Parametric Human Body Reconstruction Based on Sparse Key Points.

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.

Adaptive reconstruction of millimeter-wave radiometric images.

We present a robust method to reconstruct a millimeter-wave image from a passive sensor. The method operates directly on the raw samples from the radiometer. It allocates for each pixel to be estimated a patch in the space formed by all the raw samples of the image. It then estimates the noise in the patch by measuring some distances that reflect how far the samples are from forming a piecewise smooth surface. It then allocates a weight for each sample that defines its contribution in the pixel reconstruction. This is done via a smoothing Kernel that enforces the distances to have a piecewise smooth variation inside the patch. Results on real datasets show that our scheme leads to more contrast and less noise and the shape of an object is better preserved in a constructed image compared to state-of-the-art schemes. The proposed scheme produces better results even with low integration times, i.e., 10% of the total integration time used in our experiments.

Camera-pose estimation via projective Newton optimization on the manifold.

Determining the pose of a moving camera is an important task in computer vision. In this paper, we derive a projective Newton algorithm on the manifold to refine the pose estimate of a camera. The main idea is to benefit from the fact that the 3-D rigid motion is described by the special Euclidean group, which is a Riemannian manifold. The latter is equipped with a tangent space defined by the corresponding Lie algebra. This enables us to compute the optimization direction, i.e., the gradient and the Hessian, at each iteration of the projective Newton scheme on the tangent space of the manifold. Then, the motion is updated by projecting back the variables on the manifold itself. We also derive another version of the algorithm that employs homeomorphic parameterization to the special Euclidean group. We test the algorithm on several simulated and real image data sets. Compared with the standard Newton minimization scheme, we are now able to obtain the full numerical formula of the Hessian with a 60% decrease in computational complexity. Compared with Levenberg-Marquardt, the results obtained are more accurate while having a rather similar complexity.

Content adaptive mesh representation of images using binary space partitions.

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.

Fine-scale genetic mapping using independent component analysis.

The aim of genetic mapping is to locate the loci responsible for specific traits such as complex diseases. These traits are normally caused by mutations at multiple loci of unknown locations and interactions. In this work, we model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process. Given this model, we propose a new fine-scale genetic mapping method based on independent component analysis. The proposed method outputs both independent associated groups of SNPs in addition to specific associated SNPs with the phenotype. It is applied to a clinical data set for the Schizophrenia disease with 368 individuals and 42 SNPs. It is also applied to a simulation study to investigate in more depth its performance. The obtained results demonstrate the novel characteristics of the proposed method compared to other genetic mapping methods. Finally, we study the robustness of the proposed method with missing genotype values and limited sample sizes.