Publications by Atsuto Maki
Refereegranskade
Artiklar
[1]
K. Fukui et al., "Discriminant feature extraction by generalized difference subspace," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 2, pp. 1618-1635, 2023.
[2]
A. Maki et al., "In Memoriam : Jan-Olof Eklundh," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 4488-4489, 2022.
[3]
L. Rixon Fuchs, A. Maki and A. Gällström, "Optimization Method for Wide Beam Sonar Transmit Beamforming," Sensors, vol. 22, no. 19, pp. 7526, 2022.
[4]
M. Valan et al., "Automated Taxonomic Identification of Insects with Expert Level Accuracy Using Effective Feature Transfer from Convolutional Networks," Systematic Biology, 2019.
[5]
A. Maki, "Toward principled regularization of deep networks : From weight decay to feature contraction," Science Robotics, vol. 4, no. 30, 2019.
[6]
A. S. Razavian et al., "Visual Instance Retrieval with Deep Convolutional Networks," Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers, vol. 73, no. 5, pp. 956-964, 2019.
[7]
M. Buda, A. Maki and M. A. Mazurowski, "A systematic study of the class imbalance problem in convolutional neural networks," Neural Networks, vol. 106, pp. 249-259, 2018.
[8]
S. Nawata, A. Maki and H. Takashi, "Power packet transferability via symbol propagation matrix," Proceedings of the Royal Society. Mathematical, Physical and Engineering Sciences, vol. 474, no. 2213, 2018.
[9]
J. Olczak et al., "Artificial intelligence for analyzing orthopedic trauma radiographs Deep learning algorithms-are they on par with humans for diagnosing fractures?," Acta Orthopaedica, vol. 88, no. 6, pp. 581-586, 2017.
[10]
V. Högman et al., "A sensorimotor learning framework for object categorization," IEEE Transactions on Cognitive and Developmental Systems, vol. 8, no. 1, pp. 15-25, 2016.
[11]
H. Azizpour et al., "Factors of Transferability for a Generic ConvNet Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 9, pp. 1790-1802, 2016.
[12]
A. S. Razavian et al., "Visual instance retrieval with deep convolutional networks," ITE Transactions on Media Technology and Applications, vol. 4, no. 3, pp. 251-258, 2016.
[13]
K. Fukui and A. Maki, "Difference subspace and its generalization for subspace-based methods," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 11, pp. 2164-2177, 2015.
[14]
M. Brudfors et al., "Towards real-time, tracker-less 3D ultrasound guidance for spine anaesthesia," International Journal of Computer Assisted Radiology and Surgery, vol. 10, no. 6, pp. 855-865, 2015.
[15]
O. Woodford et al., "Demisting the Hough Transform for 3D Shape Recognition and Registration," International Journal of Computer Vision, no. 332, 2014.
[16]
M.-T. Pham et al., "Distances and Means of Direct Similarities," International Journal of Computer Vision, vol. 112, no. 3, pp. 285-306, 2014.
[17]
A. Maki et al., "Detecting Bipedal Motion from Correlated Probabilistic Trajectories," Pattern Recognition Letters, vol. 34, no. 15, pp. 1808-1818, 2013.
[18]
H. Mizuyama et al., "Explanatory analysis of the manner in which an instructor adaptively organizes skilled motion teaching process," International Journal of Industrial Ergonomics, vol. 43, no. 5, pp. 430-438, 2013.
[19]
F. Perbet, B. Stenger and A. Maki, "Homogeneous Superpixels from Markov Random Walks," IEICE transactions on information and systems, vol. E95-D, no. 7, pp. 1740-1748, 2012.
[20]
B. Sajadi et al., "Switchable Primaries Using Shiftable Layers of Colof Filter Arrays," ACM Transactions on Graphics, vol. 30, no. 4, 2011.
[21]
A. Maki, "Data Clustering Based on Random Space Partition," Systems, Control and Information, vol. 54, no. 2, pp. 66-71, 2010.
[22]
H. Yoshimoto, "A Cell-Based 3D Video Capturing method with Active Cameras," IEICE transactions on information and systems, vol. J92-D, no. 9, pp. 1579-1590, 2009.
[23]
T. Takai et al., "Difference Sphere : An Approach to Near Light Source Estimation," Computer Vision and Image Understanding, vol. 113, no. 9, pp. 966-978, 2009.
[24]
T. Mukasa et al., "Finding Articulated Body in Time-Series 3D Volume Data," ELCVIA Electronic Letters on Computer Vision and Image Analysis, vol. 7, no. 4, pp. 62-72, 2009.
[25]
J. Starck et al., "The Multiple-Camera 3-D Production Studio," IEEE transactions on circuits and systems for video technology (Print), vol. 19, no. 6, pp. 856-869, 2009.
[26]
N. Masaaki, A. Maki and T. Matsuyama, "Phase-Based Feature Matching Under Illumination Variances," IPSJ Transactions on Computer Vision and Image Media, vol. 48, no. 9, pp. 94-104, 2007.
[27]
A. Maki, M. Watanabe and C. Wiles, "Geotensity constraint for 3D surface reconstruction," Systems and Computers in Japan, vol. 35, no. 4, pp. 72-83, 2004.
[28]
A. Maki, "Photometric Subspace for Multibody Motion Segmentation," Image and Vision Computing, vol. 22, no. 8, pp. 655-662, 2004.
[29]
A. Maki and K. Fukui, "Ship Identification in Sequential ISAR Imagery," Machine Vision and Applications, vol. 15, no. 3, pp. 149-155, 2004.
[30]
A. Maki, M. Watanabe and C. Wiles, "Geotensity: Combining Motion and Lighting for 3D Surface Reconstruction," International Journal of Computer Vision, vol. 48, no. 2, pp. 75-90, 2002.
[31]
N. Kishikawa et al., "3-D Shape Modeling System Using PC and Digital Camera," Transaction of Institute of Systems, Control, Information Engineers, vol. 14, no. 4, pp. 200-208, 2001.
[32]
C. Wiles, A. Maki and N. Matsuda, "Hyper-Patches for 3D Model Acquisition and Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 12, pp. 1391-1403, 2001.
[33]
A. Maki, P. Nordlund and J.-O. Eklundh, "Attentional Scene Segmentation : Integrating Depth and Motion," Computer Vision and Image Understanding, vol. 78, no. 3, pp. 351-373, 2000.
[34]
A. Maki, "Estimation of Illuminant Direction and Surface Reconstruction by Geotensity Constraint," Pattern Recognition Letters, vol. 21, no. 13-14, pp. 1115-1123, 2000.
[35]
A. Maki, "Geotensity Constraint for 3D Surface Reconstruction," IEICE transactions on information and systems, vol. 83, no. 8, pp. 1741-1752, 2000.
[36]
A. Maki and T. Uhlin, "Disparity Selection in Binocular Pursuit," IEICE transactions on information and systems, vol. E78-D, no. 12, pp. 1591-1597, 1995.
[37]
Y. Nishikawa, H. Tamaki and A. Maki, "A Decomposition Method for Jobshop Scheduling," Transaction of Society of Instrument and Control Engineers, vol. 27, no. 5, pp. 607-613, 1991.
Konferensbidrag
[38]
D. Sabel, T. Westin and A. Maki, "3D Point Cloud Registration for GNSS-denied Aerial Localization over Forests," in Image Analysis - 23rd Scandinavian Conference, SCIA 2023, Proceedings, 2023, pp. 396-411.
[39]
X. Zhu et al., "Surface Defect Detection with Limited Training Data : A Case Study on Crown Wheel Surface Inspection," in 56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023, 2023, pp. 1333-1338.
[40]
X. Zhu et al., "Towards sim-to-real industrial parts classification with synthetic dataset," in Proceedings : 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023, 2023, pp. 4454-4463.
[41]
L. Rixon Fuchs, A. Gallstrom and A. Maki, "Towards Dense Point Correspondence with PatchMatch in Low-Resolution Sonar Images," in 2022 IEEE/OES AUTONOMOUS UNDERWATER VEHICLES SYMPOSIUM (AUV), 2022.
[42]
X. Zhu, A. Maki and L. Hanson, "Unsupervised domain adaptive object detection for assembly quality inspection," in Proceedings 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering, ICME 2021, 2022, pp. 477-482.
[43]
D. Sabel et al., "VISION-BASED LOCALISATION FOR AUTONOMOUS AERIAL NAVIGATION IN GNSS-DENIED SITUATIONS," in 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022, 2022, pp. 5973-5987.
[44]
X. Zhu et al., "Automatic assembly quality inspection based on an unsupervised point cloud domain adaptation model," in Procedia CIRP, 2021, pp. 1801-1806.
[45]
L. Marson, V. Li and A. Maki, "Boundary optimised samples training for detecting out-of-distribution images," in 2020 25th International Conference on Pattern Recognition (ICPR), 2020, pp. 10486-10492.
[46]
M. Nordström et al., "Calibrated Surrogate Maximization of Dice," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 : 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV, 2020, pp. 269-278.
[47]
Y. Zhong and A. Maki, "Regularizing CNN transfer learning with randomised regression," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020, pp. 13634-13643.
[48]
D. Feng et al., "Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector," in 2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19), 2019, pp. 667-674.
[49]
V. Li and A. Maki, "Feature contraction : New convnet regularization in image classification," in British Machine Vision Conference 2018, BMVC 2018, 2019.
[50]
O. Holesovsky and A. Maki, "Compact ConvNets with Ternary Weights and Binary Activations," in The 23rd Computer Vision Winter Workshop, 2018.
[51]
V. Li and A. Maki, "Feature Contraction: New ConvNet Regularization in Image Classification," in British Machine Vision Conference, 2018.
[52]
Z. Yang et al., "Target aware network adaptation for efficient representation learning," in ECCV 2018: Computer Vision – ECCV 2018 Workshops, 2018, pp. 450-467.
[53]
A. Ghadirzadeh et al., "Deep predictive policy training using reinforcement learning," in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, 2017, pp. 2351-2358.
[54]
A. Ghadirzadeh et al., "A sensorimotor reinforcement learning framework for physical human-robot interaction," in IEEE International Conference on Intelligent Robots and Systems, 2016, pp. 2682-2688.
[55]
J. Johansson, M. Solli and A. Maki, "An Evaluation of Local Feature Detectors and Descriptors for Infrared Images," in Lecture Notes in Computer Science, Volume 9915, 2016, pp. 711-723.
[56]
A. Sharif Razavian et al., "A Baseline for Visual Instance Retrieval with Deep Convolutional Networks," in International Conference on Learning Representations,May 7 - 9, 2015, San Diego, CA, 2015.
[57]
A. Ghadirzadeh, A. Maki and M. Björkman, "A Sensorimotor Approach for Self-Learning of Hand-Eye Coordination," in IEEE/RSJ International Conference onIntelligent Robots and Systems, Hamburg, September 28 - October 02, 2015, 2015, pp. 4969-4975.
[58]
I. Papakostas et al., "Efficient Motion Capturing and Idling Stop Display system utilizing CAAC : IGZO semiconductor FETs," in ESS2015: Embedded System Symposium 2015, 2015.
[59]
H. Azizpour et al., "From Generic to Specific Deep Representations for Visual Recognition," in Proceedings of CVPR 2015, 2015.
[60]
A. Sharif Razavian et al., "Persistent Evidence of Local Image Properties in Generic ConvNets," in Image Analysis : 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings, 2015, pp. 249-262.
[61]
M. Brudfors et al., "Towards real-time, tracker-less 3D ultrasound guidance for spine anaesthesia," in International Conference on Information Processing in Computer-Assisted Interventions, 2015.
[62]
A. S. Razavian et al., "Visual instance retrieval with deep convolutional networks," in 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings, 2015.
[63]
R. Norlander, J. Grahn and A. Maki, "Wooden Knot Detection Using ConvNet Transfer Learning," in Scandinavian Conference on Image Analysis, 2015.
[64]
A. Ghadirzadeh et al., "Learning visual forward models to compensate for self-induced image motion," in 23rd IEEE International Conference on Robot and Human Interactive Communication : IEEE RO-MAN, 2014, pp. 1110-1115.
[65]
A. Maki and R. Gherardi, "Conditional Variance of Differences: A Robust Similarity Measure for Matching and Registration," in IAPR International Workshops on Statistical Techniques in Pattern Recognition, 2012.
[66]
O. Woodford et al., "Contraction Moves for Geometric Model Fitting," in 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VII, 2012.
[67]
M. Peris et al., "Towards a simulation driven stereo vision system," in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 2012, pp. 1038-1042.
[68]
M.-T. Pham et al., "A New Distance for Scale-Invariant 3D Shape Recognition and Registration," in Proceedings of the IEEE International Conference on Computer Vision, 2011, pp. 145-152.
[69]
A. Maki et al., "Co-occurrence Flow for Pedestrian Detection," in IEEE International Conference on Image Processing, 2011.
[70]
F. Perbet and A. Maki, "Homogeneous Superpixels from Random Walks," in IAPR International Conference on Machine Vision Applications, 2011.
[71]
C. Hernandez et al., "Live 3D Shape Reconstruction, Recognition and Registration," in Proceedings of the International Conference on Computer Vision Workshops (ICCV Workshops), 2011.
[72]
B. Sajadi et al., "Switchable Primaries Using Shiftable Layers of Color Filter Arrays," in SIGGRAPH 2011, SIGGRAPH 2011; Vancouver, BC; Canada; 7 August 2011 through 11 August 2011, 2011.
[73]
D. Comanducci et al., "2D-3D Photo Rendering for 3D Displays," in 3D Data processing, Visualization and Transmission, 2010.
[74]
H. Mizuyama et al., "A Semiotic Characterization of the Process of Teaching and Learning a Skilled Motion Taking Wok Handling as an Example," in IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, 2010.
[75]
F. Perbet, A. Maki and B. Stenger, "Correlated Probabilistic Trajectories for Pedestrian Motion Detection," in 2009 IEEE 12th International Conference on Computer Vision, Kyoto, 2009, 2009, pp. 647-1654.
[76]
A. Maki and R. Cipolla, "Obtaining the Shape of a Moving Object with a Specular Surface," in British Machine Vision Conference, 2009.
[77]
F. Perbet, B. Stenger and A. Maki, "Random Forest Clustering and Application to Video Segmentation," in British Machine Vision Conference, BMVC 2009 - Proceedings 2009, 2009.
[78]
T. Mukasa et al., "Complex human motion estimation using visibility," in IEEE International Conference on Automatic Face and Gesture Recognition, 2008.
[79]
A. Maki, Y. Hatanaka and T. Matsuyama, "Tracking features on a moving object using local image bases," in IAPR International Conference on Pattern Recognition, 2008.
[80]
M. Nishino, A. Maki and T. Matsuyama, "Phase-Based Feature Matching Under Illumination Variances," in International conference on Industrial, engineering, and other applications of applied intelligent systems, 2007.
[81]
T. Takai, A. Maki and T. Matsuyama, "Self Shadows and Cast Shadows in Estimating illumination distribution," in 4th European Conference on Visual Media Production, 27-28 Nov. 2007, 2007.
[82]
T. Mukasa et al., "Finding Articulated Body in Time-Series 3D Volume Data," in International Conference on Articulated Motion and Deformable Objects, 2006.
[83]
A. Maki, "3D Surface Reconstruction of a Moving Object in the Presence of Specular Reflection," in International Conference on Image Analysis and Processing, 2005.
[84]
M. Nishiyama, A. Maki and T. Matsuyama, "Optimizing Camera Control for High-Resolution Volume Intersection," in Annual Conference of the Institute of Image Electronics Engineers of Japan Proceedings of Visual Computing/Graphics and CAD Joint Symposium, 2004.
[85]
K. Hiwada, A. Maki and A. Nakashima, "A Real-Time Face Tracking System Based on Morphable 3D Model Fitting," in International Conference on Computer Vision (ICCV) Demonstration, 2003.
[86]
K. Hiwada, A. Maki and A. Nakashima, "Mimicking Video: Real-Time Morphable 3D Model Fitting," in ACM Symposium on Virtual Reality Software and Technology, 2003.
[87]
A. Nakashima and A. Maki, "Synthesizing Pose and Lighting Variation from Object Motion," in IEEE International Conference on Image Processing, 2003.
[88]
A. Maki et al., "Automatic Ship Identification in ISAR Imagery: An On-line System using CMSM," in IEEE Radar Conference, 2002.
[89]
A. Nakashima, A. Maki and K. Fukui, "Constructing Illumination Image Basis from Object Motion," in European Conference on Computer Vision, 2002.
[90]
R. Okada et al., "Temporally Evaluated Optical Flow: Study on Accuracy," in IAPR International Conference on Pattern Recognition, 2002.
[91]
A. Maki et al., "ISAR Image Analysis by Subspace Method: Automatic Extraction and Identification of Ship Profile," in IAPR International Conference on Image Analysis and Processing, 2001.
[92]
A. Maki and H. Hattori, "Illumination Subspace for Multibody Motion Segmentation," in IEEE Conference Computer Vision and Pattern Recognition, 2001.
[93]
A. Maki and C. Wiles, "Geotensity constraint for 3D surface reconstruction under multiple light sources," in European Conference on Computer Vision, 2000.
[94]
H. Hattori and A. Maki, "Stereo without Depth Search and Metric Calibration," in IEEE Conference Computer Vision and Pattern Recognition, 2000.
[95]
A. Maki, "Estimation of Illuminant Direction and Surface Reconstruction by Geotensity Constraint," in Scandinavian Conference on Image Analysis, 1999.
[96]
A. Maki, M. Watanabe and C. Wiles, "Geotensity: Combining Motion and Lighting for 3D Surface Reconstruction," in IEEE International Conference on Computer Vision, 1998.
[97]
H. Hattori and A. Maki, "Stereo Matching with Direct Surface Orientation Recovery," in British Machine Vision Conference, 1998.
[98]
C. Wiles et al., "Hyper-Patches for 3D Model Acquisition and Tracking," in IEEE Conference Computer Vision and Pattern Recognition, 1997.
[99]
A. Maki, T. Uhlin and J.-O. Eklundh, "A Direct Disparity Estimation Technique for Depth Segmentation," in IAPR Workshop on Machine Vision Applications, 1996, pp. 530-533.
[100]
A. Maki, P. Nordlund and J.-O. Eklundh, "A computational model of depth-based attention," in International Conference on Pattern Recognition, 1996, pp. 734-739.
[101]
J.-O. Eklundh et al., "Active Vision and Seeing Robots," in International Symposium on Robotics Research, 1996.
[102]
J.-O. Eklundh et al., "Developing an Active Observer," in Asian Conference on Computer Vision, 1995, pp. 181-190.
[103]
A. Maki, L. Bretzner and J.-O. Eklundh, "Local Fourir Phase and Disparity Estimates : An Analytical Study," in International Conference on Computer Analysis of Images and Patterns, 1995, pp. 868-873.
[104]
T. Uhlin et al., "Towards an Active Visual Observer," in Computer Vision, 1995. Proceedings., Fifth International Conference on, 1995, pp. 679-686.
[105]
A. Maki, T. Uhlin and J.-O. Eklundh, "Disparity Selection in Binocular Pursuit," in IAPR Workshop on Machine Vision Applications, 1994, pp. 182-185.
[106]
A. Maki, T. Uhlin and J.-O. Eklundh, "Phase-Based Disparity Estimation in Binocular Tracking," in Scandinavian Conference on Image Analysis, 1993.
Icke refereegranskade
Artiklar
[107]
A. Maki, "Special Section on Machine Vision and its Applications FOREWORD," IEICE transactions on information and systems, vol. E103D, no. 6, pp. 1208-1208, 2020.
[108]
M. Nordström et al., "Interactive Deep Learning-Based Delineation of Gross Tumor Volume for Postoperative Glioma Patients," Medical physics (Lancaster), vol. 46, no. 6, pp. E426-E427, 2019.
[109]
A. Maki, "Epilogue to the 6th Sweden-Japan Academic Network : Towards Replicating Our Visual Function: Approaches with Machine Learning," JSPS Stockholm Newsletter (English Edition), vol. Spring 2018, no. 32, pp. 13-13, 2018.
[110]
M. Nordström et al., "Pareto Dose Prediction Using Fully Convolutional Networks Operating in 3D," Medical physics (Lancaster), vol. 45, no. 6, pp. E176-E176, 2018.
Konferensbidrag
[111]
R. Okada et al., "Chairs message," in Proceedings of the 16th International Conference on Machine Vision Applications, MVA 2019, 2019.
Böcker
[112]
A. Maki, T. Asaka and S. Mizuta, Basic Information Processing Lab. Kyoto University, student union printing publisher, 2008.
Kapitel i böcker
[113]
M.-T. Pham et al., "Scale-Invariant Vote-based 3D Recognition and Registration from Point Clouds," in Machine Learning in Computer Vision, Studies in Computational Intelligence, Roberto Cipolla et al. Ed., : Springer, 2013, pp. 137-162.
[114]
T. Takai et al., "3D Lighting Environment Estimation with Shading and Shadows," in Image and Geometry Processing for 3D Cinematography, Geometry and Computing, R. Ronfard and G. Taubin Ed., 5th ed. : Springer Berlin/Heidelberg, 2010, pp. 239-257.
[115]
H. Kawashima and A. Maki, "Fundamental Techniques for Biometric : Feature Extraction," in Biometric Security Handbook, : Ohmsha, 2008.
[116]
A. Maki, "Perception," in Artificial Intelligence: A modern Approach, S. Russel and P. Norvig (Translation in Japanese, K. Furukawa) Ed., : Kyoritsu Publisher, 2008.
Övriga
[117]
M. Nordström, H. Hult and A. Maki, "Marginal Thresholding in Noisy Image Segmentation," (Manuscript).
[118]
Senaste synkning med DiVA:
2024-04-28 02:00:02