161,116 research outputs found
3D Face Recognition using Significant Point based SULD Descriptor
In this work, we present a new 3D face recognition method based on Speeded-Up
Local Descriptor (SULD) of significant points extracted from the range images
of faces. The proposed model consists of a method for extracting distinctive
invariant features from range images of faces that can be used to perform
reliable matching between different poses of range images of faces. For a given
3D face scan, range images are computed and the potential interest points are
identified by searching at all scales. Based on the stability of the interest
point, significant points are extracted. For each significant point we compute
the SULD descriptor which consists of vector made of values from the convolved
Haar wavelet responses located on concentric circles centred on the significant
point, and where the amount of Gaussian smoothing is proportional to the radii
of the circles. Experimental results show that the newly proposed method
provides higher recognition rate compared to other existing contemporary models
developed for 3D face recognition
Self-learning voxel-based multi-camera occlusion maps for 3D reconstruction
The quality of a shape-from-silhouettes 3D reconstruction technique strongly depends on the completeness of the silhouettes from each of the cameras. Static occlusion, due to e.g. furniture, makes reconstruction difficult, as we assume no prior knowledge concerning shape and size of occluding objects in the scene. In this paper we present a self-learning algorithm that is able to build an occlusion map for each camera from a voxel perspective. This information is then used to determine which cameras need to be evaluated when reconstructing the 3D model at every voxel in the scene. We show promising results in a multi-camera setup with seven cameras where the object is significantly better reconstructed compared to the state of the art methods, despite the occluding object in the center of the room
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