• Smart Media Journal
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• v.9 no.4
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• pp.134-143
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• 2020

Vehicle License Plate Recognition is one of the approaches for transportation and traffic safety networks, such as traffic control, speed limit enforcement and runaway vehicle tracking. Although it has been studied for decades, it is attracting more and more attention due to the recent development of deep learning and improved performance. Also, it is largely divided into license plate detection and recognition. In this study, experiments were conducted to improve license plate detection performance by utilizing various object detection methods and WPOD-Net(Warped Planar Object Detection Network) model. The accuracy was improved by selecting the method of detecting the vehicle(s) and then detecting the license plate(s) instead of the conventional method of detecting the license plate using the object detection model. In particular, the final performance was improved through the process of removing noise existing in the image by using the Fast-SRGAN model, one of the Super-Resolution methods. As a result, this experiment showed the performance has improved an average of 4.34% from 92.38% to 96.72% compared to previous studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1234-1239
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• 2019

This paper proposes a method for measuring fine dust concentration through digital processing of images captured by only existing CCTVs without additional equipment. This image processing algorithm consists of noise reduction, edge sharpening, ROI setting, edge strength calculation, and correction through HSV conversion. This algorithm is implemented using the C ++ OpenCV library. The algorithm was applied to CCTV images captured over a month. The edge strength values calculated for the ROI region are found to be closely related to the fine dust concentration data. To infer the correlation between the two types fo data, a trend line in the form of a power equation is established using MATLAB. The number of data points deviating from the trend line accounts for around 12.5%. Therefore, the overall accuracy is about 87.5%.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.93-101
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• 2001

In this paper, we propose an image downscaler used in multimedia video applications, such as DTV, TV-PIP, PC-video, camcorder, videophone and so on. The proposed image downscaler provides a scaled image of high-quality and high-performance. This paper will explain the scaling theory using two-dimensional digital filters. It is the method that removes an aliasing noise and decreases the hardware complexity, compared with Pixel-drop and Upsamling. Also, this paper will prove it improves scaling precisians and decreases the loss of data, compared with the Scaler32, the Bt829 of Brooktree, and the SAA7114H of Philips. The proposed downscaler consists of the following four blocks: line memory, vertical scaler, horizontal scaler, and FIFO memory. In order to reduce the hardware complexity, the using digital filters are implemented by the multiplexer-adder type scheme and their all the coefficients can be simply implemented by using shifters and adders. It also decreases the loss of high frequency data because it provides the wider BW of 6MHz as adding the compensation filter. The proposed downscaler is modeled by using the Verilog-HDL and the model is verified by using the Cadence simulator. After the verification is done, the model is synthesized into gates by using the Synopsys. The synthesized downscaler is Placed and routed by the Mentor with the IDEC-C632 0.65${\mu}{\textrm}{m}$ library for further IC implementation. The IC master is fixed in size by 4,500${\mu}{\textrm}{m}$$\times$4,500${\mu}{\textrm}{m}$. The active layout size of the proposed downscaler is 2,528${\mu}{\textrm}{m}$$\times$3,237${\mu}{\textrm}{m}$.

• Korean Journal of Clinical Laboratory Science
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• v.52 no.1
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• pp.1-17
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• 2020

Three-dimensional microscopic approaches in histopathology display multiplex properties that present puzzling questions for specimens as related to their comprehensive volumetric information. This information includes spatial distribution of molecules, three-dimensional co-localization, structural formation and whole data set that cannot be determined by two-dimensional section slides due to the inevitable loss of spatial information. Advancement of optical instruments such as two-photon microscopy and high performance objectives with motorized correction collars have narrowed the gap between optical theories and the actual reality of deep tissue imaging. However, the benefits gained by a prolonged working distance, two-photon laser and optimized beam alignment are inevitably diminished because of the light scattering phenomenon that is deeply related to the refractive index mismatch between each cellular component and the surrounding medium. From the first approaches with simple crude refractive index matching techniques to the recent cutting-edge integrated tissue clearing methods, an achievement of transparency without morphological denaturation and eradication of natural and fixation-induced nonspecific autofluorescence out of real signal are key factors to determine the perfection of tissue clearing and the immunofluorescent staining for high contrast images. When performing integrated laboratory workflow of tissue for processing frozen and formalin-fixed tissues, clear lipid-exchanged acrylamide-hybridized rigid imaging/immunostaining/in situ hybridization-compatible tissue hydrogel (CLARITY), an equipment-based tissue clearing method, is compatible with routine procedures in a histopathology laboratory.

• 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.55-62
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• 2005

In this paper, we propose the face recognition system using HNMA(Hippocampal Neuron Modeling Algorithm) which can remodel the cerebral cortex and hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature-vector of the face images very fast and construct the optimized feature each image. The system is composed of two parts. One is feature-extraction and the other is teaming and recognition. In the feature extraction part, it can construct good-classified features applying PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) in order. In the learning part, it cm table the features of the image data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in the dentate gyrus region and remove the noise through the associate memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are face changes, pose changes and low quality image. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.