• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.205-208
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• 2010

We introduce a novel feature extraction scheme for online handwritten digit based on utilizing Zernike moment and angulation feature. The time sequential signal from mouse movement on the writing pad is described as a sequence of consecutive points on the x-y plane. So, we can create data-set which are successive and time-sequential pixel position data by preprocessing. Data preprocessed is used for Zernike moment and angulation feature extraction. this feature is scale-, translation-, and rotation-invariant. The extracted specific feature is fed to a BP(backpropagation) neural network, which in turn classifies it as one of the nine digits. In this paper, proposed method not noly show high recognition rate but also need less learning data for 200 handwritten digit data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.190-196
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• 2013

Curved integral imaging is a simple method to display 3D images in space using lens array and provides wide viewing angle. In this paper, we propose a nonlinear 3D correlator based on the direct pixel-mapping (DPM) method in order to improve the recognition performance of 3D target object in curving-effective integral imaging. With this scheme, the elemental image array (EIA) originally picked up from a partially occluded 3-D target object can be converted into a resolution enhanced new EIA by using DPM method. Then, through nonlinear cross-correlations between the reconstructed reference and the target plane images, the improved pattern recognition can be performed from the correlation outputs. To show the feasibility of the proposed method, some preliminary experiments are carried out and results are presented by comparing the conventional method.

• Plant Journal
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• v.17 no.3
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• pp.37-41
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• 2021

P&ID((Piping and Instrument Diagram) is a key drawing in the engineering industry because it contains information about the units and instrumentation of the plant. Until now, simple repetitive tasks like listing symbols in P&ID drawings have been done manually, consuming lots of time and manpower. Currently, a deep learning model based on CNN(Convolutional Neural Network) is studied for drawing object detection, but the detection time is about 30 minutes and the accuracy is about 90%, indicating performance that is not sufficient to be implemented in the real word. In this study, the detection of symbols in a drawing is performed using 1-stage object detection algorithms that process both region proposal and detection. Specifically, build the training data using the image labeling tool, and show the results of recognizing the symbol in the drawing which are trained in the deep learning model.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2461-2469
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• 1997

Target detection and recognition problems, in which neural networks are widely used, require translation invariant and real-time processing in addition to the requirements that general pattern recognition problems need. This paper presents a novel architecture that meets the requirements and explains effective methodology to train the network. The proposed neural network is an architectural extension of the shared-weight neural network that is composed of the feature extraction stage followed by the pattern recognition stage. Its feature extraction stage performs correlational operation on the input with a weight kernel, and the entire neural network can be considered a nonlinear correlation filter. Therefore, the output of the proposed neural network is correlational plane with peak values at the location of the target. The architecture of this neural network is suitable for implementing with parallel or distributed computers, and this fact allows the application to the problems which require realtime processing. Net training methodology to overcome the problem caused by unbalance of the number of targets and non-targets is also introduced. To verify the performance, the proposed network is applied to detection and recognition problem of a specific automobile driving around in a parking lot. The results show no false alarms and fast processing enough to track a target that moves as fast as about 190 km per hour.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.125-132
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• 1993

When a mobile robot moves around autonomously without man-made landmarks, it is essential to recognize the placement of surrounding objects especially for current position estimation, obstacle avoidance, or homing into the work station. In this paper, we propose a novel approach to recognize the floor paln for indoor mobile robot navigation using ultrasonic time-of-flight method. We model the floor plan as a collection of polygonal plane objects and recognize the floor plan by locating edges and vertices of the objects. The direction is estimated by the patterns of transmission beam and reception sensitivity of the ultrasonic transducer, and the distance is estimated by the correlation detection method. We show the validity of the proposed approach through experimental results and discuss the resolution and the accuracy of the estimation of direction and distance.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.34-40
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• 2005

In this paper, we describe the modeling for the 3D robot laser scanning system consisting of a laser stripe projector, camera, and 5-DOF robot and propose its calibration method. Nonlinear radial distortion in the camera model is considered for improving the calibration accuracy. The 3D range data is calculated using the optical triangulation principle which uses the geometrical relationship between the camera and the laser stripe plane. For optimal estimation of the system model parameters, real-coded genetic algorithm is applied in the calibration process. Experimental results show that the constructed system is able to measure the 3D position within about 1mm error. The proposed scheme could be applied to the kinematically dissimilar robot system without losing the generality and has a potential for recognition for the unknown environment.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.104-113
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• 1996

In robot system, the robot manipulation needs the information of task and objects to be handled in possessing a variaty of positions and orientations. In the current industrial robot system, determining position and orientation of objects under industrial environments is one of major problems. In order to pick up an object, the roblt needs the information about the position and orientation of object, and between objects and gripper. When sensing is accomplished by pinhole model camera, the mathematical relationship between object points and their images is expressed in terms of perspective, i.e., central projection. In this paper, a new approach to determine the information of the supporting points related to position and orientation of the object using the robot vision system is developed and testified in experimental setup. The result will be useful for the industrial, agricultural, and autonomous robot.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.8
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• pp.678-688
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• 1990

In this paper, pSDF-based referance image is realized. Using BJTC(binary joint transform correlator) as the spatial plane correlator, optical pattern recognition for intraclass indentification is performed. Computer simulation shows that the correlation performance of BJTC is superior to that of JTC. Experimental results using BJTC reveal that correlation peak intensity is constant within the error range from 4.1% to 9.6% in intraclass indentification.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.57-60
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• 1991

A new approach to texture classification for ultrasound liver diagnosis using run difference matrix was developed. The run difference matrix consists of the gray level difference along with distance. From this run difference matrix, we defined several parameters such as LDE, LDEL, NUF, SMO, SMG, SHP etc. and three vectors namely DOD, DGD and DAD. Each parameter value calculated in fatty cirrhotic, chronic hepatitic and normal liver mage was plotted in two dimensional plane. We compared our results with run length method. There are several advantages of run difference matrix method over the run lengths. 1) It is more sensitive to small difference of gray level distribution. 2) The parameters provide more statistically significant value. Images were classified with the extracted parameters to each diseases using neural networks. In preliminary clinical exprements, this approach showed satisfying results.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.6
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• pp.78-87
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• 1994

In this paper, a binary phase extraction joiont transform correlator (BPEJTC)system is proposed as a new phase-type optical correlator in which the correlation errors and DC component are dramtically reduced and optical efficiency and correlation performance are also improved by reconstructing the joint transform power spectrum (JTPS) of the conventional joint transform correlator (JTC)system form which the autocorrelation and crosscorrelation siganls generated in each self-plane are removed, and the binary phase function is extracted from the phase value of the reconstructed JTPS. Through some computer simulation and optical experimental results, the possibility of the implementation of the real-time multi-target tracking and recognition system is also suggested.