Sudo, K.[Kyoko]
* 2007: MCMC based multi-body tracking using full 3D model of both target and environment
* 2008: Estimating Anomality of the Video Sequences for Surveillance Using 1-Class SVM
* 2008: Monocular 3D tracking of multiple interacting targets
* 2008: Online anomal movement detection based on unsupervised incremental learning
* 2012: Region of Interest detection using indoor structure and saliency map
* 2014: Occlusion boundary detection based on mid-level figure/ground assignment features
* 2015: Cross-Domain Concept Detection with Dictionary Coherence by Leveraging Web Images
* 2015: Egocentric articulated pose tracking for action recognition
* 2015: method based on view-directional consistency constraints for robust 3D object recognition, The
* 2015: Mix and Match: Joint Model for Clothing and Attribute Recognition
* 2016: Attribute Discovery for Person Re-Identification
* 2016: Automatic Attribute Discovery with Neural Activations
* 2017: Domain adaptation of articulated pose estimation via synthetic pose prior
13 for Sudo, K.
Sudo, N.
* 2006: Experiment on parameter selection of image distortion model
* 2006: Experiment on precision of camera calibration of non-metric digital cameras
* 2010: Effect of Sampling in Creating a Digital Image on Measurement Accuracy of Center Location of a Circle
* 2011: Measurement Accuracy of Center Location of a Circle by Centroid Method
Includes: Sudo, N. Sudo, N.[Noboru]
Sudo, T.
* 2002: Pen pressure features for writer-independent on-line handwriting recognition based on substroke HMM
* 2003: On-line overlaid-handwriting recognition based on substroke HMMs
Sudol, J.[Jeremi]
* 2010: Looktel: A comprehensive platform for computer-aided visual assistance
* 2013: Low-bitrate benefits of JPEG compression on sift recognition
Sudowe, P.[Patrick]
* 2011: Efficient Use of Geometric Constraints for Sliding-Window Object Detection in Video
* 2011: Real-Time Multi-Person Tracking with Time-Constrained Detection
* 2015: Person Attribute Recognition with a Jointly-Trained Holistic CNN Model
* 2016: PatchIt: Self-Supervised Network Weight Initialization for Fine-grained Recognition
Includes: Sudowe, P.[Patrick] Sudowe, P.