• 한국정보디스플레이학회:학술대회논문집
• /
• 2007.08b
• /
• pp.1504-1507
• /
• 2007

Depth data for is very important data for 3D display. Disparity and depth data makes users to feel 3D effect. We used stereo camera to measure depth and made fast algorithm to get in real time. This vision system can be substituted for expensive laser system.

• Proceedings of the Optical Society of Korea Conference
• /
• 2007.02a
• /
• pp.125-126
• /
• 2007

This paper discussed a method for estimating the distance using the stereo zoom lens module. A method for estimating the distance using single zoom lens is limited in convergence angle. So, we composed horizontal moving camera and estimated the distance. In the experiment, we get the wider convergence angle and more precise distance than single camera's.

• Journal of Broadcast Engineering
• /
• v.13 no.5
• /
• pp.596-601
• /
• 2008

In this paper, we propose a new scheme to generate region-of-interest (ROI) enhanced depth maps in the hybrid camera system, which is composed of a low-resolution depth camera and a high-resolution stereoscopic camera. The proposed method creates an ROI depth map for the left image by carrying out a three-dimensional (3-D) warping operation onto the depth information obtained from the depth camera. Then, we generate a background depth map for the left image by applying a stereo matching algorithm onto the left and right images captured by the stereoscopic camera. Finally, we merge the ROI map with the background one to create the final depth map. The proposed method provides higher quality depth information on ROI than the previous methods.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.2C
• /
• pp.166-174
• /
• 2006

In this paper, a new stereo object tracking system using the disparity motion vector is proposed. In the proposed method, the time-sequential disparity motion vector can be estimated from the disparity vectors which are extracted from the sequence of the stereo input image pair and then using these disparity motion vectors, the area where the target object is located and its location coordinate are detected from the input stereo image. Being based on this location data of the target object, the pan/tilt embedded in the stereo camera system can be controlled and as a result, stereo tracking of the target object can be possible. From some experiments with the 2 frames of the stereo image pairs having 256$\times$256 pixels, it is shown that the proposed stereo tracking system can adaptively track the target object with a low error ratio of about 3.05$\%$ on average between the detected and actual location coordinates of the target object.

• Journal of Biosystems Engineering
• /
• v.28 no.1
• /
• pp.65-76
• /
• 2003

The analysis of cow body parameters is important to provide some useful information fur cow management and cow evaluation. Present methods give many stresses to cows because they are invasive and constrain cow postures during measurement of body parameters. This study was conducted to develop the stereo vision system fur non-invasive analysis of cow body features. Body feature parameters of 16 heads at two farms(A, B) were measured using scales and nineteen stereo images of them with walking postures were captured under outdoor illumination. In this study, the camera calibration and inverse perspective transformation technique was established fer the stereo vision system. Two calibration results were presented for farm A and fm B, respectively because setup distances from camera to cow were 510 cm at farm A and 630cm at farm B. Calibration error values fer the stereo vision system were within 2 cm for farm A and less than 4.9 cm for farm B. Eleven feature points of cow body were extracted on stereo images interactively and five assistant points were determined by computer program. 3D world coordinates for these 15 points were calculated by computer program and also used for calculation of cow body parameters such as withers height. pelvic arch height. body length. slope body length. chest depth and chest width. Measured errors for body parameters were less than 10% for most cows. For a few cow. measured errors for slope body length and chest width were more than 10% due to searching errors fer their feature points at inside-body positions. Equation for chest girth estimated by chest depth and chest width was presented. Maximum of estimated error fur chest girth was within 10% of real values and mean value of estimated error was 8.2cm. The analysis of cow body parameters using stereo vision system were successful although body shape on the binocular stereo image was distorted due to cow movements.

• Journal of KIISE:Software and Applications
• /
• v.31 no.11
• /
• pp.1496-1504
• /
• 2004

In this paper, we propose an algorithm for initializing a target vehicle detection, tracking the vehicle and estimating the distance from it on the stereo images acquired from a forward-looking stereo camera mounted on a road driving vehicle. The process of vehicle detection extracts road region using lane recognition and searches vehicle feature from road region. The distance of tracking vehicle is estimated by TSS correlogram matching from stereo Images. Through the simulation, this paper shows that the proposed method segments, matches and tracks vehicles robustly from image sequences obtained by moving stereo camera.

• The Journal of Korea Robotics Society
• /
• v.4 no.4
• /
• pp.298-304
• /
• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.201-204
• /
• 2005

Recently, the generation of panoramic images and high quality mosaic images from video sequences has been attempted by a variety of investigations. Among a matter of investigation, in this paper, left and right stereo mosaic image generation utilizing airborne-video sequence images is focused upon. The stereo mosaic image is generated by creating left and right mosaic image which is generated by front and rear slit having different viewing angle in consecutive video frame images. The generation of stereo mosaic image proposed in this paper consists of several processes: camera parameter estimation for each video frame image, rectification, slicing, motion parallax elimination and image mosaicking. However it is necessary to check the feasibility on generating stereo mosaic image as explained processes. Therefore, in this paper, we performed the feasibility test on generating stereo mosaic image using video frame images. In doing so, anaglyphic image for stereo mosaic images is generated and tested for feasibility check.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.18 no.1
• /
• pp.137-145
• /
• 2004

In this paper, a new algorithm for the segmentation of the multi moving objects at the 3 dimension space and the method of measuring the distance from the camera to the moving object by using stereo video monitoring system is proposed. It get the input image of left and right from the stereo video monitoring system, and the area of the multi moving objects segmented by using adaptive threshold and PRA(pixel recursive algorithm). Each of the object segmented by window mask, then each coordinate value and stereo disparity of the multi moving objects obtained from the window masks. The distance of the multi moving objects can be calculated by this disparity, the feature of the stereo vision system and the trigonometric function. From the experimental results, the error rate of a distance measurement be existed within 7.28％, therefore, in case of implementation the proposed algorithm, the stereo security system, the automatic moving robot system and the stereo remote control system will be applied practical application.

• The KIPS Transactions:PartB
• /
• v.10B no.2
• /
• pp.135-142
• /
• 2003

Robust extraction of 3D object's features, shape and global motion information from 2D image sequence is described. The object's 21 feature points on the pyramid type synthetic object are extracted automatically using color transform technique. The extracted features are used to recover the 3D shape and global motion of the object using stereo paraperspective camera model and sequential SVD(Singuiar Value Decomposition) factorization method. An inherent error of depth recovery due to the paraperspective camera model was removed by using the stereo image analysis. A 30 synthetic object with 21 features reflecting various position was designed and tested to show the performance of proposed algorithm by comparing the recovered shape and motion data with the measured values.