79 research outputs found
Hand Posture Recognition Using Convolutional Neural Networks
International audienceIn this work we present a convolutional neural network-based algorithm for recognition of hand postures on images acquired by a single color camera. The hand is extracted in advance on the basis of skin color distribution. A neural network-based regressor is applied to locate the wrist. Finally, a convolutional neural network trained on 6000 manually labeled images representing ten classes is executed to recognize the hand posture in a sub-window determined on the basis of the wrist. We show that our model achieves high classification accuracy, including scenarios with different camera used in testing. We show that the convolutional network achieves better results on images pre-filtered by a Gabor filter
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
This paper proposes a new hybrid architecture that consists of a deep
Convolutional Network and a Markov Random Field. We show how this architecture
is successfully applied to the challenging problem of articulated human pose
estimation in monocular images. The architecture can exploit structural domain
constraints such as geometric relationships between body joint locations. We
show that joint training of these two model paradigms improves performance and
allows us to significantly outperform existing state-of-the-art techniques
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