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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 30  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2023-03-06
15:34
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Reproducing method of cancer annotation by local features in pathological images
Shunya Mutsuda (KIT), Sohsuke Yamada (Kanazawa Medical Univ.), Toshiki Kindo (KIT) MI2022-90
In recent years, AI-based pathological image diagnosis technology has been actively researched. Therefore, in this study... [more] MI2022-90
pp.82-87
EMM 2021-01-28
14:45
Online Online Proposal of a method using color extraction processing in the QR code authenticity judgment algorithm
Kohei Shirai, Kitahiro Kaneda, Keiichi Iwamura (Tokyo Univ. of Science) EMM2020-64
(To be available after the conference date) [more] EMM2020-64
pp.19-24
MI 2020-01-29
10:05
Okinawa OKINAWAKEN SEINENKAIKAN A study of generalized generation of image features for computer-aided detection systems based on unsupervised learning with normal datasets -- Experimental evaluations of feature generation by small datasets --
Kazuyuki Ushifusa, Mitsutaka Nemoto(, Yuichi Kimura, Takashi Nagaoka, Takahiro Yamada, Atsuko Tanaka (Kindai Uni.), Naoto Hayashi (The Uni of Tokyo Hosp) MI2019-68
In a computer-aided detection system, image features are essential factors. In this study, we propose an image feature g... [more] MI2019-68
pp.15-18
MI 2020-01-30
15:10
Okinawa OKINAWAKEN SEINENKAIKAN Exploration of image features associated with ruptures of intracranial aneurysms using magnetic resonance angiography
Masayuki Yamanouchi (KMH), Hidetaka Arimura (Kyushu Univ.) MI2019-120
Ruptures of intracranial aneurysms are serious conditions resulting in subarachnoid hemorrhage. We hypothesized that the... [more] MI2019-120
pp.235-236
IE, ITE-ME, ITE-AIT [detail] 2017-10-05
14:50
Nagasaki   Segmentation of Campus Images based on Image Features for Building Recognition
Kenta Yamanishi, Shun Hattori, Yukinori Suzuki (Muroran-IT) IE2017-51
We have been studying building recognition in natural images by taking picture in a college campus. Itis difficult to re... [more] IE2017-51
pp.25-30
MBE, NC
(Joint)
2017-03-14
10:50
Tokyo Kikai-Shinko-Kaikan Bldg. Relationship between Image Statistics in Natural Movie and Responses of Neurons in Visual Areas V1 and V4
Gaku Hatanaka (Osaka Univ.), Koji Ikezoe (Univ. of Yamanashi), Ryosuke F Takeuchi, Mikio Inagaki (Osaka Univ.), Shinji Nishimoto (NICT), Ichiro Fujita (Osaka Univ.) NC2016-89
We studied the response preference for image statistics of neurons in macaque visual cortical areas V1 and V4. A large p... [more] NC2016-89
pp.149-154
MBE, NC
(Joint)
2017-03-14
13:10
Tokyo Kikai-Shinko-Kaikan Bldg. Analysis of temporal change of astrocyte morphology under hypoxial adaptation using higher-order image features
Tomohiro Nishino, Sosuke Tanaka, Masahiro Nitta, Takuma Sugashi, Kazuto Masamoto, Yoichi Miyawaki (UEC Tokyo) NC2016-91
Astrocytes play an important role in controlling oxygen and nutrition from the blood to neurons. Although the shape of a... [more] NC2016-91
pp.159-164
EMM 2017-03-06
14:30
Okinawa Miyakojima Marin Terminal Building (Okinawa) [Poster Presentation] A DCT-OFDM Based Digital Watermarking Scheme Using Synchronization Recovery with Image Features
Yui Ichioka, Tetsuya Kojima (NIT, Tokyo College) EMM2016-89
In today’s highly digitized society, the services providing digital contents such as movies, images and music become ver... [more] EMM2016-89
pp.19-24
SIS 2017-03-02
11:30
Kanagawa Kanagawa Inst. Tech. Yokohama Office Discrimination of Printed Images Using Image Features and Its Application
Mori Ryosuke, Mitsuji Muneyasu (Kansai Univ.) SIS2016-45
If we make to obtain the information during our movement by using a mobile terminal, we may need to use the relationship... [more] SIS2016-45
pp.19-24
HCGSYMPO
(2nd)
2016-12-07
- 2016-12-09
Kochi Kochi City Culture Plaza (CUL-PORT) Estimating the Location of Pedestrian Using Similar Image Retrieval Between Different Image Groups of Shooting Environment
Kazuho Kamasaka, Itaru Kitahara, Yoshinari Kameda (Tsukuba Univ.)
We are studying a walker navigation system for visual impaired people. It need to estimate pedestrian’s location by simi... [more]
IMQ, HIP 2016-07-29
15:45
Hokkaido Sapporp City Univ. A noise predictor based on image features for an IQA database
Yasuhiro Inazumi, Naho Ito, Yuukou Horita (Univ. of Toyama) IMQ2016-14 HIP2016-40
A set of images and mean opinion scores is needed to develop an objective image quality assessment (IQA). Recently, publ... [more] IMQ2016-14 HIP2016-40
pp.43-47
MBE, NC
(Joint)
2016-03-22
11:25
Tokyo Tamagawa University Image classification of astrocytes for pre- and post-hypoxia adaptation using deep convolutional neural network
Sosuke Tanaka, Masahiro Nitta, Kazuto Masamoto, Yoichi Miyawaki (UEC Tokyo) NC2015-91
Abstract Astrocytes are a type of glial cells that regulate transportation of nutrition from blood to neurons. Previous... [more] NC2015-91
pp.125-130
ITS, IEE-ITS 2016-03-10
11:20
Kyoto Kyoto Univ. Robust Multi-directional Bicycle Recognition under Pose Variation using Stereo Vision
Kousuke Matsushima, Kenta Fukushima (NIT, Kurume College), Masahiro Iwahashi (NUT), Keiichi Uchimura (Kumamoto Univ.) ITS2015-88
Many researches have worked for the development of automotive technologies, so as to realize a convenient and safety aut... [more] ITS2015-88
pp.23-28
IE, ITS, ITE-AIT, ITE-HI, ITE-ME, ITE-MMS, ITE-CE [detail] 2015-02-23
13:30
Hokkaido Hokkaido Univ. Image Feature Descriptor of Rotation-Invariant Gradient Orientation for Pedestrian Detection under Changes of Posture
Yuta Horikawa, Kousuke Matsushima (Kurume NCT) ITS2014-42 IE2014-69
Pedestrian recognition is an important subject of research in automotive safe driving support technology. Combining imag... [more] ITS2014-42 IE2014-69
pp.53-58
PRMU, CNR 2015-02-20
14:50
Miyagi   Predicting User Demographics and Click Through Rate of Display Ads Applying a Generic Image Recognition Methodology
Kohei Yamamoto (The Univ. of Tokyo), Tetsuya Motegi, Yukihiro Tagami, Hayato Kobayashi, Shingo Ono (Yahoo Japan), Hideki Nakayama (The Univ. of Tokyo) PRMU2014-149 CNR2014-64
In the field of click-through rate (CTR) prediction of pay per click display ads, the latency and the cold-start problem... [more] PRMU2014-149 CNR2014-64
pp.179-184
SP 2015-01-22
10:25
Gifu Juroku Plaza A study for the robustness of multi-modal voice conversion
Daiki Kawashima, Satoshi Tamura, Satoru Hayamizu (Gifu Univ.) SP2014-128
Voice Conversion (VC) is a technique to convert speeches of source speaker into those of target speaker. VC has an issue... [more] SP2014-128
pp.7-12
NLC, IPSJ-NL, SP, IPSJ-SLP, JSAI-SLUD
(Joint) [detail]
2014-12-16
11:00
Kanagawa Tokyo Institute of Technology (Suzukakedai Campus) Multimodal Voice Conversion using Weighted Features in Noisy Environments
Kenta Masaka, Ryo Aihara, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2014-116
Voice conversion is a technique for converting specific information in speech while maintaining the other information, s... [more] SP2014-116
pp.87-92
PRMU 2014-03-13
14:30
Tokyo   Restoration of Archaeological Artifacts Based on ORB Image Features
Koji Kashihara (Univ. of Tokushima) PRMU2013-185
The restoration work of archaeological artifacts broken into pieces includes positioning problems. An intelligent comput... [more] PRMU2013-185
pp.103-107
ITS, IE, ITE-AIT, ITE-HI, ITE-ME [detail] 2014-02-18
13:35
Hokkaido Hokkaido Univ. HOG Feature based Polar Coordinates for Pedestrian Detection
Yuta Horikawa, Kousuke Matsushima (KurumeNCT) ITS2013-65 IE2013-130
Recently, the number of traffic accidents and injured people has been decreasing. However, the traffic-related pedestria... [more] ITS2013-65 IE2013-130
pp.357-362
SIP, CAS, CS 2013-03-15
15:10
Yamagata Keio Univ. Tsuruoka Campus (Yamagata) A Study on Multimodal Biometrics Authentication Method Using Features in Utterance
Takeshi Nishino, Yoshinobu Kajikawa, Mitsuji Muneyasu (Kansai Univ.) CAS2012-141 SIP2012-172 CS2012-147
In this paper, we propose a biometrics authentication method using multimodal features in utterance.
The multimodal fea... [more]
CAS2012-141 SIP2012-172 CS2012-147
pp.259-264
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