prototype of QuViE/P using a multiagent framework. The experiment results show that even when the computer resource is reduced to 20% of the required amount, the proposed scheme can maintain the quality of important objects to a certain level.

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This paper presents an algorithm for the robust watermarking of 3D polygonal mesh models. The proposed algorithm embeds the watermark into a 2D image extracted from the 3D model, rather than directly embedding it into 3D geometry. The proposed embedding domain, i. e., the 2D image, is devised to be robust against the attacks like mesh simplification which severely modifies the vertices and connectivity while preserving the appearance of the model. The watermark-embedded model is obtained by using a simple vertex perturbation algorithm without iterative optimization. Two exemplary watermark applications using the proposed methods are also presented: one is to embed several bits into 3D models and the other is to detect only the existence of a watermark. The experimental results show that the proposed algorithm is robust against similarity transform, mesh simplification, additive Gaussian noise, quantization of vertex coordinates and mesh smoothing, and that its computational complexity is lower than that of the conventional methods.kn-abstract=

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The information technology revolution has transformed all aspects of our society including critical infrastructures and led a significant shift from their old and disparate business models based on proprietary and legacy environments to more open and consolidated ones. Supervisory Control and Data Acquisition (SCADA) systems have been widely used not only for industrial processes but also for some experimental facilities. Due to the nature of open environments, managing SCADA systems should meet various security requirements since system administrators need to deal with a large number of entities and functions involved in critical infrastructures. In this paper, we identify necessary access control requirements in SCADA systems and articulate access control policies for the simulated SCADA systems. We also attempt to analyze and realize those requirements and policies in the context of role-based access control that is suitable for simplifying administrative tasks in large scale enterprises.

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We considered that the surgical mechatoronics for navigation systems using MRI image available under the MRI environment was useful. Therefore, the MRI compatibility surgical navigation manipulator was developed to MRI compatibility surgical mechatoronics. Evaluation was performed by the MRI image and the leakage electromagnetic-wave measurement to the device. Result, while the manipulator was moving, the S/N ratio was down by 19.8±11.8%, and there was no distortion in the image from the subtraction image, and there was no artefact by leakage electromagnetic waves. Therefore, the MRI compatibility surgical navigation manipulator was MRI compatibility to 1.5 Tesla MRI.

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This paper describes a new color segmentation based on a normalized RGB chromaticity diagram for face detection. Face skin is extracted from color images using a coarse skin region with fixed boundaries followed by a fine skin region with variable boundaries. Two newly developed histograms that have prominent peaks of skin color and non-skin colors are employed to adjust the boundaries of the skin region. The proposed approach does not need a skin color model, which depends on a specific camera parameter and is usually limited to a particular environment condition, and no sample images are required. The experimental results using color face images of various races under varying lighting conditions and complex backgrounds, obtained from four different resources on the Internet, show a high detection rate of 87%. The results of the detection rate and computation time are comparable to the well known real-time face detection method proposed by Viola-Jones [11], [12].

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To date, enormous studies have been devoted to investigate biochemical functions of receptors, which have crucial roles for signal processing in organisms. Ligands are key tools in experiments since receptor specificity with respect to them enables us to control activity of receptors. However, finding ligands is difficult; choosing ligand candidates relies on expert knowledge of biologists and conducting test experiments in vivo or in vitro has a high cost. Here we investigate the ligand finding problem with a machine learning approach by formalizing the problem as multi-label classification mainly discussed in the area of preference learning. We develop in this paper a new algorithm LIFT (Ligand FInding via Formal ConcepT Analysis) for multi-label classification, which can treat ligand data in databases in a semi-supervised manner. The key to LIFT is to achieve clustering by putting an original dataset on lattices using the data analysis technique of Formal Concept Analysis (FCA), followed by obtaining the preference for each label using the lattice structure. Experiments using real data of ligands and receptors in the IUPHAR database show that LIFT effectively solves the task compared to other machine learning algorithms.

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To date, enormous studies have been devoted to investigate biochemical functions of receptors, which have crucial roles for signal processing in organisms. Ligands are key tools in experiments since receptor specificity with respect to them enables us to control activity of receptors. However, finding ligands is difficult; choosing ligand candidates relies on expert knowledge of biologists and conducting test experiments in vivo or in vitro has a high cost. Here we investigate the ligand finding problem with a machine learning approach by formalizing the problem as multi-label classification mainly discussed in the area of preference learning. We develop in this paper a new algorithm LIFT (Ligand FInding via Formal ConcepT Analysis) for multi-label classification, which can treat ligand data in databases in a semi-supervised manner. The key to LIFT is to achieve clustering by putting an original dataset on lattices using the data analysis technique of Formal Concept Analysis (FCA), followed by obtaining the preference for each label using the lattice structure. Experiments using real data of ligands and receptors in the IUPHAR database show that LIFT effectively solves the task compared to other machine learning algorithms.

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The possibility to utilize 3D information for engineering purposes is incleasing by the recent development of various 3D sensors. But because optical 3D sensors such as laser range finder use light, which moves straight, the measurement area is limited to the front face of the object and the back side is not measurable. To adapt such unmeasurable area, we need a system that memorizes shapes of objects that have often encountered and superimposes them to scene. To realize such kind of system, an appropriate 3D shape representation, which is 1) able to compare partial and entire shape information and to detect corresponding areas 2) working as quick as possible for real-time tasks, is needed. We propose a novel representation framework to describe and compare 3D objects: Internal Radiatedlight Projection (IRP) - projection of local shape informations of an object on a sphere by imaginary rays from the proposing reference point “kernel”-. In order to describe local shape information, Harmonic Contour Analysis (HCA), and to arrange them properly the shape matrix are used respectively in IRP framework. The characteristics of IRP are: (1) simple representation for complicated 3D shape information; (2) use of local shape characteristics and their adjacency informations; and moreover, by using shape matrix, (3) simple matching procedure considering the effect of gravity and stable poses for objects. By IRP representation, we can categorize objects in known classes and estimate their positions and attitudes. In this paper, the basic concept of IRP, a reliable way to obtain reference point “kernel”, representation of local 3D shape by HCA, and shape matrix based comparison and categorization of objects are explained. Experiments of object recognition for both virtual and real objects are shown to demonstrate its efficiency and feasibility.

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