研究者詳細 - 丹上　結乃純

論文 - 丹上　結乃純

分割表示601 件中 101 - 120 件目／全件表示 >>

Segmentation of Spinal Canal Region in CT Images using 3D Region Growing Technique

Fu G., Lu H., Tan J., Kim H., Zhu X., Lu J.

2018 International Conference on Information and Communication Technology Robotics 1 - 4 2018年11月

Object Detection on Video Images Based on R-FCN and GrowCut Algorithm

Mouri K., Lu H., Tan J.K., Kim H.

2018 International Conference on Information and Communication Technology Robotics 1 - 4 2018年11月

Feature Transform Optimization for Pedestrian Classification 査読有り

Nakashima Y., Tan J.

2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2018 804 - 809 2018年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

In this paper, we propose a FTOP (Feature Transform Optimization Problem) and its solution. We propose a method to optimize both parameters and processing order of feature transform simultaneously, not limited to convolution and pooling included in CNN (Convolutional Neural Network). In order to realize the optimization, we formulate it as a combinatorial optimization problem and solve it by meta-heuristics. The effectiveness of the proposed method is shown by applying the proposed method to pedestrian classification based on a benchmark data set.

DOI： 10.23919/SICE.2018.8492633

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85056737437&origin=inward

Detection of circulating tumor cells in fluorescence microscopy Image based on ANN classifier 査読有り

辻,陸　慧敏,タン　ジュークイ,金　亨燮,田中

Journal of Mobile Networks and Application 2018年10月

Registration of phalange region from CR Images based on genetic algorithm 査読有り

Kawagoe K., Murakami S., Lu H., Tan J.K., Kim H., Aoki T.

Proceedings of the 18th International Conference on Control, Automation and Systems 1464 - 1467 2018年10月

Feature Transform Optimization for Pedestrian Classification 査読有り

中島　祐樹,タン　ジュークイ

Proc. of SICE Annual Conference 2018 804 - 809 2018年09月

Activity representation by SURF-based templates 査読有り

Ahad M., Tan J., Kim H., Ishikawa S.

Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization 6 ( 5 ) 573 - 583 2018年09月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. This paper presents a method to recognise actions, which are overlapping and multi-dimensionalities. A spatio-temporal representation is illustrated on local interest points to compute global features. Motion history image (MHI) is computed and motion overwriting the motion overwriting problem of the MHI. The main contribution of this paper is that it demonstrates a higher discriminative ability of various complex actions when compared to the other MHI-based approaches. It selects local interest feature points to capture motion information using Speeded-Up Robust Features (SURF). These key interest points are exploited to compute gradient-based optical flow into four channels. RANSAC is exploited to remove outliers. It incorporates frame-subtracted accumulated image so that we can mask out points that are not required. Afterwards, feature vectors are computed based on moments. Actions are recognised by employing a nearest neighbour classifier and leave-one-out cross-validation partitioning scheme. The proposed method provides satisfactory recognition rates over several other approaches for some challenging actions in outdoor scenes.

DOI： 10.1080/21681163.2017.1298472

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85016247850&origin=inward

Automatic identification of Bone Erosions in rheumatoid Arthritis from Hand Radiographs Based on Deep Convolution Neural Network 査読有り

Seiichi Murakami, Kazuhiro Hatano, Joo Kooi Tan, Hyoungseop Kim, Takatoshi Aoki

Multimedia Tools and Applications 77 ( 9 ) 10921 - 10937 2018年09月

Environment recognition for electric wheelchair based on YOLOv2 査読有り

Sakai Y., Lu H., Tan J.K., Kim H.

ACM International Conference Proceeding Series 112 - 117 2018年08月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

At present, the aging population is growing in Japan. Along with that, the need for the utilization of welfare equipment is increasing. Electric wheelchair, a convenient transportation tool, is popularized rapidly. However, many accidents have occurred by using electric wheelchair, and the dangers for driving are pointed out. Therefore, it needs to improve accident factors, reduce accidents and improve the convenience of electric wheelchair by automation. Environmental recognition is the key technology for developing autonomous electric wheelchair. Environmental recognition includes self-position estimation, recognition of sidewalks, crosswalks, traffic lights, and moving object prediction, etc. In order to solve these problems, this paper describes a system for the detection of sidewalks, crosswalks and traffic lights. We develop the object recognition methods using a modified YOLOv2 that is one of object detection algorithms applying convolutional neural networks (CNN). We detect the object through YOLOv2 and perform processing such as unnecessary bounding box deletion and interpolation. The experimental results demonstrate that the area under the curve (AUC) of the detection rate is 0.620.

DOI： 10.1145/3278229.3278231

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85058546828&origin=inward

Environment recognition for electric wheelchair based on YOLOv2 査読有り

Y. Sakai, H.,Lu, Joo Kooi Tan, Hyoungseop Kim

The 3rd International Conference on Biomedical Signal and Image Processing, ICBIP 2018 112 - 117 2018年08月

物体識別における特徴量変換構造の最適化査読有り

中島祐樹,タンジュークイ

バイオメディカル・ファジィ・システム学会誌 20 ( １ ) １ - １３ 2018年05月

Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network 査読有り

Murakami S., Hatano K., Tan J., Kim H., Aoki T.

Multimedia Tools and Applications 77 ( 9 ) 10921 - 10937 2018年05月

記述言語：英語掲載種別：研究論文（学術雑誌）

Although radiographic assessment of joint damage is essential in characterizing disease progression and prognosis in patients with rheumatoid arthritis (RA), it is often difficult even for trained radiologists to find radiographic changes on hand and foot radiographs because lesion changes are often subtle. This paper proposes a novel quantitative method for automatically detecting bone erosion on hand radiographs to assist radiologists. First, the proposed method performs with the crude segmentation of phalanges regions from hand radiograph and extracts the detailed phalanges regions by the multiscale gradient vector flow (MSGVF) Snakes method. Subsequently, the region of interest (ROI; 40 × 40 pixels) is automatically set on the contour line of the segmented phalanges by the MSVGF algorithm. Finally, these selected ROIs are identified by the presence or absence of bone erosion using a deep convolutional neural network classifier. This proposed method is applied to the hand radiographs of 30 cases with RA. The true-positive rate and the false-positive rate of the proposed method are 80.5% and 0.84%, respectively. The number of false-positive ROIs is 3.3 per case. We believe that the proposed method is useful for supporting radiologists in imaging diagnosis of RA.

DOI： 10.1007/s11042-017-5449-4

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85056120147&origin=inward

Detection of a specific moving object from head-mounted camera images 査読有り

Ishitobi K., Tan J.K., Kim H., Ishikawa S.

SII 2017 - 2017 IEEE/SICE International Symposium on System Integration 2018-January 817 - 822 2018年02月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2017 IEEE. In this paper, a method is proposed for detecting and tracking a specific moving object (e.g., a bus) on the road from images of a camera attached to the head of a user, aiming at developing a system to support daily lives of visually impaired people. The proposed method traces feature points on the images, extracts a moving object region, and detects a bus by applying Haar-like feature and random trees to the region. The effectiveness of the proposed method is shown experimentally.

DOI： 10.1109/SII.2017.8279323

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85050871522&origin=inward

Development of a Support System for Human Communication Based on the Visualization of Words 査読有り

Kazuya Motoyama, Tan Joo Kooi, Ishikawa Seiji

The 23rd International Symposium on Artificial Life and Robotics 2018 329 - 332 2018年01月

A unified action recognition framework 査読有り

Amine Ilidrissi, Tan Joo Kooi, Kim Hyoungseop,Ishikawa Seiji

The 23rd International Symposium on Artificial Life and Robotics 2018 57 - 62 2018年01月

Bone Erosions Detection on Hand CR Images Based on DCNN 査読有り

Seiichi Murakami, Kazuhiro Hatano, Huimin Lu, Joo Kooi Tan, Hyoungseop Kim, Takatoshi Aoki

The 23rd International Symposium on Artificial Life and Robotics 2018/The Third International Sysposium onBioComplexity(ISBC) 357 - 360 2018年01月

DCNNによる指骨CR画像からの骨粗しょう症の自動識別

畠野和裕, 村上誠一, 植村知規, 陸慧敏, タンジュークイ, 金亨燮, 青木隆敏

Ｍｅｄｉｃａｌ　Ｉｍａｇｉｎｇ　Ｔｅｃｈｎｏｌｏｇｙ 36 ( 2 ) 90 - 95 2018年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

骨のおもな疾患として，骨粗しょう症が挙げられる．骨粗しょう症に対する画像診断は有効であるが，類似した低骨量を呈する画像も多く，画像診断における客観性や再現性の問題がある．そこで本稿では，指骨computed radiography（CR）画像から骨粗しょう症の自動識別手法を提案する．提案手法では，深層畳み込みニューラルネットワーク（DCNN）を用いた識別器を構築し，骨粗しょう症有無の識別を行う．DCNNの学習および識別には，CR画像から3種類の画像を作成し，各指骨領域内部からROIを抽出後，この3種類のROIをR，G，Bチャンネルに割り当て生成した疑似カラー画像を用いる．実験では，101症例に対し提案手法を適用し，真陽性率（TPR）：75.5［％］，偽陽性率（FPR）：13.9［％］という結果を得た．

DOI： 10.11409/mit.36.90

CiNii Article

その他リンク： https://ci.nii.ac.jp/naid/130006588793

Describing a Self-occlusive Motion by a Reverse Descriptive Motion History Image

TAN Joo Kooi, OKAE Sayaka, YAMASHITA Youtaro, ONO Yuta, ISHIKAWA Seiji

バイオメディカル・ファジィ・システム学会大会講演論文集 ( バイオメディカル・ファジィ・システム学会 ) 31 ( 0 ) 25 - 26 2018年01月

担当区分：筆頭著者記述言語：日本語掲載種別：研究論文（研究会，シンポジウム資料等）

This paper proposes a new method of describing a human motion based on Motion History Image (MHI) representation. MHI representation is a well-known way of describing a motion by a single image, but it cannot deal with a self-occlusive motion in a proper way. This paper focuses on the motion in depth direction and proposes Reverse Descriptive MHI (RD-MHI). The RD-MHI and the original MHI are both employed for motion recognition, since the former well describes the motion toward an observing camera, whereas the latter away from the camera. Experimental results support the combination in human motion recognition.

DOI： 10.24466/pacbfsa.31.0_25

CiNii Article

その他リンク： https://ci.nii.ac.jp/naid/130007979978

A human care system by a mobile robot employing cooperative objects recognition 査読有り

Takaki S., Tana J.K., Ishikawa S.

IEEE Region 10 Annual International Conference, Proceedings/TENCON 2017-December 1148 - 1152 2017年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

This paper proposes a system for caring a physically impaired person by a mobile robot employing cooperative object recognition. It is a well-known fact that object recognition is not an easy task for a robot particularly in a real environment. It is, on the contrary, easy for a human. In the proposed system, a mobile robot goes to a spot where a user wishes to go in place of him/her and transfers images containing various objects in the spot. Having seen the images, the user chooses one of those objects and sends a request to the robot to get it. The robot performs the request by taking and bringing it back to the user. As an example, the robot is asked to go to a store, buy goods which a user wants and bring them back to the user. In this process, the goods in the captured images are segmented by the robot and they are recognized and one of them is chosen by the user. In this way, cooperative object recognition is realized to achieve a certain purpose. A laboratory experiment was done with promising results. The idea is different from remote control of a mobile robot, because the robot is autonomous and a user takes a role of only recognizing objects and sending his/her request to the robot.

DOI： 10.1109/TENCON.2017.8228030

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044202825&origin=inward

Automatic identification of circulating tumor cells in fluorescence microscopy images based on AdaBoost 査読有り

Tsuji K., Lu H., Tan J., Kim H., Yoneda K., Tanaka F.

International Conference on Control, Automation and Systems 2017-October 1449 - 1454 2017年12月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2017 Institute of Control, Robotics and Systems - ICROS. Circulating tumor cells (CTCs) is a useful biomarker for cancer metastasis. The blood from a cancer patient is analyzed by a fluorescence microscope. Each case takes a large number of images, which usually have a lot of cell regions. Thus, analyzing the images is hard work for pathologists, and misdiagnosis may happen. In this paper, we develop an automatic CTCs identification method for fluorescence microscopy images. The proposed method consists of three steps. First, we extract cell regions in images using filtering methods. Second, we compute features of each CTC candidate regions. Finally, we identify the CTCs using AdaBoost algorithm. And we analyze the features to know which ones are effective for characterizing CTCs and normal cells. We apply the proposed method to 5040 microscopy images, and evaluate the effectiveness of our method by using leave-one-out cross validation. We achieve a true positive rate of 97.30 [%] and a false positive rate of 12.82 [%].

DOI： 10.23919/ICCAS.2017.8204219

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044436784&origin=inward

　前のページ - 次のページ　

<丹上 結乃純>

論文 - 丹上 結乃純

<丹上　結乃純>

論文 - 丹上　結乃純