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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 41  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
MI 2022-09-15
14:00
Kanagawa
(Primary: On-site, Secondary: Online)
Unsupervised Cell Detection for Suppression of Background Information in Cytology
Keita Takeda, Kazuki Matsuo, Kohei Fujiwara, Eiji Mitate, Tomoya Sakai (Nagasaki Univ.) MI2022-57
(To be available after the conference date) [more] MI2022-57
pp.35-38
SIS, IPSJ-AVM 2022-06-09
13:40
Fukuoka KIT(Wakamatsu Campus)
(Primary: On-site, Secondary: Online)
Junction Detection and Recognition Based on Results of Feature Matching Computed by SuperGlue
Kohei Saito, Hiroaki Sudo, Kazufumi Honda, Marin Wada, Yuriko Ueda, Yuki Yuda, Miho Adachi, Ryusuke Miyamoto (Meiji Univ.) SIS2022-3
We attempt to actualize a novel visual navigation scheme
that strongly depends on results of semantic segmentation.
To... [more]
SIS2022-3
pp.12-17
SIS, IPSJ-AVM 2022-06-09
14:30
Fukuoka KIT(Wakamatsu Campus)
(Primary: On-site, Secondary: Online)
Orientation Control at An Intersection Based on Semantic Segmentation for Visual navigation
Kazufumi Honda, Kouhei Saito, Hiroaki Sudo, Yuki Yuda, Yuriko Ueda, Marin Wada, Miho Adachi, Ryusuke Miyamoto (Meiji Univ.) SIS2022-5
We attempt to actualize a novel visual navigation scheme that strongly depends on results of semantic segmentation. The ... [more] SIS2022-5
pp.24-29
SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] 2022-05-20
10:40
Kumamoto Kumamoto University Kurokami Campus
(Primary: On-site, Secondary: Online)
SIP2022-19 BioX2022-19 IE2022-19 MI2022-19 n this paper, GAN learning was performed using a dataset of live-action pig images taken in the previous year to generat... [more] SIP2022-19 BioX2022-19 IE2022-19 MI2022-19
pp.97-102
CCS 2022-03-27
16:05
Hokkaido RUSUTSU RESORT HOTEL & CONVENTION
(Primary: On-site, Secondary: Online)
Range-Equivariant Convolution for Spherical Projection-based Segmentation of LiDAR Point Clouds
Hidetaka Marumo, Takashi Matsubara (Osaka Univ) CCS2021-49
In autonomous driving, LiDAR point clouds segmentation has attracted much attention. For efficiency and ease of design, ... [more] CCS2021-49
pp.78-83
PRMU, IPSJ-CVIM 2022-03-11
16:10
Online Online Spatial attention and parallel convolution for Real-time segmentation
Yuki Sugimoto, Masaki Aono (TUT) PRMU2021-86
Semantic segmentation is an image recognition technique that classifies all objects and backgrounds by pixel in images. ... [more] PRMU2021-86
pp.163-168
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-21
15:35
Online Online Liver Tumor Segmentation by Using a Massive-Training Artificial Neural Network (MTANN) and its Analysis in Liver CT.
Yuqiao Yang, Muneyuki Sato, Ze Jin, Kenji Suzuki (Tokyo Tech) ITS2021-33 IE2021-42
Based on a 3D massive-training artificial neural network (MTANN) combined with a Hessian-based ellipse enhancer, a small... [more] ITS2021-33 IE2021-42
pp.49-54
CQ, CBE
(Joint)
2022-01-27
16:05
Ishikawa Kanazawa(Ishikawa Pref.)
(Primary: On-site, Secondary: Online)
Proposal and evaluation of 3D-point object estimation method based on probability space representation
Hiroaki Sato, Shin'ichi Arakawa, Masayuki Murata (Osaka Univ.) CQ2021-83
New network services are expected to emerge using real spatial information in remote areas. For the advancement of servi... [more] CQ2021-83
pp.39-44
NS, NWS
(Joint)
2022-01-28
13:05
Online Online A Case for Federated Learning with Training Data Generation for Walking Navigation
Yuto Hoshino, Hiroki Matsutani (Keio Univ.) NS2021-119
(To be available after the conference date) [more] NS2021-119
pp.50-55
MI, MICT [detail] 2021-11-05
10:45
Online Online [Short Paper] A Method for Extracting and Visualizing Calcified Areas from Coronary OCT Images.
Ryo Oikawa, Toru Kato, Akio Doi (Iwate Prefectural Univ.), Masaru Ishida (Iwate Medical Univ.) MICT2021-32 MI2021-30
Optical coherence tomography (OCT) has been widely used to diagnose calcified areas in coronary arteries in the last dec... [more] MICT2021-32 MI2021-30
pp.22-25
MBE, NC
(Joint)
2021-10-28
15:05
Online Online Enhancement of spatio-temporal coding performance in spiking neural network and its application to hazard detection for landing of spacecrafts
Hideaki Kinoshita, Shinichi Kimura (TUS), Seisuke Fukuda (JAXA) NC2021-21
Spiking neural networks (SNNs) are a neuromimetic computational architecture that has attracted much attention in recent... [more] NC2021-21
pp.16-21
MI 2021-07-08
14:30
Online Online Extraction of Calcified Regions from OCT Images Using Deep Learning
Ryo Oikawa, Toru Kato, Akio Doi, Basabi Chakraborty (Iwate Prefectural Univ.), Masaru Ishida (Iwate Medical Univ.) MI2021-12
Stenosis or occlusion of coronary arteries leads to angina attacks and myocardial infarctions, and coronary artery disea... [more] MI2021-12
pp.15-19
SIS 2021-03-04
13:30
Online Online Improvement of Detection Accuracy of Calcification Regions from Dental Panoramic Radiograph Using Deep Learning
Taito Murano, Mitsuji Muneyasu, Soh Yoshida, Akira Asano (Kansai Univ.), Keiichi Uchida (Matsumoto Dental Univ. Hospital), Dewake Nanae, Yasuaki Ishioka, Nobuo Yoshinari (Matsumoto Dental Univ.) SIS2020-45
Dental panoramic radiographs may show calcified areas that are a sign of vascular disease. Finding these areas in dentis... [more] SIS2020-45
pp.55-60
PRMU, IPSJ-CVIM 2021-03-05
14:10
Online Online A Consideration on Suspicious Object Detection by Mixup and Improved U-Net
Naruki Kanno, Wataru Kameyama, Toshio Sato, Yutaka Katsuyama, Takuro Sato (Waseda Univ.) PRMU2020-90
In this paper, on suspicious object detection by using semantic segmentation, we study the effectiveness of Mixup data a... [more] PRMU2020-90
pp.121-126
PRMU, IPSJ-CVIM 2021-03-05
14:25
Online Online Semantic Segmentation based on MobileNet Extended with FPN
Yuki Sugimoto, Masaki Aono (TUT) PRMU2020-91
Semantic Segmentation is attracting attention in autonomous driving, but high-precision models require a huge amount of ... [more] PRMU2020-91
pp.127-132
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2021-02-18
14:50
Online Online Production and Evaluation of Data Set for Semantic Segmentation of 3D CG Image by H.265/HEVC
Norifumi Kawabata (Tokyo Univ. of Science) ITS2020-30 IE2020-44
As one of purpose of study on image segmentation, we are able to consider whether between object and background region c... [more] ITS2020-30 IE2020-44
pp.19-24
CPSY, RECONF, VLD, IPSJ-ARC, IPSJ-SLDM [detail] 2021-01-26
09:50
Online Online FPGA Implementation of Semantic Segmentation on LWIR Images for Autonomous Robot
Yuichiro Niwa (ATLA), Taiki Fujii (eSOL) VLD2020-57 CPSY2020-40 RECONF2020-76
Recently, deep learning of images has made remarkable progress, and its results are being applied to the automatic
reco... [more]
VLD2020-57 CPSY2020-40 RECONF2020-76
pp.101-106
PRMU 2020-12-17
14:55
Online Online Improving the accuracy of unsupervised segmentation by introducing a Laplacian filter loss function -- Application to automotive wire harness components --
Yuki Matsumoto (SEI) PRMU2020-45
Semantic segmentation, in which images are classified into pixel-by-pixel classes by deep learning, has been widely stud... [more] PRMU2020-45
pp.42-46
IE, IMQ, MVE, CQ
(Joint) [detail]
2020-03-06
13:25
Fukuoka Kyushu Institute of Technology
(Cancelled but technical report was issued)
Detection of running area from forest road images with different image quality using deep learning
Misato Ushiro, Tetsuya Higashino, Yuukou Horita (Univ. of Toyama) IMQ2019-60 IE2019-142 MVE2019-81
Natural disasters such as falling rocks and landslides are increasing year by year on forest roads in hilly and mountain... [more] IMQ2019-60 IE2019-142 MVE2019-81
pp.231-234
ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2020-02-27
10:00
Hokkaido Hokkaido Univ.
(Cancelled but technical report was issued)
A lane estimation method by road image analysis with influence reduction of other vehicles
Kohei Mori, Yuki Yokohata, Takahiro Hata, Aki Hayashi, Kazuaki Obana (NTT) ITS2019-31 IE2019-69
We have been promoting smart mobility like advanced navigation services or driving support services. GPS is one of usefu... [more] ITS2019-31 IE2019-69
pp.135-138
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