• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.1
• /
• pp.151-163
• /
• 1990

In this paper, rule-based stereo algorithm is explored to obtain three dimensional information of an object. In the preprocessing of the stereo matching, feature points of stereo images must be less sensitive to noise and well linked. For this purpose, a new feature points detection algorithm is developed. For performing the stereo matching which is most important process of the stereo algorithm, the feature representation of feature points is first described. The feature representation is then used for a rule-based stereo algorithm to determine the correspondence between the input stereo images. Finally, the three dimensional information of the object is determined from the correspondence of the feature points of right and left images.

• The KIPS Transactions:PartB
• /
• v.19B no.2
• /
• pp.113-122
• /
• 2012

This paper presents a new stereo matching technique which is based on the matching of feature windows and feature links. The proposed method uses the FAST feature detector to find image features in stereo images and determines the correspondences of the detected features in the stereo images. We define a feature window which is an image region containing several image features. The proposed technique consists of two matching steps. First, a feature window is defined in a standard image and its correspondence is found in a reference image. Second, the corresponding features between the matched windows are determined by using the feature link technique. If there is no correspondence for an image feature in the standard image, it's disparity is interpolated by neighboring feature sets. We evaluate the accuracy of the proposed technique by comparing our results with the ground truth of in a stereo image database. We also compare the matching accuracy and computation time with two conventional feature-based stereo matching techniques.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.4
• /
• pp.277-285
• /
• 2004

We present a new hybrid stereo matching technique in terms of the co-operation of area-based stereo and feature-based stereo. The core of our technique is that feature matching is carried out by the reference of the disparity evaluated by area-based stereo. Since the reference of the disparity can significantly reduce the number of feature matching combinations, feature matching error can be drastically minimized. One requirement of the disparity to be referenced is that it should be reliable to be used in feature matching. To measure the reliability of the disparity, in this paper, we employ the self-consistency of the disunity Our suggested technique is applied to the detection of 3D line segments by 2D line matching using our hybrid stereo matching, which can be efficiently utilized in the generation of the rooftop model from urban imagery. We carry out the experiments on our hybrid stereo matching scheme. We generate synthetic images by photo-realistic simulation on Avenches data set of Ascona aerial images. Experimental results indicate that the extracted 3D line segments have an average error of 0.5m and verify our proposed scheme. In order to apply our method to the generation of 3D model in urban imagery, we carry out Preliminary experiments for rooftop generation. Since occlusions are occurred around the outlines of buildings, we experimentally suggested multi-image hybrid stereo system, based on the fusion of 3D line segments. In terms of the simple domain-specific 3D grouping scheme, we notice that an accurate 3D rooftop model can be generated. In this context, we expect that an extended 3D grouping scheme using our hybrid technique can be efficiently applied to the construction of 3D models with more general types of building rooftops.

• Proceedings of the IEEK Conference
• /
• 2001.06d
• /
• pp.69-72
• /
• 2001

This paper proposes a positional estimation method for extracted line features by stereo vision. Based on given reference plane, planar surfaces corresponding the given plane are first extracted. Then, features in the planar surfaces are selected. Using the Extended Kalman Filter the feature positions are estimated by combining area- and feature-based stereo matching results. Experimental results show the proposed method is feasible.

• Korean Journal of Remote Sensing
• /
• v.23 no.5
• /
• pp.401-408
• /
• 2007

Animated anaglyph is the most cost-effective method for 3D stereo visualization of virtual or actual 3D geo-based data model. Unlike 3D anaglyph scene generation using paired epipolar images, the main data sets of this study is the multi-typed 3D feature model containing 3D shaped objects, DEM and satellite imagery. For this purpose, a prototype implementation for 3D animated anaglyph using OpenGL API is carried out, and virtual 3D feature modeling is performed to demonstrate the applicability of this anaglyph approach. Although 3D features are not real objects in this stage, these can be substituted with actual 3D feature model with full texture images along all facades. Currently, it is regarded as the special viewing effect within 3D GIS application domains, because just stereo 3D viewing is a part of lots of GIS functionalities or remote sensing image processing modules. Animated anaglyph process can be linked with real-time manipulation process of 3D feature model and its database attributes in real world problem. As well, this approach of feature-based 3D animated anaglyph scheme is a bridging technology to further image-based 3D animated anaglyph rendering system, portable mobile 3D stereo viewing system or auto-stereo viewing system without glasses for multi-viewers.

• Journal of information and communication convergence engineering
• /
• v.12 no.2
• /
• pp.128-134
• /
• 2014

In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.1
• /
• pp.52-57
• /
• 2007

In this paper we proposed multi-directional matching windows combined by multi-dimensional feature vector matching, which uses not only intensity values but also multiple feature values, such as variance, first and second derivative of pixels. Multi-dimensional feature vector matching has the advantage of compensating the drawbacks of area-based stereo matching using one feature value, such as intensity. We define matching cost of a pixel by the minimum value among eight multi-dimensional feature vector distances of the pixels expanded in eight directions having the interval of 45 degrees. As best stereo matches, we determine the two points with the minimum matching cost within the disparity range. In the experiment we used aerial imagery and IKONOS satellite imagery and obtained more accurate matching results than that of conventional matching method.

• Journal of Ubiquitous Convergence Technology
• /
• v.1 no.1
• /
• pp.9-17
• /
• 2007

Feature based stereo matching is an effective way to perform 3D building reconstruction. However, in urban scene, the cluttered background and various building structures may interfere with the performance of building reconstruction. In this paper, we propose a novel method to robustly reconstruct buildings on the basis of rectangle regions. Firstly, we propose a multi-scale linear feature detector to obtain the salient line segments on the object contours. Secondly, candidate rectangle regions are extracted from the salient line segments based on their local information. Thirdly, stereo matching is performed with the list of matching line segments, which are boundary edges of the corresponding rectangles from the left and right image. Experimental results demonstrate that the proposed method can achieve better accuracy on the reconstructed result than pixel-level stereo matching.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.1
• /
• pp.58-63
• /
• 2020

This paper proposes the method for improving the localization accuracy of the mobile robot based on the stereo camera. To restore the position information from stereo images obtained by the stereo camera, the corresponding point which corresponds to one pixel on the left image should be found on the right image. For this, there is the general method to search for corresponding point by calculating the similarity of pixel with pixels on the epipolar line. However, there are some disadvantages because all pixels on the epipolar line should be calculated and the similarity is calculated by only pixel value like RGB color space. To make up for this weak point, this paper implements the method to search for the corresponding point simply by calculating the gap of x-coordinate when the feature points, which are extracted by feature extraction and matched by feature matching method, are a pair and located on the same y-coordinate on the left/right image. In addition, the proposed method tries to preserve the number of feature points as much as possible by finding the corresponding points through the conventional algorithm in case of unmatched features. Because the number of the feature points has effect on the accuracy of the localization. The position of the mobile robot is compensated based on 3-D coordinates of the features which are restored by the feature points and corresponding points. As experimental results, by the proposed method, the number of the feature points are increased for compensating the position and the position of the mobile robot can be compensated more than only feature extraction.

• Journal of Digital Contents Society
• /
• v.5 no.4
• /
• pp.257-263
• /
• 2004

This paper is related to the object-based stereo matching algorithm which makes it possible to estimate inner-region disparities for each segmented region. First, several sample points are selected for effectively representing the segmented region, Next, stereo matching is applied to the small area within segmented region which existed in the neighborhood or each sample point. Finally, inner-region disparities are interpolated using a plane equation with disparity of each selected sample. According to the proposed method, the problem of feature-based method that the depth estimation is possible only in the feature points can be solved through the propagation of the disparity in the sample point into the inside of the region. Also, as selecting sample points in contour of segmented region we can effectively suppress obscurity which is occurred in the depth estimation of the monotone region in area-based methods.