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

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.23 no.11 s.170
• /
• pp.1929-1939
• /
• 1999

This paper presents a neural network approach, which was named PRONET, to 3D object recognition and pose calculation. 3D objects are represented using a set of centroidal profile patterns that describe the boundary of the 2D views taken from evenly distributed view points. PRONET consists of the training stage and the execution stage. In the training stage, a three-layer feed-forward neural network is trained with the centroidal profile patterns using an error back-propagation method. In the execution stage, by matching a centroidal profile pattern of the given image with the best fitting centroidal profile pattern using the neural network, the identity and approximate orientation of the real object, such as a workpiece in arbitrary pose, are obtained. In the matching procedure, line-to-line correspondence between image features and 3D CAD features are also obtained. An iterative model posing method then calculates the more exact pose of the object based on initial orientation and correspondence.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.28B no.7
• /
• pp.578-589
• /
• 1991

In this paper, there is proposed a novel concept of Incremintal Circle Transform which can describe the boundary contour of a two-dimensional object without object without occlusions. And a pattern recognition algorithm to determine the posture of an object is developed with the aid of line integral and similarity transform. Also, It is confirmed via experiments that the algorithm can find the posture of an object in a very fast manner independent of the starting point for boundary coding and the position of the object.

• Proceedings of the Korea Contents Association Conference
• /
• 2006.05a
• /
• pp.50-53
• /
• 2006

In this paper I present a system that with infrared vision sense tracks the position and orientation of a wireless object on a tabletop display surface. The system offers two types of improvements over existing computer vision tracking approaches. First, the system tracks an object accurately without susceptibility to changes in lighting conditions. Second, the system tracks not only the orientation but button click state of the object. This system can detect these changes in real time. Finally, I present an application of the system : Color Lab Box.

• Journal of Game and Entertainment
• /
• v.2 no.2
• /
• pp.70-74
• /
• 2006

In this paper I present a system that with infrared vision sense tracks the position and orientation of a wireless object on a tabletop display surface. The system offers two types of improvements over existing computer vision tracking approaches. First, the system tracks an object accurately without susceptibility to changes in lighting conditions. Second, the system tracks not only the orientation but button click state of the object. This system can detect these changes in real time. Finally, I present an application of the system : Color Lab Box.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.12
• /
• pp.38-44
• /
• 1995

This paper presents a method to measure the position and orientation of a Cylindrical Object(unknown the eiameter and length) lying on a floor, using a camera. The two extreme cross section of the cylinder will be viewed as distorted ellipese or circular are, while its limb edge will be shown as two straight lines. The diameter of the cylinder is determined from the geometric properties of the two straight lines, which in turn provides information regarding the length of the cylinder. From the 3-dimensional measurement, the 3D coordinates of the center points of the two extreme cross sections are determined to give the position and orientation of the cylinder. This method is used for automated pick-and-place operations of cylinder, such as sheet coils, or drums in warehouses.

• Korean Journal of Cognitive Science
• /
• v.27 no.3
• /
• pp.355-376
• /
• 2016

We used holes defined by color similarity (Experiment 1) and binocular disparity (Experiment 2) to study how the inner contour of an object (i.e., boundary of a hole in it) is encoded in visual working memory. Many studies in VWM have shown that an object's boundary properties can be integrated with its surface properties via their shared spatial location, yielding an object-based encoding benefit. However, encoding of the hole contours has rarely been tested. We presented objects (squares or circles) containing a bar under a change detection paradigm, and relevant features to be remembered were the color of objects and the orientation of bars (or holes). If the contour of a hole belongs to the surrounding object rather than to the hole itself, the object-based feature binding hypothesis predicts that the shape of it can be integrated with color of an outer object, via their shared spatial location. Thus, in the hole display, change detection performance was expected to better than in the conjunction display where orientation and color features to be remembered were assigned to different parts of a conjunction object, and comparable to that in a single bar display where both orientation and color were assigned into a single bar. However, the results revealed that performance in the hole display did not differ from that in the conjunction display. This suggests that the shape of holes is not automatically encoded together with the surface properties of the outer object via object-based feature binding, but encoded independently from the surrounding object.

• Journal of Korea Multimedia Society
• /
• v.6 no.4
• /
• pp.698-706
• /
• 2003

The primary goal of surface approximation is to reduce the degree of deviation of the simplified surface from the original surface. However it is difficult to define the metric that can measure the amount of deviation quantitatively. Many of the existing studies analogize it by using the change of the scalar quantity before and after simplification. This approach makes a lot of sense in the point that the local surfaces with small scalar are relatively less important since they make a low impact on the adjacent areas and thus can be removed from the current surface. However using scalar value alone there can exist many cases that cannot compute the degree of geometric importance of local surface. Especially the perceptual geometric features providing important clues to understand an object, in our observation, are generally constructed with small scalar value. This means that the distinguishing features can be removed in the earlier stage of the simplification process. In this paper, to resolve this problem, we present various factors and their combination as the metric for calculating the deviation error by introducing the orientation of local surfaces. Experimental results indicate that the surface orientation has an important influence on measuring deviation error and the proposed combined error metric works well retaining the relatively high curvature regions on the object's surface constructed with various and complex curvatures.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10b
• /
• pp.271-276
• /
• 1993

Here in the present paper. A methodology for understanding scenes which includes moving objects in it, in the framework of notion of concepts. First by conceptualizing, understanding an object which is an element of a scene will be described. Then how to know the direction to which that object is heading will be discussed. Further, the methodology proposed, for understanding conceptually the motion of an object will be described utilizing the above knowledge of direction.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10b
• /
• pp.917-922
• /
• 1990

Pattern classification is an essential step in automatic robotic assembly which joins together finite number of seperated industrial parts. In this paper, a fast and systematic algorithm for classifying occlusion-free objects is proposed, using the notion of incremental circle transform which describes the boundary contour of an object as a parametric vector function of incremental elements. With similarity transform and line integral, normalized determinant curve of an object classifies each object, independent of position, orientation, scaling of an object and cyclic shift of the stating point for the boundary description.