• Hidenori Ide, Takumi Kobayashi, Kenji Watanabe, Takio Kurita, “ Robust Pruning For Efficient CNNs,’’ Pattern Recognition Letters, 2020 (accepted).
  • Muthu Subash Kavitha, Chang-Hee Lee, Kattakkali Subhashdas Shibudas, Takio Kurita, Byeong-Cheol Ahn, “ Deep learning enables automated localization of the metastatic lymph node from the thyroid remnant tissue on 131I post-ablation whole-body planar scans,” Scientific Reports, 2020 (accepted).
  • Yudistira, Novanto, and Takio Kurita. “Correlation Net: Spatiotemporal multimodal deep learning for action recognition.” Signal Processing: Image Communication 82 (2020): 115731.
  • Novanto Yudistia, Muthu Subash Kavitha, Takeshi Itabashi, Atsuko H. Iwane, and Takio Kurita, “Prediction of Sequential Organelles localization under Imbalance using A Balanced Deep U-Net,” Scientific Reports, (2020) 10:2626, 2020.01 | https://doi.org/10.1038/s41598-020-59285-9.  


  • Indraswari, R., Kurita, T., Arifin, A. Z., Suciati, N., & Astuti, E. R. (2019). Multi-projection deep learning network for segmentation of 3D medical images. Pattern Recognition Letters125, 791-797.
  • Rarasmaya Indraswari, Agus Zainal Arifin, Nanik Suciati, Eha Renwi Astuti, Takio Kurita, “ Automatic Segmentation of Mandibular Inferior Cortical Bone on Cone-Beam CT Images Based on Histogram Thresholding and Polynomial Fitting,’’  International Journal of Intelligent Engineering and Systems, Vol.12, No.4, pp.130–141, 2019.08. DOI: 10.22266/ijies2019.0831.13 
  • Arna Faruza, Agus Zainal Arifin, Eha Renwi Astuti, and Takio Kurita, “ Segmenting Tooth Components in Dental X-Ray Images Using Gaussian Kernel-Based Conditional Spatial Fuzzy C-Means Clustering Algorithm,’’ International Journal of Intelligent Engineering and Systems, Vol.12, No.3, pp.108–117, 2019.06.  DOI: 10.22266/ijies2019.0630.12 
  • Yudistira, Novanto, and Takio Kurita. “Deep Packet Flow: Action Recognition via Multiresolution Deep Wavelet Packet of Local Dense Optical Flows.” Journal of Signal Processing Systems91.6 (2019): 609-625.
  • Hidaka, Akinori, Kenji Watanabe, and Takio Kurita. “Sparse discriminant analysis based on estimation of posterior probabilities.” Journal of Applied Statistics (2019): 1-25.


  • Sabri, Motaz, and Takio Kurita. “Facial expression intensity estimation using Siamese and triplet networks.” Neurocomputing313 (2018): 143-154.
  • 井手秀徳, & 栗田多喜夫. (2018). CNN における ReLU 活性化関数に対するスパース正則化の適用と分析. 電子情報通信学会論文誌 D101(8), 1110-1119.
  • Kumagai, Shohei, Kazuhiro Hotta, and Takio Kurita. “Mixture of counting CNNs.” Machine Vision and Applications 29.7 (2018): 1119-1126.
  • Shimono, Eri, et al. “Logistic Regression Analysis for the Material Design of Chiral Crystals.” Chemistry Letters 47.5 (2018): 611-612.
  • Muthu Subash Kavitha, Takio Kuirta, and Byeong-Cheol Ahn, “Critical texture pattern feature assessment for characterizing colonies of induced pluripotent stem cells through machine learning techniques,” Computers in Biology and Medicine, Volume 94, 1 March 2018, Pages 55–64. 
  • Motaz Sabri, and Takio Kurita, “Improvement of feature localization for facial expressions by adding noise,” International Journal of Affective Engineering, Vol.17, No.1, pp.27-37, 2018. https://doi.org/10.1016/j.compbiomed.2018.01.005 
  • Akinori Hidaka and Takio Kurita, “Data Visualization for Deep Neural Networks Based on Interlayer Canonical Correlation Analysis,” Trans. of the Institute of Systems, Control and Information Engineers (ISCIE), Vol.31, No.1, pp.10-20, Jan. 2018. 


  • Fangda Zhao, Toru Tamaki, Takio Kurita, Bisser Raytchev, Kazufumi Kaneda, “Marker-based non-overlapping camera calibration methods with additional support camera views,” Image and Vision Computing, Volume 70, February 2018, Pages 46-54, ISSN 0262-8856, https://doi.org/10.1016/j.imavis.2017.12.006. 
  • Muthu Subash Kavitha, Takio Kurita, Soon-Yong Park, Beong-Cheol Ahn, Sun-Il Chien, “Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells,” PLOS ONE 12(12), 2017.12. e0189974. https://doi.org/10.1371/journal.pone.0189974. 
  • Yudistira, Novanto, and Takio Kurita. “Gated spatio and temporal convolutional neural network for activity recognition: towards gated multimodal deep learning.” EURASIP Journal on Image and Video Processing 2017.1 (2017): 85.
  • Tsubasa Hirakawa, Toru Tamaki, Takio Kurita, Bisser Raytchev, Kazufumi Kaneda, Chaohui Wang, Laurent Najman, “Tree-wise Discriminative Subtree Selection for Texture Image Labeling,” IEEE Access, Vol.5, pp.13617-13634, 2017.07. D OI: 10.1109/ACCESS.2017.2725319 
  • Sabri, Motaz, and Takio Kurita. “Effect of additive noise for multi-layered perceptron with autoencoders.” IEICE TRANSACTIONS on Information and Systems 100.7 (2017): 1494-1504.

~ 2016

  • Akinori Hidaka and Takio Kurita, “Optimum Nonlinear Discriminant Analysis and Discriminant Kernel Support Vector Machine,’’ IEICE Trans. on Information and Systems, Vol.E99-D, No.11, pp.2734-2744, Nov. 2016.
  • Muthu Subash Kavitha , Pugalendhi Ganesh Kumar, Soon-Yong Park, Kyung-Hoe Huh, Min-Suk Heo, Takio Kurita, Akira Asano, Seo-Yong An, Sung-Il Chien, “Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches,” Dentomaxillofacial Radiology, June 08, 2016. DOI: http://dx.doi.org/10.1259/dmfr.20160076
  • Akinori Hidaka and Takio Kurita, “ Randomized and Dimension Reduced Radial Basis Features for Support Vector Machine,’’ Trans. of the Institute of Systems, Control and Information Engineers (ISCIE), Vol. 29, No. 1, pp. 1-8, Jan. 15, 2016.
  • Takashi Takahashi and Takio Kurita, “ Mixture of Subspaces Image Representation and Compact Coding for Large-Scale Image Retrieval,’’ IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol.37, No.7, pp.1469-1479, July 2015 (DOI:10.1109/TPAMI.2014.2382092).[高IF論文誌(IF=5.781)]
  • Takahashi Takashi and Takio Kurita, “Image Classification using a Mixture of Subspace Models,’’ IPSJ Transactions on Computer Vision and Application, Vol.6, pp.93-97, 2014.07. [DOI: 10.2197/ipsjtcva.6.93]
  • Zhouxin YANG, Takio KURITA, “ Improvements of local descriptor in HOG/SIFT by BOF approach,’’ IEICE Trans. on Information and Systems, Vol.E97-D, No.5, pp.1293-1303, May. 2014.(DOI:10.1587/transinf.E97.D.1293) 
  • 野口 祥宏, 嶋田 敬士, マノジ ペレラ, 栗田 多喜夫, “人物画像認識による来場者モニタリング,’’ 精密工学会誌, Vol.80, No.1, pp.89-93, 2014.
  • 田辺 和俊, 栗田 多喜夫, 西田 健次, 鈴木 孝弘、“サポートベクター回帰を用いた158カ国の国債格付けの再現,’’ 情報知識学会誌, Vol. 23 (2013) No. 1, pp. 70-91. 
  • X. Y. Guo, C. Muraki Asano, A. Asano, and T. Kurita, “Modeling the Perception of Visual Complexity in Texture Images,” International Journal of Affective Engineering, vol. 12, no. 2, pp.223-231, 2013. 
  • Tanabe, Kazutoshi, Suzuki, Takahiro, Kurita, Takio, Nishida, Kenji, Lucic, Bono, Amic, Dragan, “Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of SVM models,’’ the Journal of Chemical Information and Modeling (SAR QSAR Environ Res. 2013 Jan 25. DOI:10.1080/1062936X.2012.762425 [Epub ahead of print]) 
  • Keiji Shimada, Yoshihiro Noguchi, and Takio Kuria, “Fast and Robust Smile Intensity Estimation by Cascaded Support Vector Machines,”International Journal of Computer Theory and Engineering, vol.5, no.1, pp.24-30, 2013. 
  • Lei Yang, Akira Asano, Liang Li, Chie Muraki Asano, Takio Kurita, “Multi-structural texture analysis using mathematical morphology, ” IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E95-A, No.10, pp.1759-1767, 2012.10. 
  • Xinoying Guo, Chie Muraki Asano, Akira Asano, Takio Kurita, and Liang Li, “ Analysis of the texture characteristics associated with visual complexity perception, Optical Review, Vol.19, no.5, pp.306-314, 2012.10. 
  • Tsuchiya, C., Tanaka, S., Furusho, H., Nishida, K., and Kurita, T.: Real-Time Vehicle Detection using a Single Rear Camera for a Blind Spot Warning System, SAE International Journal of Passenger Cars – Electron. Electr. Syst. 5(1), pp.146-153, 2012. doi:10.4271/2012-01-0293 
  • Kenji Watanabe, Akinori Hidaka, Nobuyuki Otsu, and Takio Kurita, “Automatic Analysis of Composite Physical Signals using Non-negative Factorization and Information Criterion,” PLoS ONE,Vol.7, No.3, e32352, March 2012. 
  • M. S. Kavitha, A. Asano, A. Taguchi, T. Kurita, and M. Sanada, Diagnosis of Osteoporosis on Dental Panoramic Radiographs using Support Vector Machine in Computer-Aided system, BMC Medical Imaging 2012, 12:1 
  • ・Tetsu Matsukawa, and Takio Kurita, “Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images,” Pattern Recognition (February 2012), 45 (2), pg. 707-719. 
  • Rameswar Debnath, Haruhisa Takahashi and Takio Kurita, “A comparison of SVM-based evolutionary methods for multicategory cancer diagnosis using microarray gene expression data,” Journal of Systemics, Cybernatics and Informatics, vol. 9, no. 6, pp. 63-68, 2011. 
  • 田辺和俊、栗田多喜夫、西田健次、鈴木孝弘、“サポートベクターマシンを用いた企業の信用格付けの予測,” Journal of the Japan Society for Management Information, Vol.20, No.1, pp.23-38, 2011.06. 
  • K.Tanabe, B.Lucic, D.Amic, T.Kurita, M.Kaihara, N.Onodera, T.Suzuki “Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling,” Molecular Diversity, Vol.14, No.4, pp.789-802, 2010.11. 
  • T.Matsukawa and T.Kurita, “Extraction of combined features from global/local statistics of visual words using relevant operations,” IEICE Trans. On Information and Systems, Vol.E93-D, No.10, pp.2870-2871, 2010.10.