• The Journal of the Korea Contents Association
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• v.4 no.3
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• pp.125-130
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• 2004

This paper proposes a method of the car license plate recognition from digital camera image. Lots of technology advancement has been accomplished for the least several years. The key issue for recognition rate improvement has been the extraction of correct area on the plate. In the previous studies, the information from an edge or an color on a plate hasn't been used but some declination also taken into account in most cases due to the difficulty of area extraction on a tilted plate The proposed method focuses on transforming a slant plate image to the normalized form to be recognized. It shows good robustness on situations defined by a variety of locations, slants and heights of the license plate, because it detects the edge of license plate by using both the color information and linear regression method. The computer simulation shows that the proposed method records 92％ detection rates of license plate and can recognize characters of slant plate with about 50 degrees.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.73-78
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• 2005

Images coded by DCT based compression contain several quality degradations by quantization process. Among them contrast distortion is the important one because human eyes are sensitive to contrast. In case of low bit-rate coded image, we can not get an image having good quality due to quantization error. In this paper, we suggest a new scheme to enhance image's contrast in DCT domain. Proposed method enhances only edge regions. Homogeneous regions are not considered in this method. $8{\times}8$ DCT coefficient blocks are decomposed to $4{\times}4$ sub-blocks for detail edge region discrimination. we could apply this scheme to real-time application because proposed scheme is DCT based method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1711-1717
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• 2017

In this paper, we propose a fast and accurate method of extracting lens array lattice in integral imaging by using an appropriate calibration pattern image and fast median filtering. In order to extract the lattice of a lens array, vertical and horizontal edge images are required. To extract edge images, the well-known previous method used separable median filters. However, this method is slow and difficult to determine the median filter size. In order to overcome this problem, we try to improve speed by calculating median value through binary counting method. In addition, we propose a calibration pattern image that detects edges well and improves the accuracy. Experimental results indicate that the proposed method is superior to the existing method in extracting the lattice of a lens array in integral imaging.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.1-13
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• 2009

Scene classification and concept-based procedures have been the great interest for image categorization applications for large database. Knowing the category to which scene belongs, we can filter out uninterested images when we try to search a specific scene category such as beach, mountain, forest and field from database. In this paper, we propose an adaptive segmentation method for real-world natural scene classification based on a semantic modeling. Semantic modeling stands for the classification of sub-regions into semantic concepts such as grass, water and sky. Our adaptive segmentation method utilizes the edge detection to split an image into sub-regions. Frequency of occurrences of these semantic concepts represents the information of the image and classifies it to the scene categories. K-Nearest Neighbor (k-NN) algorithm is also applied as a classifier. The empirical results demonstrate that the proposed adaptive segmentation method outperforms the Vogel and Schiele's method in terms of accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.195-198
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• 2013

Many libraries utilize RFID tags for checking in and out of books. However, the recognition rate of this automatic process may depend on the orientation of antennas and RFID tags. Therefore we need supplemental systems to improve the recognition rate. The proposed algorithm sets up the ROI of the book existing area from the input image and then performs Canny edge detection algorithm to extract edges of books. Finally Hough line transform algorithm allows to detect the number of books from the extracted edges. To evaluate the performance of the proposed method, we applied our method to 350 book images under various circumstances. We then analyzed the performance of proposed method from results using recognition and mismatch ratio. The experimental result gave us 97.1% accuracy in book counting.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.903-909
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• 2018

In this paper, performance analysis of machine vision techniques is presented to improve the convenience and speed of automatic inspection in the industrial field when machine vision is applied to the image not taken in the stationary state, but in the moving state on a conveyer. When the length of cylindrical rods used for automobiles was measured using the edge detection method, the conveying speed increased, and the uncertainty of the boundary between the background and the part image increased, which resulted in a shorter image of the object taken. This paper proposes a linear compensation method to predict the biased errors of the length measurements after examining the pattern of biased and random errors, respectively, with 6 different types of specimens and 7 velocity stages. The length measurement corrected by the linear compensation method had the same accuracy as the stationary state within the speed range of 30 cm/s and could enhance the application capability in automatic inspections.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.63-73
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• 2009

This paper presents a vehicle detection method that uses convolution matching method based on a simple color information. An input image is expressed as 8 oriented color expression(Red, Green, Blue, White, Black, Cyan, Yellow, Magenta) considering an orientation of a pixel color vector. It makes the image very reliable and strong against changes of illumination condition or environment. This paper divides the vehicle detection into a hypothesis generation step and a hypothesis verification step. In the hypothesis generation step, the vehicle candidate region is found by vertical edge and shadow. In the hypothesis verification step, the convolution matching and the complexity of image edge are used to detect real vehicles. It is proved that the proposed method has the fast and high detection rate on various experiments where the illumination source and environment are changed.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.104-113
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• 2000

In this study, we propose facial image recognition based on wavelet transform and neural network. This algorithm is proposed by following processes. First, two gray level images is captured in constant illumination and, after removing input image noise using a gaussian filter, differential image is obtained between background and face input image, and this image has a process of erosion and dilation. Second, a mask is made from dilation image and background and facial image is divided by projecting the mask into face input image Then, characteristic area of square shape that consists of eyes, a nose, a mouth, eyebrows and cheeks is detected by searching the edge of divided face image. Finally, after characteristic vectors are extracted from performing discrete wavelet transform(DWT) of this characteristic area and is normalized, normalized vectors become neural network input vectors. And recognition processing is performed based on neural network learning. Simulation results show recognition rate of 100 % about learned image and 92% about unlearned image.

• Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.286-294
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• 2016

In this paper we present an efficient technique for sketch-based 3D modeling using automatically extracted image features. Creating a 3D model often requires a drawing of irregular shapes composed of curved lines as a starting point but it is difficult to hand-draw such lines without introducing awkward bumps and edges along the lines. We propose an automatic alignment of a user's hand-drawn sketch lines to the contour lines of an image, facilitating a considerable level of ease with which the user can carelessly continue sketching while the system intelligently snaps the sketch lines to a background image contour, no longer requiring the strenuous effort and stress of trying to make a perfect line during the modeling task. This interactive technique seamlessly combines the efficiency and perception of the human user with the accuracy of computational power, applied to the domain of 3D modeling where the utmost precision of on-screen drawing has been one of the hurdles of the task hitherto considered a job requiring a highly skilled and careful manipulation by the user. We provide several examples to demonstrate the accuracy and efficiency of the method with which complex shapes were achieved easily and quickly in the interactive outline drawing task.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.283-286
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• 2001

In this paper, we present a robust region segmentation method based on the watershed transformation of a pseudo-distance map (PDM). A usual approach for the segmentation of a gray-scale image with the watershed algorithm is to apply it to a gradient magnitude image or the Euclidean distance map (EDM) of an edge image. However, it is well known that this approach suffers from the oversegmentation of the given image due to noisy gradients or spurious edges caused by a thresholding operation. In this paper we show thor applying the watershed algorithm to the EDM, which is a regularized version of the EDM and is directly computed form the edgestrength function (ESF) of the input image, significantly reduces the oversegmentation, and the final segmentation results obtained by a simple region-merging process are more reliable and less noisy than those of the gradient-or EDM-based methods. We also propose a simple and efficient region-merging criterion considering both boundary strengths and inner intensities of regions to be merged. The robustness of our method is proven by testing it with a variety of synthetic and real images.