• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.133-140
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• 2005

This paper presents a rapid face shape acquisition system. The system is composed of two cameras and one projector. The technique works by projecting a pattern on the object and capturing two images with two cameras. We use a 'one shot' system which provides 3D data acquired by single image per camera. The system is good for rapid data acquisition as our purpose. We use the 'absolutely coded pattern' using the hue and saturation of pattern lines. In this 'absolutely coded pattern' all patterns have absolute identification numbers. We solve the correspondence problem between the two images by using epipolar geometry and absolute identification numbers. In comparison to the 'relatively coded pattern' which uses relative identification numbers, the 'absolutely coded pattern' helps obtain rapid 3D data by one to one point matching on an epipolar line. Because we use two cameras, we obtain two images which have similar hue and saturation. This enables us to have the same absolute identification numbers in both images, and we can use the absolutely coded pattern for solving the correspondence problem. The proposed technique is applied to face data and the total time for shape acquisition is estimated.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.546-550
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• 2010

In this paper, we propose a method for the postprocessing of cursive script recognition in Wine Label Images. The proposed method mainly consists of three steps: combination matrix generation, character combination filtering, string matching. Firstly, the combination matrix generation step detects all possible combinations from a recognition result for each of the pieces. Secondly, the unnecessary information in the combination matrix is removed by comparing with bigram of word in the lexicon. Finally, string matching step decides the identity of result as a best matched word in the lexicon based on the levenshtein distance. An experimental result shows that the recognition accuracy is 85.8%.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.39-46
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• 2015

This paper presents an automatic method to detect and count the cells from microorganism images based on mobile environments. Cell counting is an important process in the field of biological and pathological image analysis. In the past, cell counting is done manually, which is known as tedious and time consuming process. Moreover, the manual cell counting can lead inconsistent and imprecise results. Therefore, it is necessary to make an automatic method to detect and count cells from biological images to obtain accurate and consistent results. The proposed multi-step cell counting method automatically segments the cells from the image of cultivated microorganism and labels the cells by utilizing topological analysis of the segmented cells. To improve the accuracy of the cell counting, we adopt watershed algorithm in separating agglomerated cells from each other and morphological operation in enhancing the individual cell object from the image. The system is developed by considering the availability in mobile environments. Therefore, the cell images can be obtained by a mobile phone and the processed statistical data of microorganism can be delivered by mobile devices in ubiquitous smart space. From the experiments, by comparing the results between manual and the proposed automatic cell counting we can prove the efficiency of the developed system.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.451-456
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• 2017

Semantic segmentation, which is the most basic and complicated problem in computer vision, classifies each pixel of an image into a specific object and performs a task of specifying a label. MRF and CRF, which have been studied in the past, have been studied as effective methods for improving the accuracy of pixel level labeling. In this paper, we propose a semantic partitioning method that combines CNN, a kind of deep running, which is in the spotlight recently, and CRF, a probabilistic model. For learning and performance verification, Pascal VOC 2012 image database was used and the test was performed using arbitrary images not used for learning. As a result of the study, we showed better partitioning performance than existing semantic partitioning algorithm.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1997.04a
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• pp.79-79
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• 1997

자연계에 존재하는 수은중 유기수은은 생태계 먹이사슬을 통하여 체내의 여러장기에 축적되어 조직손상을 일으키는 것으로 잘 알려져 있다. 그러나 이러한 세포독성에 대한 정확한 생화학적 기전에 대해서는 자세히 알려진 바가 없다. 포스포리파아제 $A_2$(PLA$_2$)는 세포막의 인지질로부터 Arachidonic acid (AA)와 Lysophospholipid를 유리시키는 효소로 최근 세포손상과 관련하여 그 역할이 주목되고 있으며, 극히 최근, 일차배양 소뇌신경세포를 이용한 연구에서 메칠수은처리에 의해 세포독성의 지표인 Lactate dehydrogenase (LDH)의 유리와 함께 AA 유리가 증가되는 것이 관찰되었으나 여러형태의 PLA$_2$중 어느형태의 효소가 관련되어 있는지, 또한, 그 자세한 기전에 대해서는 불분명한 점이 많다. 본 연구에서는 신장세포의 일종인 MDCK세포를 이용하여 메칠수은의 처리에 의한 PLA$_2$의 활성화 및 그 생화학적인 기전을 구명하고자 하였다. [$^3$H]AA를 MDCK세포의 배양액에 첨가하여 라벨링한 후 메칠수은을 처리하였을때 [$^3$H]AA가 대조군에 비해 농도의존적 및 경시적으로 현저하게 증가하였으며 동시에 LDH의 유리도 함께 관찰되었다. 이러한 [$^3$H]AA의 유리 증가는 세포질 PLA$_2$에 특이적인 저해제로 알려진 AACOCF$_3$의 전처리에 의해 거의 완전히 억제되었으나 LDH의 유리는 오히려 증가하였다. 또한, 글루타치온(GSH)의 전구체인 NAC (N-Acetyl Cysteine)에 의해 [$^3$H]AA의 유리는 부분적으로 감소하였으나, LDH의 유리는 변함이 없었다. 돼지비장이나 MDCK 세포에서 얻어진 세포질 PLA$_2$에 메칠수은을 직접 처리하였을때는 오히려 PLA$_2$의 활성은 감소되었다. 위의 결과들로부터 메칠수은에 의한 [$^3$H]AA의 유리 증가는 세포질 PLA$_2$효소에 대한 직접적인 작용이 아니라 세포내 -SH기의 차단이나 Oxidative Stress에 의해 간접적으로 활성화되는 것으로 예상되며, 세포질 PLA$_2$에 의해 유리된 AA의 세포독설과 관련된 세포내의 역할에 대해 의문이 제기되었다.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.1-13
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• 2017

In This Paper, we find the Target Candidate Region and the Location of the Candidate Region by Performing the Morphological Difference Calculation and Pixel Labeling for Robust Small Target Detection in Infrared Image with low SNR. Conventional Target Detection Methods based on Morphology Algorithms are low in Detection Accuracy due to their Vulnerability to Clutter in Infrared Images. To Address the Problem, Target Signal Enhancement and Background Clutter Suppression are Achieved Simultaneously by Combining Moravec Algorithm and LCM (Local Contrast Measure) Algorithm to Classify the Target and Noise in the Candidate Region. In Addition, the Proposed Algorithm can Efficiently Detect Multiple Targets by Solving the Problem of Limited Detection of a Single Target in the Target Detection method using the Morphology Operation and the Gaussian Distance Function Which were Developed for Real time Target Detection.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.84-91
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• 2006

In this paper, we present a new segmentation algorithm for color images based on mathematical morphology and a Gaussian mixture model(GMM). We use the morphological operations to determine the number of components in a mixture model and to detect their modes of each mixture component. Next, we have adopted the GMM to represent the probability distribution of color feature vectors and used the deterministic annealing expectation maximization (DAEM) algorithm to estimate the parameters of the GMM that represents the multi-colored objects statistically. Finally, we segment the color image by using posterior probability of each pixel computed from the GMM. The experimental results show that the morphological operation is efficient to determine a number of components and initial modes of each component in the mixture model. And also it shows that the proposed DAEM provides a global optimal solution for the parameter estimation in the mixture model and the natural color images are segmented efficiently by using the GMM with parameters estimated by morphological operations and the DAEM algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9C
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• pp.546-554
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• 2011

Near targets in the infrared (IR) images have the steady feature for inner region and the transient feature for the boundary region. Based on these features, this paper proposes a new method to extract the fine target shape of near targets in the IR images. First, we detect the boundary region of the candidate targets using the local variance weighted information entropy (WIE) of the original images. And then, a coarse target region can be estimated based on the labeling of the boundary region. For the coarse target region, we use the opening filter with an adaptive structure element to extract the fine target shape. The decision of the adaptive structure element size is optimized for the width information of target boundary by calculating the average WIE in the enlarged windows. The experimental results show that a proposed method has better extraction performance than the previous threshold algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.1
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• pp.15-21
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• 2007

We propose a 3D modeling method for surface inspection of non-constant velocity moving object. 1'lie laser lines reflect tile surface curvature. We can acquire 3D surface information by analyzing projected laser lines on object. In this paper, we use multi-line laser to improve the single stripe method and high speed of single frame. Binarization and edge extraction of frame image were proposed for robust laser each line extraction. A new labeling method was used for laser line labeling. We acquired some feature points for image matching from the frame data and juxtaposed the frames data to obtain a 3D shape image. We verified the superiority of proposed method by applying it to inspect container's damages.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.75-81
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• 2012

Road identification is the important task for extracting a road region from the high-resolution satellite images, when the road candidates is extracted by the pre-processing tasks using a binarization, noise removal, and color processing. Therefore, we propose a noble approach for identifying a road using the orientation-selective spatial filters, which is motivated by a computational model of neuron cells found in the primary visual cortex. In our approach, after the neuron cell typed spatial filters is applied to the isolated connected-labeling road candidate regions, proposed method identifies the region of perceiving the strong orientation feature with the real road region. To evaluate the effectiveness of the proposed method, the accuracy&error ratio in the confusion matrix was measured from road candidates including road and non-road class. As a result, the proposed method shows the more than 92% accuracy.