By Richard Chbeir, Yannis Manolopoulos, Ilias Maglogiannis, Reda Alhajj
This e-book constitutes the refereed lawsuits of the eleventh IFIP WG 12.5 overseas convention on man made Intelligence functions and recommendations, AIAI 2015, held in Bayonne, France, in September 2015. The 35 revised complete papers offered have been conscientiously reviewed and chosen from eighty one submissions. The papers function hugely novel learn describing leading edge algorithms, platforms, instruments and strategies, in addition to offering complex prototypes within the region of synthetic intelligence.
Read Online or Download Artificial Intelligence Applications and Innovations: 11th IFIP WG 12.5 International Conference, AIAI 2015, Bayonne, France, September 14–17, 2015, Proceedings PDF
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Extra resources for Artificial Intelligence Applications and Innovations: 11th IFIP WG 12.5 International Conference, AIAI 2015, Bayonne, France, September 14–17, 2015, Proceedings
The use of the fisheye cameras, instead of perspective ones, allows the simultaneous silhouette acquisition at a large number of different positions (pi) and orientations. Thus, while the human silhouette is walking along a trajectory, many frames can be acquired by both cameras. 2), to build a more accurate visual hull of the object. Generating Binary Silhouettes Along a Trajectory. To simulate the human silhouette trajectory, we place the synthetic model, shown in Fig. 2 (a), at several positions along an arbitrary path shown in Fig.
In this paper, we describe a method for non-rigid registration of cardiac images and quantify the uncertainty of the estimated transformation. The registration method was validated on a clinical dataset with results comparable to previously published methods, but with the added benefit of also providing uncertainty estimates which may be important to take into account during cardiac surgery procedures. Improvements are demonstrated in accuracy and robustness over non-rigid registration between CT and echocardiography images and it is shown how to summarize this uncertainty in terms of uncertainty quantification and user interaction.
The proposed algorithm is capable of detecting such linear structures using local image curvature information obtained by the Hessian matrix. A linear structure is characterized as streak, based on its geometric characteristics. The streak detection algorithm is applied to a number of dermoscopy images containing malignant and non-malignant skin lesions. In this work we propose also a new class of streak based image features and we investigate their value in image classification according to malignancy.