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15 results on '"Ohsawa Y"'

1. Region Analysis of Abdominal CT Scans using Image Partition Forests

Author

Golodetz, S, Voiculescu, I, Cameron, S, Chbeir, R, Badr, Y, Abraham, A, Laurent, D, Köppen, M, Ferri, F, Zadeh, LA, Ohsawa, Y, Chbeir, R, Badr, Y, Abraham, A, Laurent, D, Köppen, M, Ferri, F, Zadeh, L, and Ohsawa, Y

Subjects
Parameter identification problem, Tree (data structure), Identification (information), Hierarchy (mathematics), Computer science, Feature (computer vision), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Medical imaging, Segmentation, Data mining, computer.software_genre, computer, Partition (database)
Abstract

The segmentation of medical scans (CT, MRI, etc.) and the subsequent identification of key features therein, such as organs and tumours, is an important precursor to many medical imaging applications. It is a difficult problem, not least because of the extent to which the shapes of organs can vary from one image to the next. One interesting approach is to start by partitioning the image into a region hierarchy, in which each node represents a contiguous region of the image. This is a well-known approach in the literature: the resulting hierarchy is variously referred to as a partition tree, an image tree, or a semantic segmentation tree. Such trees summarise the image information in a helpful way, and allow efficient searches for regions which satisfy certain criteria. However, once built, the hierarchy tends to be static, making the results very dependent on the initial tree construction process (which, in the case of medical images, is done independently of any anatomical knowledge we might wish to bring to bear). In this paper, we describe our approach to the automatic feature identification problem, in particular explaining why modifying the hierarchy at a later stage can be useful, and how it can be achieved. We illustrate the efficacy of our method with some preliminary results showing the automatic identification of ribs. Copyright 2008 ACM.

Published
2008

2. Quadratic Ordered Median Location Problems

Author

Ohsawa, Y., Ozaki, Naoya, Plastria, Frank, Tamura, K., and Mathematics, Operational Research, Statistics and Information Systems for Management

Subjects
Pareto solutions, ordered median problem, arrangement of curves, multi-criteria, Voronoi diagram, location, quadratic euclidean distance
Abstract

The criteria used in location analysis have to be chosen according to the character of the facility. The single facility location models addressed in this paper accommodate simultaneous multiple criteria in a continuous space in the framework of ordered median problems, which generate and unify many standard location problems. We demonstrate that tools of computational geometry such as Voronoi diagrams and arrangements of curves and lines, enable us to identify the entire set of Pareto-optimal locations, when the squared Euclidean distances between the facility and affected inhabitants are used. For two objectives this works for any type of ordered median objectives and any polygonally bounded feasible region. When more than two criteria are present the objectives and the feasible region have to be convex. For the analysis of this last case we extend several recent structural results for unconstrained convex vector optimization to a convex and compact constraint. Our findings are illustrated byseveral examples.

Published
2007

3. Compensation of the Crossing Angle with Crab Cavities at KEKB

Author

Abe, T., Akai, K., Akemoto, M., Akiyama, A., Arinaga, M., Ebihara, K., Egawa, K., Enomoto, A., Flanagan, J., Fukuda, S., Fukuma, H., Funakoshi, Y., Furukawa, K., Furuya, T., Hara, K., Higo, T., Hiramatsu, S., Hisamatsu, H., Honma, H., Honma, T., Hosoyama, K., Ieiri, T., Iida, N., Ikeda, H., Ikeda, M., Inagaki, S., Isagawa, S., Ishii, H., Kabe, A., Kadokura, E., Kageyama, T., Kakihara, K., Kako, E., Kamada, S., Kamitani, T., Kanazawa, K., Katagiri, H., Kato, S., Kawamoto, T., Kazakov, S., Kikuchi, M., Kikutani, E., Kitagawa, K., Koiso, H., Kojima, Y., Komada, I., Kubo, T., Kudo, K., Kudo, N., Marutsuka, K., Masuzawa, M., Matsumoto, S., Matsumoto, T., Michizono, S., Mikawa, K., Mimashi, T., Mitsunobu, S., Mori, K., Morita, A., Morita, Y., Nakai, H., Nakajima, H., Nakamura, T. T., Nakanishi, H., Nakanishi, K., Nakao, K., Ninomiya, S., Ogawa, Y., Ohmi, K., Ohsawa, S., Ohsawa, Y., Ohnishi, Y., Ohuchi, N., Oide, K., Ono, M., Ozaki, T., Saito, K., Sakai, H., Sakamoto, Y., Sato, M., Satoh, M., Shibata, K., Shidara, T., Shirai, M., Shirakawa, A., Sueno, T., Suetake, M., Suetsugu, Y., Sugahara, R., Sugimura, T., Suwada, T., Tajima, O., Takano, S., Takasaki, S., Takenaka, T., Takeuchi, Y., Tawada, M., Tejima, M., Tobiyama, M., Tokuda, N., Uehara, S., Uno, S., Yamamoto, Y., Yano, Y., Yokoyama, K., Yoshida, Ma., Yoshida, Mi., Yoshimoto, S., Yoshino, K., KEK, Oho, Tsukuba, Perevedentsev, E., Shatilov, D. N., and BINP

Subjects
Physics - Instrumentation and Detectors, Astrophysics::High Energy Astrophysical Phenomena, FOS: Physical sciences, Physics::Accelerator Physics, High Energy Physics::Experiment, Instrumentation and Detectors (physics.ins-det)
Abstract

Crab cavities have been installed in the KEKB B--Factory rings to compensate the crossing angle at the collision point and thus increase luminosity. The beam operation with crab crossing has been done since February 2007. This is the first experience with such cavities in colliders or storage rings. The crab cavities have been working without serious issues. While higher specific luminosity than the geometrical gain has been achieved, further study is necessary and under way to reach the prediction of simulation., Submitted to Particle Accelerator Conference 2007, MOZAKI01, Albuquerque

Published
2007

4. Maps, agents and dialogue for exploring a virtual world

Author

Nijholt, Anton, Zwiers, Jakob, van Dijk, Betsy, Callaos, N., Ohsawa, Y., Zhang, Y., Szabo, R., and Aveledo, M.

Subjects
HMI-VRG: Virtual Reality and Graphics, IR-66299, METIS-203257, EWI-6632
Abstract

In previous years we have been involved in several projects in which users (or visitors) had to find their way in information-rich virtual environments. 'Information-rich' means that the users do not know beforehand what is available in the environment, where to go in the environment to find the information and, moreover, users or visitors do not necessarily know exactly what they are looking for. Information-rich means also that the information may change during time. A second visit to the same environment will require different behavior of the visitor in order for him or her to obtain similar information than was available during a previous visit. In this paper we report about two projects and discuss our attempts to generalize from the different approaches and application domains to obtain a library of methods and tools to design and implement intelligent agents that inhabit virtual environments and where the agents support the navigation of the user/visitor.

Published
2001

10. Letter of intent for KEK Super $B$ Factory

Author

Hashimoto, S., Hazumi, M., Haba, J., Flanagan, J. W., Ohnishi, Y., Abe, K., Abe, T., Adachi, I., Agoh, T., Akai, K., Akemoto, M., Akeroyd, A. G., Arinaga, M., Aulchenko, V., Aziz, T., Bahinipati, S., Bakich, A. M., Baner, C., Banerjee, S., Bartel, W., Bauer, Christian W., Bedny, I., Bigi, I., Bitenc, U., Bizjak, Ilija, Bondar, A., Bozek, A., Bracko, M., Brodzicka, J., Browder, T. E., Chang, P., Chen, A. E., Chen, K. F., Cheon, B. G., Choi, Y. I., Cole, S., Danilov, M., Drutskoy, A., Ebihara, K., Egawa, K., Eidelman, S., Enari, Y., Endo, K., Enomoto, A., Epifanov, D., Ezura, E., Fratina, S., Fukuda, S., Fukuma, H., Funakoshi, Y., Furukawa, K., Furuya, T., Garmash, A., Gershon, T., Gokhroo, G., Golob, B., Gorisek, A., Goto, T., Grossman, Y., Gurtu, A., Handa, F., Hara, K., Hara, T., Hastings, N. C., Hayashii, H., Higuchi, T., Hinz, L., Hiramatsu, S., Hirata, K., Hisano, J., Honma, H., Hosoyama, K., Hou, W. S., Ieiri, T., Iida, N., Iijima, T., Ikeda, H., Ikeda, M., Inami, K., Isagawa, S., Ishino, H., Itoh, R., Iwasaki, Y., Jin, L., Kabe, A., Kageyama, T., Kakihara, K., Kakuno, H., Kamada, S., Kamitani, T., Kanazawa, K., Kang, J. S., Kapusta, P., Katagiri, H., Katayama, N., Katoh, T., Kawamura, H., Kawasaki, T., Keum, Y. Y., Khan, M. H. R., Kichimi, H., Kikuchi, M., Kikutani, E., Kim, J. H., Kim, S. K., Kim, T. H., Kinoshita, K., Koiso, H., Kojima, Y., Konstantin, B., Korpar, S., Kou, E., Krizan, P., Krokovny, P., Kubo, T., Kumar, R., Kumar, S., Kurimoto, T., Kurokawa, S., Kuzmin, A., Kwon, Y. J., Lange, J. S., Leder, G., Lee, J., Lee, S. E., Lee, S. H., Lesiak, T., Li, J., Macnaughton, J., Majumder, G., Mandl, F., Masuzawa, M., Matsumoto, T., Matyja, A., Mao, Z., Mazumdar, K., Michizono, S., Mikhail, S., Mimashi, T., Mishima, S., Mitsuhashi, T., Mitsunobu, S., Miyabayashi, K., Mizuk, R., Moloney, G. R., Morita, A., Morita, Y., Morozumi, T., Murakami, A., Nagamine, T., Nagasaka, Y., Nakajima, H., Nakamura, I., Nakamura, T. T., Nakanishi, H., Nakano, E., Nakao, K., Nakao, M., Nakayama, H., Natkaniec, Z., Nishida, S., Nozaki, T., Ogawa, Y., Ohmi, K., Ohsawa, S., Ohsawa, Y., Ohshima, T., Ohuchi, N., Oide, K., Okada, Y., Okuno, S., Olsen, S. L., Ono, M., Onogi, T., Oogoe, T., Ostrowicz, W., Ozaki, H., Ozaki, T., Pakhlov, P., Palka, H., Park, C. W., Park, H., Park, I. H., Parslow, N., Peak, L. S., Pernicka, M., Pestotnik, R., Peters, M., Piilonen, L. E., Pirjol, D., Poluektov, A., Qiu, J., Recksiegel, S., Root, N., Rozanska, M., Rusinov, V., Sagawa, H., Sakai, Y., Sanda, A. I., Satoh, M., Schumann, J., Schietinger, T., Schonmeier, P., Schwanda, C., Schwartz, Alan J., Senyo, K., Sevior, M. E., Shibata, T., Shidara, T., Shirakawa, A., Shwartz, B., Luca Silvestrini, Singh, J. B., Sokolov, A., Somov, A., Son, D., Soni, A., Soni, N., Stamen, R., Stanic, S., Staric, M., Stock, R., Sudhakar, K., Suetake, M., Suetsugu, Y., Sugahara, R., Sugimura, T., Sumisawa, K., Sumiyoshi, T., Suwada, T., Suzuki, S., Suzuki, S. Y., Tajima, O., Takasaki, F., Takenaka, T., Takeuchi, Y., Tamai, K., Tanaka, K., Tanaka, M., Tawada, M., Taylor, G. N., Tejima, M., Teramoto, Y., Tikhomirov, I., Tobiyama, M., Trabelsi, K., Tsuboyama, T., Tsuchiya, K., Tsukamoto, T., Uehara, S., Uglov, T., Uno, S., Ushiroda, Y., Varner, Gary Sim, Varvell, K. E., Villa, S., Wang, C. H., Wang, L. F., Wang, M. Z., Yamaguchi, S., Yamamoto, H., Yamauchi, M., Yano, Y., Ying, J., Yoshida, M., Yoshikawa, T., Yoshimoto, S., Yoshioka, M., Yuan, Y., Zhang, C. C., Zhang, J., Zhang, Z. P., and Zurcher, D.

13. Performance of KEKB with crab cavities

Author

Funakoshi, Y., Abe, T., Akai, K., Akemoto, M., Akiyama, A., Arinaga, M., Ebihara, K., Egawa, K., Enomoto, A., Flanagan, J., Fukuda, S., Fukuma, H., Furukawa, K., Furuya, T., Hara, K., Higo, T., Hiramatsu, S., Hisamatsu, H., Honma, H., Honma, T., Hosoyama, K., Ieiri, T., Iida, N., Ikeda, H., Ikeda, M., Inagaki, S., Isagawa, S., Ishii, H., Kabe, A., Kadokura, E., Kageyama, T., Kakihara, K., Kako, E., Kamada, S., Kamitani, T., Kanazawa, K., Katagiri, H., Kato, S., Kawamoto, T., Kazakov, S., Kikuchi, M., Kikutani, E., Kitagawa, K., Koiso, H., Kojima, Y., Komada, I., Kubo, T., Kudo, K., Kudo, N., Marutsuka, K., Masuzawa, M., Matsumoto, S., Matsumoto, T., Michizono, S., Mikawa, K., Mimashi, T., Mitsunobu, S., Takako Miura, Mori, K., Monta, A., Monta, Y., Nakai, H., Nakajima, H., Nakamura, T. T., Nakanishi, H., Nakanishi, K., Nakao, K., Ninomiya, S., Nishiwaki, M., Ogawa, Y., Ohmi, K., Ohsawa, S., Ohsawa, Y., Ohnishi, Y., Ohuchi, N., Oide, K., Ono, M., Ozaki, T., Saito, K., Sakai, H., Sakamoto, Y., Sato, M., Satoh, M., Shibata, K., Shidara, T., Shirai, M., Shirakawa, A., Sueno, T., Suetake, M., Suetsugu, Y., Sugahara, R., Sugimura, T., Suwada, T., Tajima, O., Takano, S., Takasaki, S., Takenaka, T., Takeuchi, Y., Tawada, M., Tejima, M., Tobiyama, M., Tokuda, N., Uehara, S., Uno, S., Yamamoto, Y., Yano, Y., Yokoyama, K., Yoshida, Ma, Yoshida, Mi, Yoshimoto, S., Yoshino, K., Perevedentsev, E., and Shatilov, D. N.

14. Positive Artificial Intelligence Meets Affective Walkability

Author

Stefania Bandini, Francesca Gasparini, Flavio S. Correa da Silva, Ohsawa, Y, Bandini, S, Gasparini, F, and Silva, F

Subjects
Human-centered design, Positively intelligent neighborhood, Affective walkability, Positively intelligent design agent, Positive technologie
Abstract

Urban design has followed different schools and traditions, among which we highlight the smart cities and the human-centered cities initiatives, which contrast in certain respects and are complementary in others, similar to how traditional psychology and positive psychology are contrasting and complementary, and to how artificial intelligence has followed contrasting and complementary development pathways along automation of human tasks and augmentation of human capabilities. In this chapter we explore these traditions to establish the concept of positively intelligent neighborhoods, as a convergence of intelligent sensing and positive technologies to design human-centered neighborhoods based on the notion of affective walkability with the support of positively intelligent design agents.

Published
2023

15. Using sequence constraints for modelling network interactions

Author

De Smedt, Johannes, Mori, Junichiro, Ochi, Masanao, Ohsawa, Y, Yada, K, Ito, T, Takama, Y, Sato-Shimokawara, E, Abe, A, Mori, J, and Matsumura, N

Subjects
sequence mining, network analysis
Abstract

This is an extension from a selected paper from JSAI2019. The ubiquitous nature of networks has led to a vast number of works dedicated to the study of capturing their information. Various graph-based techniques exist that report on the characteristics of nodes and edges, e.g., author-citation networks, social interactions, and so on. A significant amount of information can be extracted by summarizing the surrounding network structure of nodes, e.g., by capturing motives, or walk patterns. In this work, we present a new way of capturing the interaction between nodes in a network by making use of the sequence in which they occur. (1) The objective of this paper is to make use of behavioural constraint patterns; a concise but detailed report of node's interactions can be constructed that can be used for various purposes. (2) It is shown how the constraint patterns can be mined form interaction data, and how they can be used for various applications. (3) A case study is presented which uses behavioural constraint patterns to profile user interactions in a communication network.

Published
2020
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