畳み込みニューラルネットワークと時空間サポートベクター回帰に基づくギターの運指の自動評価システム

早大学位記番号:新7965早稲田大

Higher level techniques for the artistic rendering of images and video

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Action recognition from RGB-D data

In recent years, action recognition based on RGB-D data has attracted increasing attention. Different from traditional 2D action recognition, RGB-D data contains extra depth and skeleton modalities. Different modalities have their own characteristics. This thesis presents seven novel methods to take advantages of the three modalities for action recognition. First, effective handcrafted features are designed and frequent pattern mining method is employed to mine the most discriminative, representative and nonredundant features for skeleton-based action recognition. Second, to take advantages of powerful Convolutional Neural Networks (ConvNets), it is proposed to represent spatio-temporal information carried in 3D skeleton sequences in three 2D images by encoding the joint trajectories and their dynamics into color distribution in the images, and ConvNets are adopted to learn the discriminative features for human action recognition. Third, for depth-based action recognition, three strategies of data augmentation are proposed to apply ConvNets to small training datasets. Forth, to take full advantage of the 3D structural information offered in the depth modality and its being insensitive to illumination variations, three simple, compact yet effective images-based representations are proposed and ConvNets are adopted for feature extraction and classification. However, both of previous two methods are sensitive to noise and could not differentiate well fine-grained actions. Fifth, it is proposed to represent a depth map sequence into three pairs of structured dynamic images at body, part and joint levels respectively through bidirectional rank pooling to deal with the issue. The structured dynamic image preserves the spatial-temporal information, enhances the structure information across both body parts/joints and different temporal scales, and takes advantages of ConvNets for action recognition. Sixth, it is proposed to extract and use scene flow for action recognition from RGB and depth data. Last, to exploit the joint information in multi-modal features arising from heterogeneous sources (RGB, depth), it is proposed to cooperatively train a single ConvNet (referred to as c-ConvNet) on both RGB features and depth features, and deeply aggregate the two modalities to achieve robust action recognition

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions