• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.1
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• pp.9-17
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• 2007

In this study, Digital Elevation Models (DEMs) were constructed from color images, grayscale images and each bands (Red, Green, Blue) of color image, and the accuracies of each DEMs were evaluated, And then, correlation coefficients between left and right images of each stereopairs were analyzed. The DEM can be constructed conventionally from the digital map and stereopair images using image matching. The image matching requires stereo satellite images or aerial photographs. In case of rotor aerial photographs, these are to be scanned in 3 bands (Red, Green, Blue). For this study, 5 types of images were acquired; color, grayscale, RED band, GREEN band, and BLUE band image. DEMs were constructed from 5 types of stereopair images and evaluated using elevation points of digital maps. In order to analyze the cause of various accuracies of each DEMs, the similarity between left and right images of each stereopairs were analyzed. Consequently, the accuracy of the DEM constructed from RED band images of color aerial photograph were proved best.

• Journal of the Korea Society of Computer and Information
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• v.19 no.7
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• pp.17-25
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• 2014

In the devices that leave video records, it is an important issue to distinguish whether the input image is a real object or a photo when securing an identifying image. Using a single image and sensor, which is a simple way to distinguish the target from distance measurement has many weaknesses. Thus, this paper proposes a way to distinguish a simple photo and a real object by using stereo images. It is not only measures the distance to the target, but also checks a three-dimensional effect by making the depth map of the face area. They take pictures of the photos and the real faces, and the measured value of the depth map is applied to the learning algorithm. Exactly through iterative learning to distinguish between the real faces and the photos looked for patterns. The usefulness of the proposed algorithm was verified experimentally.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.10
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• pp.839-846
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• 2016

Bin picking is a task of picking a small object from a bin. For accurate bin picking, the 3D pose information, position, and orientation of a small object is required because the object is mixed with other objects of the same type in the bin. Using this 3D pose information, a robotic gripper can pick an object using exact distance and orientation measurements. In this paper, we propose a 3D vision technique for accurate measurement of 3D position and orientation of small objects, on which a paper label is stuck to the surface. We use a maximally stable extremal regions (MSERs) algorithm to detect the label areas in a left bin image acquired from a stereo camera. In each label area, image features are detected and their correlation with a right image is determined by a stereo vision technique. Then, the 3D position and orientation of the objects are measured accurately using a transformation from the camera coordinate system to the new label coordinate system. For stable measurement during a bin picking task, the pose information is filtered by averaging at fixed time intervals. Our experimental results indicate that the proposed technique yields pose accuracy between 0.4~0.5mm in positional measurements and $0.2-0.6^{\circ}$ in angle measurements.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.695-698
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• 2014

As most the scanning systems developed until now provide radiation scan plane images of the inspected objects, there has been a limitation in judging exactly the shape of the objects inside a logistics container exactly with only 2-D radiation image information. Two 2-dimensional radiation images which have different disparity values are acquired from a newly designed stereo image acquisition system which has one additional line sensor to the conventional system. Using a matching algorithm the 3D reconstruction process which find the correspondence between the images is progressed. In this paper, we proposed a new volume based 3D reconstruction algorithm and experimental results show the proposed new volume based reconstruction technique can provide more efficient visualization for cargo inspection. The proposed technique can be used for the development of the high speed and more efficient non-destructive auto inspection system.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.1 s.5
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• pp.55-66
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• 1995

Most studies using satellite images have been performed to determine three dimensional positioning by stereoscopic analysis for stereo-pair or to extract digital elevation model by stereo matching using image correlation techniques. Because the small errors on the ground control points have a great impact on the results, however, it is hard to get reliable products when we analyze satellite orbital parameters or acquire digital elevation model by using only stereo-pair. Also, if there are noises, shadows, or clouds on the one of stereo pair, it is difficult to produce DEM(digital elevation model) on the area under analysis or to have good accuracy. In these case, it can be solved by systematic analysis of the multiple stereo images. This paper suggests the improvements on the accuracy of the digital elevation model by the developments of stereoscopic analysis techniques for the triplet of SPOT satellite images on the same area.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.487-497
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• 2013

In this paper, a method of using high resolution stereo images is proposed to efficiently detect DSM errors. Automatically generated DSMs from stereo matching can be a useful solution to acquire DSM data in various aspects but they may include many gross errors coming from automatic processing. Therefore, a method to detect the gross errors on DSM is required for efficient DSM update. In this paper, stereo analysis using high resolution stereo images was investigated to represent reliability of DSM grids. The analysis enabled automatic detection of the gross errors which greatly influenced DSM quality. We used the reference DSM to assess reliability of our proposed method. We confirmed from experimental results that our method can be a valuable DSM errors analysis for efficient DSM correction. Our method is useful to analyze and improve DSM accuracy for various types of DSM and DEM. It is expected that our approach can be exploited for achievement of reliable DSM and DEM.

• 한국지형공간정보학회:학술대회논문집
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• 1995.10a
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• pp.99-119
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• 1995

Most studies using satellite images have been performed to determine three dimensional positioning by stereoscopic analysis for stereo-pair or to extract digital elevation model by stereo matching using image correlation techniques. Because the small errors on the ground control points have a great impact on the results, honorer, it is hard to get reliable products when we analyze satellite orbital parameters or acquire digital elevation model by using only stereo-pair. Also, if there are noises, shadows, or clouds on the one of stereo pair, it is difficult to produce DEM(digital elevation model) on the area under analysis or to have good accuracy. In these case, it can be solved by systematic analysis of the multiple stereo images. This paper suggests the improvements on the accuracy of the digital elevation model by the developments of stereoscopic analysis techniques for the triplet of SPOT satellite images on the same area.

• Korean Journal of Construction Engineering and Management
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• v.10 no.2
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• pp.145-154
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• 2009

For developing an Automation equipment in construction, it is a key issue to develop 3D modeling technology which can be used for automatically recognizing environmental objects. Recently, for the development of "Intelligent Excavating System(IES), a research developing the real-time 3D terrain modeling technology has been implemented from 2006 in Korea and a stereo vision system is selected as the optimum technology. However, as a result of performance tests implemented in various earth moving environment, the 3D images obtained by stereo vision included considerable noise. Therefore, in this study, for getting rid of the noise which is necessarily generated in stereo image matching, the noise elimination algorithm of stereo-vision images for 3D terrain modeling was developed. The consequence of this study is expected to be applicable in developing an automation equipments which are used in field environment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.538-547
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• 2001

Currently, 2D real-time image coding techniques had great developments and many related products were commercially developed. However, these techniques lack the capability of handling 3D actuality, occurred by the advent of virtual reality, because they handle only the temporal transmission for 2D image. Besides, many 3D virtual reality researches have been studied in computer graphics. Since the graphical researches were limited to the application of artificial models, the 3D actuality for real scene images could not be managed also. Therefore, a new 3D model extraction method based on stereo vision, that can deal with real scene virtual reality, is proposed in this paper. The proposed method adapted a compact genetic algorithm using population-based incremental learning (PBIL) to matching environments, in order to reduce memory consumption and computational time of conventional genetic algorithms. Since the PBIL used a probability vector and competitive learning, the matching algorithm became simple and the computation load was considerably reduced. Moreover, the matching quality was superior than conventional methods. Even if the characteristics of images are changed, stable outputs were obtained without the modification of the matching algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1872-1879
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• 2001

As multiview video applications become more popular, correspondence problem for stereo image matching plays an important role in expanding view points. Thus, we propose an efficient dense disparity estimation algorithm considering features of each image pair of multiview image sets. Main concepts of the proposed algorithm are based on the region-dividing-bidirectional-pixel-matching method. This algorithm makes matching process efficient and keeps the reliability of the estimated disparities. Other improvement have obtained by proposed cost function, matching window expanding technique, disparity regularization, and disparity assignment in ambiguous region. These techniques make disparities more stable by removing false disparities and ambiguous regions. The estimated disparities are used to synthesize intermediate views of multiview images. Computer simulation demonstrates the excellence of the proposed algorithm in both subjective and objective evaluations. In addition, processing time is reduced as well.