• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.87-98
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• 2011

Detecting a moving object from videos and tracking it are basic and necessary preprocessing steps in many video systems like object recognition, context aware, and intelligent visual surveillance. In this paper, we propose a method that is able to detect a moving object quickly and accurately in a condition that background and light change in a real time. Furthermore, our system detects strongly an object in a condition that the target object is covered with other objects. For effective detection, effective Eigen-space and FCM are combined and employed, and a CONDENSATION algorithm is used to trace a detected object strongly. First, training data collected from a background image are linear-transformed using Principal Component Analysis (PCA). Second, an Eigen-background is organized from selected principal components having excellent discrimination ability on an object and a background. Next, an object is detected with FCM that uses a convolution result of the Eigen-vector of previous steps and the input image. Finally, an object is tracked by using coordinates of an detected object as an input value of condensation algorithm. Images including various moving objects in a same time are collected and used as training data to realize our system that is able to be adapted to change of light and background in a fixed camera. The result of test shows that the proposed method detects an object strongly in a condition having a change of light and a background, and partial movement of an object.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.312-321
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• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.

• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.208-217
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• 2020

The classification of plant diseases by images captured by a camera sensor has been studied over past decades. A method that has gained much interest is to use image segmentation, from which statistical features are derived and analyzed by machine learning. Recently, deep learning has been adopted in this area. However, image segmentation is still a difficult task to achieve stable performance due to a variety of environmental variations. The end-to-end learning in neural network has a demerit that train images may be different from real images acquired in outdoor fields. To solve these problems, we propose superpixel-based disease classification method using end-to-end CNN (convolutional neural network) learning. Based on experiments performed on PlantVillage apple images, the classification accuracy is 98.29% and 92.43% for full-image and superpixel. As well, the multivariate F1-score is (0.98, 0.93). Therefore we validate that the method of using superpixel is comparable to that of full-image.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.4
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• pp.7-14
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• 1999

The measuring systems of auto-parts are most of greater part very expensive. This paper tries to study to make a low-cost measuring equipment there's several kinds of parts in automobile. In this study, we take aircon-switch called magnet coil-housing as the object of measurements. The measurements of this product is currently in difficult situations at factory. In the case of the mesuring objects being big sizes and camera sensor having under 410000 pixels, the key point is the number of pixels not to be changed whenever the same object is measured under the same position. We modified and used fuzzy c-means algorithm to get mostly without the change of the numbers of pixels exactly. Also, the standardized ruler is necessary to measure the length of the object but it is not easy to get the precised ruler. Therefore, the standard length has been taken as the mean value of the pixels in the previous passed objects manually obtained at factory. The results are displayed on monitor and transferred these signals to the microprocessor through RSC-232 port to determine a good or bad of products.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.12a
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• pp.315-323
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• 1999

The measuring systems of auto-parts are most of greater part very expensive. This paper tries to study to make a low-cost measuring equipment. There's several kinds of parts in automobile. In this study, we take aircon-switch called magnet coil-housing as the object of measurements. The measurements of this product is currently in difficult situations at factory. In the case of the mesuring objects being big sizes and camera sensor having under 410000 pixels, the key point is the number of pixels not to be changed whenever the same object is measured under the same position. We modified and used fuzzy c-means algorithm to get mostly without the change of the numbers of pixels exactly. Also, the standardized ruler is necessary to measure the length of the object but it is not easy to get the precised ruler. Therefore, the standard length has been taken as the mean value of the pixels in the previous passed objects manually obtained at factory. The results are displayed on monitor and transferred these signals to the microprocessor through RSC-232 port to determine a good or bad of products.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.2
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• pp.148-156
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• 2007

The fiber dispersion in fiber-reinferced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion in the composite PVA-ECC (polyvinyl alcohol-engineered cementitious composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, a new evaluation method is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a charged couple device (CCD) camera through a microscope, the fiber dispersion is evaluated using an image processing technique and statistical tools. In this image processing technique, the fibers are more accurately detected by employing an enhanced algorithm developed based on a discriminant method and watershed segmentation. The influence of fiber orientation on the fiber dispersion evaluation was also investigated via shape analyses of fiber images.

• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.323-335
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• 2012

In this paper, we propose an AR(augmented reality) system using user's bare hand based on computer vision. It is important for registering a virtual object on the real input image to detect and track correct feature points. The AR systems with markers are stable but they can not register the virtual object on an acquired image when the marker goes out of a range of the camera. There is a tendency to give users inconvenient environment which is limited to control a virtual object. On the other hand, our system detects fingertips as fiducial features using adaptive ellipse fitting method considering the geometric characteristics of hand. It registers the virtual object stably by getting movement of fingertips with determining the shortest distance from a palm center. We verified that the accuracy of fingertip detection over 82.0% and fingertip ordering and tracking have just 1.8% and 2.0% errors for each step. We proved that this system can replace the marker system by tacking a camera projection matrix effectively in the view of stable augmentation of virtual object.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.711-720
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• 2012

In this paper, we introduce the implementation of driver fatigue monitering system and its result. Input video device is selected commercially available web-cam camera. Haar transform is used to face detection and adopted illumination normalization is used for arbitrary illumination conditions. Facial image through illumination normalization is extracted using Haar face features easily. Eye candidate area through illumination normalization can be reduced by anthropometric measurement and eye detection is performed by PCA and Circle Mask mixture model. This methods achieve robust eye detection on arbitrary illumination changing conditions. Drowsiness state is determined by the level on illumination normalize eye images by a simple calculation. Our system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. Our algorithm is implemented with low computation complexity and high recognition rate. We achieve 97% of correct detection rate through in-car environment experiments.

• Journal of IKEEE
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• v.14 no.3
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• pp.224-231
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• 2010

Automatic detection of objects in images has been one of core challenges in the areas such as computer vision and pattern analysis. Especially, with the recent deployment of personal mobile devices such as smart phone, such technology is required to be transported to them. Usually, these smart phone users are equipped with devices such as camera, GPS, and gyroscope and provide various services through user-friendly interface. However, the smart phones fail to give excellent performance due to limited system resources. In this paper, we propose a new scheme to improve object recognition performance based on pre-computation and simple local features. In the pre-processing, we first find several representative parts from similar type objects and classify them. In addition, we extract features from each classified part and train them using regression functions. For a given query image, we first find candidate representative parts and compare them with trained information to recognize objects. Through experiments, we have shown that our proposed scheme can achieve resonable performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.684-691
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• 2001

In this paper, we design and implement a system which recognizes and tracks a human head on a sequence of images. In this paper, the color of the skin and ellipse modeling is used as feature vectors to recognize the human head. And the modified time-varying edge detection method and the vertical projection method is used to acquire regions of the motion from images with very complex backgrounds. To select the head from the acquired candidate regions, the process for thresholding on the basis of the I-component of YIQ color information and mapping with ellipse modeling is used. The designed system shows an excellent performance in the cases of the rotated heads, occluded heads, and tilted heads as well as in the case of the normal up-right heads. And in this paper, the combinational technique of motion-based tracking and recognition-based tracking is used to track the human head exactly even though the human head moves fast.