Download Articulated Motion and Deformable Objects: 8th International by Francisco José Perales, José Santos-Victor PDF

By Francisco José Perales, José Santos-Victor

This e-book constitutes the refereed court cases of the eighth foreign convention on Articulated movement and Deformable items, AMDO 2014, held in Palma de Mallorca, Spain, in July 2014. The 18 papers offered have been rigorously reviewed and chosen from 37 submissions. The convention handled the subsequent themes: geometric and actual deformable types; movement research; articulated types and animation; modeling and visualization of deformable versions; deformable version purposes; movement research purposes; unmarried or a number of human movement research and synthesis; face modeling, monitoring, recuperating and popularity types; digital and augmented fact; haptics units; biometric techniques.

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Additional info for Articulated Motion and Deformable Objects: 8th International Conference, AMDO 2014, Palma de Mallorca, Spain, July 16-18, 2014. Proceedings

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Visual Analysis of Humans. Springer (2011) 2. : Poselet conditioned pictorial structures. In: IEEE CVPR, pp. 588–595 (2013) 3. : Articulated pose estimation with flexible mixtures-ofparts. In: IEEE CVPR, pp. 1385–1392 (2011) Holistic Human Pose Estimation with Regression Forests 29 4. : Pictorial structures revisited: People detection and articulated pose estimation. In: IEEE CVPR, pp. 1014–1021 (2009) 5. : Pictorial structures for object recognition. IJCV 61(1), 55–79 (2005) 6. : The representation and matching of pictorial structures.

The runtime is linear to the number of joint predictions s. Then, an integral matrix is generated for each cell in order to accumulate its votes. All the cells together form an integral image. Now, the window containing the maximum number of points can be found by sliding the window over the integral image. This can be done in O(m2 ) time where m is the resolution of the grid. 1 of the person’s bounding box height and the grid resolution to 100x100 pixels. The complexity of this algorithm is O(s + m2 ) which is much faster than O(T s2 ) of Mean Shift, where T is the number of iterations.

Regarding the easiness of recognition, the results concluded that: - Pleasure was correctly identified in the majority of the cases, except when P value was close to zero. It is the dimension that gives meaning to the expression. - Arousal was also correctly identified in most of the cases, except when A was negative and most of the subjects did not know how to assess it. - Dominance presented most of the perception problems. Subjects were not sure how to measure it or what to take into consideration to do it.

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