By Francisco José Perales, Josef Kittler
This publication constitutes the refereed complaints of the ninth overseas convention on Articulated movement and Deformable gadgets, AMDO 2016, held in Palma de Mallorca, Spain, in July 2016.The 20 papers offered have been conscientiously reviewed and chosen from 34 submissions. The convention handled the next themes: complicated special effects and immersive videogames; human modeling and animation; human movement research and monitoring; 3D human reconstruction and popularity; multimodal person interplay and functions; ubiquitous and social computing; layout instruments; enter expertise; programming consumer interfaces; 3D scientific deformable versions and visualization; deep studying equipment for computing device imaginative and prescient and images; multibiometric.
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Extra resources for Articulated Motion and Deformable Objects: 9th International Conference, AMDO 2016, Palma de Mallorca, Spain, July 13-15, 2016, Proceedings
As can be seen, the recognition rate is highly improved going from 83 % in the single-view case to a 92 % global score. This big improvement in the recognition is caused by the high recognition increase in the case of the trousers. The trousers are the biggest object in the dataset. Acquiring more views allows the system to disambiguate in complicated cases like partial views of the trousers. The confusion matrix for this scenario is shown in Fig. 4b. As stated, the confusion between items has drastically dropped.
Head pose estimation in the wild using approximate view manifolds. In: Proceedings of Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2015) 19. : Combining head pose and eye location information for gaze estimation. IEEE Trans. Image Process. 21(2), 802–815 (2012) 20. : A two-stage head pose estimation framework and evaluation. Pattern Recognit. 41(3), 1138–1158 (2008) 21. : Face detection, pose estimation, and landmark localization in the wild. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) (2012) Spatiotemporal Facial Super-Pixels for Pain Detection Dennis H.
4 Conclusions We have designed a system capable of grabbing a cloth item with a robot arm and use a Deep Convolutional Neural Network to classify its type. Contrary to other approaches, we deal with partial views of garments that cause confusion in the classiﬁcation. Experimental results show that we can highly improve the classiﬁcation score by using the robot arm to rotate the garment to obtain more views of it. The obtained results should be later used in a complete system to decide the number of required views and best viewing directions.