Detecting human heads with their orientations

We propose a two-step method for detecting human heads with their orientations. In the first step, the method employs an ellipse as the contour model of human-head appearances to deal with wide variety of appearances. Our method then evaluates the ellipse to detect possible human heads. In the second step, on the other hand, our method focuses on features inside the ellipse, such as eyes, the mouth or cheeks, to model facial components. The method evaluates not only such components themselves but also their geometric configuration to eliminate false positives in the first step and, at the same time, to estimate face orientations. Our intensive experiments show that our method can correctly and stably detect human heads with their orientations

3D discrete rotations using hinge angles

International audienceIn this paper, we study 3D rotations on grid points computed by using only integers. For that purpose, we investigate the intersection between the 3D half-grid and the rotation plane. From this intersection, we define 3D hinge angles which determine a transit of a grid point from a voxel to its adjacent voxel during the rotation. Then, we give a method to sort all 3D hinge angles with integer computations. The study of 3D hinge angles allows us to design a 3D discrete rotation and to estimate the rotation between a pair of digital images in correspondence

Discrete plane segmentation and estimation from a point cloud using local geometric patterns

International audienceThis paper presents a method for segmenting a 3D point cloud into planar surfaces using recently obtained discrete geometry results. In discrete geometry, a discrete plane is defined as a set of grid points lying between two parallel planes with a small distance, called thickness. Contrarily to the continuous case, there exist a finite number of local geometric patterns (LGPs) appearing on discrete planes. Moreover, such a LGP does not possess the unique normal vector but a set of normal vectors. By using those LGP properties, we first reject non-linear points from a point cloud, and then classify non-rejected points whose LGPs can have common normal vectors into a planar-surface-point set. From each segmented point set, we also estimate parameters of a discrete plane by minimizing its thickness