Isao Miyagawa

Simple Camera Calibration From a Single Image Using Five Points on Two Orthogonal 1-D Objects

We propose a simple and practical calibration technique that effectively estimates camera parameters from just five points on two orthogonal 1-D objects, each which has three collinear points, one of which is shared. We derive the basic equations needed to realize camera calibration from just five points observed on a single image that captures the objects. We describe a new camera calibration algorithm that estimates the camera parameters based on the basic equations and optimizes them by the bundle adjustment technique. Our method is validated by both computer simulated data and real images. The results show that the camera parameters yielded by our method are close to those yielded by existing methods. The tests demonstrate that our method is both effective and practical.

Motion and shape recovery based on iterative stabilization for modest deviation from planar motion

We describe an iterative stabilization method that can simultaneously recover camera motion and 3D shape from an image sequence captured under modest deviation from planar motion. This technique iteratively applies a factorization method based on planar motion and can approximate the observed image points to the 2D points projected under planar motion by stabilizing the camera motion. We apply the proposed method to aerial images acquired by a helicopter-borne camera and show better reconstruction of both motion and shape than Christy-Horauds perspective factorization. Moreover, we confirm that the reprojection errors calculated from the recovered camera motion and 3D shape are very similar to the optimum results yielded by bundle adjustment

Estimating the number of people in a video sequence via geometrical model

We propose a novel technique for estimating the number of people in a video sequence; it has the advantages of being stable even in crowded situations and needing no ground-truth data. By analyzing the geometrical relationships between image pixels and their intersection volumes in the real world quantitatively, a foreground image can be directly indicate the number of people. Because foreground detection can be done even in crowded situations, the proposed method can be applied to such situations. Also it can estimate the number of people in an a-priori manner, so it needs no ground-truth data which is necessary for existing feature-based estimating techniques. Experiments show the validity of the proposed method.

Contrasting shadow for occluder light suppression from one-shot image

Two main problems for front projection systems when a user appears between a screen and a projector are 1) shadows being cast on a screen and 2) the user being illuminated due to undesirable strong projection light. To solve these problems, it is necessary to know which projectors are occluded by a user and cast a shadow. We propose a method that suppresses occluder light from a single image based on the fact that the darkness of shadows on a screen is differentiated when projectors have different luminance. Contrary to previous approaches, our method utilizes different alpha masks for each projector to contrast shadows on a screen. This enables us to clarify the relationship between the shadow on a screen and the occluded projector. Additionally, by differentiating all projector luminances, our method makes it possible to discriminate shadows even when some shadow regions overlap on a screen. We implemented the proposed method into a three-projector system to demonstrate its effectiveness.