The interest in content adaptive mesh generation of images has been arising lately due to its wide area of applications in image processing. The major issue is to represent an image with a low number of pixels while preserving its content. These pixels or the nonuniform samples are then used to generate a mesh that approximates the corresponding image. This work presents a novel method based on Binary Space Partitions in combination with three clustering schemes to approximate an image with a mesh. The algorithm has the ability to simultaneously reduce the number of pixels and generate the mesh approximation. The idea is to assume each triangle of the mesh as a plane. Consequently, it will be possible to reconstruct the inlying pixels with planar equations defined from the three nodes of each triangle. If a triangle's equation does not have the ability to reconstruct the pixels lying within up to a predefined error, it is split into two new triangles. Tested on several real images, the proposed method leads to reduced size meshes in a fast manner while retaining the visual quality of the reconstructed images. In addition, it is parallelizable due to the property of Binary Space Partitions which facilitates its application in real-time scenarios.
We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user's body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.
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