• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.1
• /
• pp.54-59
• /
• 2004

We suggest a 3D vision inspection algorithm which is based on the external shape feature. Because many electronic parts have the regular shape, if we have the database of pattern and can recognize the object using the database of the object s pattern, we can inspect many types of electronic parts. Our proposed algorithm uses the geometrical pattern matching method and 3D database on the electronic parts. We applied our suggested algorithm fer inspecting several objects including typical IC and capacitor. Through the experiments, we could find that our suggested algorithm is more effective and more robust to the inspection environment(rotation angle, light source, etc.) than conventional 2D inspection methods. We also compared our suggested algorithm with the feature space trajectory method.

• Journal of Korean Ophthalmic Optics Society
• /
• v.16 no.2
• /
• pp.179-186
• /
• 2011

Purpose: This study aimed to compare and assess changes of visual functions in viewing an anaglyph 3D image. Methods: Visual functions were examined before and after viewing a 2D image and an anaglyph 3D image with red-green glasses on seventy college students (mean age = 22.29${\pm}$2.19 years). Visual function tests were carried out for von Graefe phoria test, accommodative amplitude test by (-) lens addition, negative relative accommodation (NRA) and positive relative accommodation (PRA) test, negative relative convergence (NRC) and positive relative convergence (PRC) test, accommodative facility, and vergence facility test. Results: Assessment of the visual functions indicated that near exophoria and accommodative amplitude were reduced after viewing a 3D image, and although there were small changes in relation to these findings, NRC and PRC showed tendencies to increase and decrease at near, respectively. There were no significant changes with NRA and PRA, and accommodative and vergence facility were shown to have improved. Conclusions: Changes of visual functions were more in the 3D image than the 2D image, especially at near than distance. Particularly, the improvement of accommodative and vergence facility could be related to an effect of subsequent accommodation and vergence shift to have stereopsis in the 3D image. These results indicate that an anaglyph 3D image may, to some extent, be the effect of vision training such as anaglyphs.

• Investigative Magnetic Resonance Imaging
• /
• v.9 no.1
• /
• pp.24-29
• /
• 2005

Purpose : Instead of conventional two-dimensional (2-D) visual stimuli, three-dimensional (3-D) visual stimuli with stereoscopic vision were employed for the study of functional Magnetic Resonance Imaging (f-MRI). In this paper f-MRI with 3-D visual stimuli is investigated in comparison with f-MRI with 2-D visual stimuli. Materials and Methods : The anaglyph which generates stereoscopic vision by viewing color coded images with red-blue glasses is used for 3-D visual stimuli. Two-dimensional visual stimuli are also used for comparison. For healthy volunteers, f-MRI experiments were performed with 2-D and 3-D visual stimuli at 3.0 Tesla MRI system. Results : Occipital lobes were activated by the 3-D visual stimuli similarly as in the f-MRI with the conventional 2-D visual stimuli. The activated regions by the 3-D visual stimuli were, however, larger than those by the 2-D visual stimuli by $18\%$. Conclusion : Stereoscopic vision is the basis of the three-dimensional human perception. In this paper 3-D visual stimuli were applied using the anaglyph. Functional MRI was performed with 2-D and 3-D visual stimuli at 3.0 Tesla whole body MRI system. The occipital lobes activated by the 3-D visual stimuli appeared larger than those by the 2-D visual stimuli by about $18\%$. This is due to the more complex character of the 3-D human vision compared to 2-D vision. The f-MRI with 3-D visual stimuli may be useful in various fields using 3-D human vision such as virtual reality, 3-D display, and 3-D multimedia contents.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1920-1925
• /
• 2004

This paper describes a new method for 3D object recognition which used surface segment-based stereo vision. The position and orientation of an objects is identified accurately enabling a robot to pick up, even though the objects are multiple and partially occluded. The stereo vision is used to get the 3D information as 3D sensing, and CAD model with its post processing is used for building models. Matching is initially performed using the model and object features, and calculate roughly the object's position and orientation. Though the fine adjustment step, the accuracy of the position and orientation are improved.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.669-672
• /
• 2003

3D object recognition is difficult but important in computer vision. The important thing is to understand about the relationship between a geometric structure in three dimensions and its image projection. Most 3D recognition systems construct models either manually or by training the pose and orientation of the objects. But both approaches are not satisfactory. In this paper, we focus on a commercial CAD model as a third type of model building for vision. The models are expressed in Initial Graphics Exchanges Specification(IGES) output and reconstructed in a pinhole camera coordinate.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.1
• /
• pp.8-16
• /
• 2016

In the field of computer vision and robotics, bin picking is an important application area in which object pose estimation is necessary. Different approaches, such as 2D feature tracking and 3D surface reconstruction, have been introduced to estimate the object pose accurately. We propose a new approach where we can use both 2D image features and 3D surface information to identify the target object and estimate its pose accurately. First, we introduce a label detection technique using Maximally Stable Extremal Regions (MSERs) where the label detection results are used to identify the target objects separately. Then, the 2D image features on the detected label areas are utilized to generate 3D surface information. Finally, we calculate the 3D position and the orientation of the target objects using the information of the 3D surface.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.1
• /
• pp.161-166
• /
• 2011

This paper addresses the vision based autonomous walking control system. To handle the obstacles which exist beyond the field of view(FOV), we used the 3d panoramic depth image. Moreover, to decide the avoidance direction and walking motion of a humanoid robot for the obstacle avoidance by itself, we proposed the vision based path planning using 3d panoramic depth image. In the vision based path planning, the path and walking motion are decided under environment condition such as the size of obstacle and available avoidance space. The vision based path planning is applied to a humanoid robot, URIA. The results from these evaluations show that the proposed method can be effectively applied to decide the avoidance direction and the walking motion of a practical humanoid robot.

• 한국정보디스플레이학회:학술대회논문집
• /
• 2002.08a
• /
• pp.887-890
• /
• 2002

In this paper, a new 3D robot vision system using the hierarchical opto-digital algorithm is proposed and implemented. From some experimental results with the 20 frames of the stereo input image pairs, the proposed system is found to be able to effectively extract the area where the target object is located from the stereo input image regardless of the background noises.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.7
• /
• pp.634-640
• /
• 2015

This paper proposes a novel three-dimensional mapping algorithm in Omni-Directional Vision SLAM based on a fisheye image and laser scanner data. The performance of SLAM has been improved by various estimation methods, sensors with multiple functions, or sensor fusion. Conventional 3D SLAM approaches which mainly employed RGB-D cameras to obtain depth information are not suitable for mobile robot applications because RGB-D camera system with multiple cameras have a greater size and slow processing time for the calculation of the depth information for omni-directional images. In this paper, we used a fisheye camera installed facing downwards and a two-dimensional laser scanner separate from the camera at a constant distance. We calculated fusion points from the plane coordinates of obstacles obtained by the information of the two-dimensional laser scanner and the outline of obstacles obtained by the omni-directional image sensor that can acquire surround view at the same time. The effectiveness of the proposed method is confirmed through comparison between maps obtained using the proposed algorithm and real maps.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.12
• /
• pp.133-142
• /
• 1999

We introduce the algorithms of 2-D and 3-D position estimation using 2-D vision sensors. The sensors used in this research issue red laser slit light to the body. So, it is very convenient to obtain the coordinates of corner point or edge in sensor coordinate. Since the measured points are normally not fixed in the body coordinate, the additional conditions, that corner lines or edges are straight and fixed in the body coordinate, are used to find out the position and orientation of the body. In the case of 2-D motional body, we can find the solution analytically. But in the case of 3-D motional body, linearization technique and least mean squares method are used because of hard nonlinearity.