• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.10
• /
• pp.1005-1013
• /
• 2008

This paper describes a new sensor system for 3D range measurement using the structured infrared light. Environment and obstacle sensing is the key issue for mobile robot localization and navigation. Laser scanners and infrared scanners cover $180^{\circ}$ and are accurate but too expensive. Those sensors use rotating light beams so that the range measurements are constrained on a plane. 3D measurements are much more useful in many ways for obstacle detection, map building and localization. Stereo vision is very common way of getting the depth information of 3D environment. However, it requires that the correspondence should be clearly identified and it also heavily depends on the light condition of the environment. Instead of using stereo camera, monocular camera and the projected infrared light are used in order to reduce the effects of the ambient light while getting 3D depth map. Modeling of the projected light pattern enabled precise estimation of the range. Identification of the cells from the pattern is the key issue in the proposed method. Several methods of correctly identifying the cells are discussed and verified with experiments.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10b
• /
• pp.539-543
• /
• 2006

사람은 실내나 인공구조물을 가진 실외 환경에서 깊이를 지각하는데 소실점이라는 강력한 깊이 지각 단서를 활용한다. 소실점은 관찰자로부터 가장 먼 거리의 지점에 대응된다. 인간은 단안영상이 가지는 기하학적 성분을 이용해서 소실점을 추정하고, 소실점의 위치와 관찰자의 시점을 기준으로 상대적 깊이지각을 할 수 있다. 본 논문에서는 하나의 소실점을 가진 2차원 단안영상에서 유효한 소실점을 추정하고, 입력영상에 대한 소실점의 위치 관계를 이용한 상대적 깊이지도의 생성 방법을 제안한다.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.43 no.3 s.309
• /
• pp.30-38
• /
• 2006

In this paper, we improved to drops recognition rate according to distance using distance and depth information with 3D from stereo face images. A monocular face image has problem to drops recognition rate by uncertainty information such as distance of an object, size, moving, rotation, and depth. Also, if image information was not acquired such as rotation, illumination, and pose change for recognition, it has a very many fault. So, we wish to solve such problem. Proposed method consists of an eyes detection algorithm, analysis a pose of face, md principal component analysis (PCA). We also convert the YCbCr space from the RGB for detect with fast face in a limited region. We create multi-layered relative intensity map in face candidate region and decide whether it is face from facial geometry. It can acquire the depth information of distance, eyes, and mouth in stereo face images. Proposed method detects face according to scale, moving, and rotation by using distance and depth. We train by using PCA the detected left face and estimated direction difference. Simulation results with face recognition rate of 95.83% (100cm) in the front and 98.3% with the pose change were obtained successfully. Therefore, proposed method can be used to obtain high recognition rate with an appropriate scaling and pose change according to the distance.