• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4475-4481
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• 2014

A histogram equalization method have been used traditionally for the image enhancement of low quality images. This uses the transformation function, which is a cumulative density function of an input image, and it has mathematically maximum entropy. This method, however, may yield whitening artifacts. This paper proposes the weighted histogram equalization method based on histogram equalization. It has Weber-Fechner's law for a human's vision characteristics, and a dynamic range modification to solve the problem of some methods, which yield a transformation function, regardless of the input image. Finally, the proposed transformation function was calculated using the weighted average of Weber-Fechner and the histogram equalization transformation functions in a modified dynamic range. The simulation results showed that the proposed algorithm effectively enhances the contrast in terms of the subjective quality. In addition, the proposed method has similar or higher entropy than the other conventional approaches.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.161-172
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• 2021

Neuromorphic architecture is drawing attention as a next-generation computing that supports artificial intelligence technology with low energy. However, FPGA embedded boards based on Neuromorphic architecturehave limited resources due to size and power. In this paper, we compared and evaluated the image reduction method using the interpolation method that rescales the size without considering the feature points and the DCT (Discrete Cosine Transform) method that preserves the feature points as much as possible based on energy. The scaled images were compared and analyzed for accuracy through CNN (Convolutional Neural Networks) in a PC environment and in the Nengo framework of an FPGA embedded board.. As a result of the experiment, DCT based classification showed about 1.9% higher performance than that of interpolation representation in both CNN and FPGA nengo environments. Based on the experimental results, when the DCT method is used in a limited resource environment such as an embedded board, a lot of resources are allocated to the expression of neurons used for classification, and the recognition rate is expected to increase.

• Smart Media Journal
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• v.10 no.1
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• pp.32-38
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• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.6
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• pp.3696-3705
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• 2014

This study examined the nurse images perceived by nursing students. The study was specifically meant to determine what images grade 2 and 3 nursing students had about nurses before and after their clinical practice to define the nurse's image. The selected nursing students were interviewed to obtain their opinions on the definition and necessity of a nurse and what a great nurse should be like as well as their prejudice about nurses, and content analysis was carried out to categorize their statements. As a result, 48 significant statements and 14 categories were selected. The findings of the study might not be generalizable because the subjects in this study were selected by convenience sampling from two different nursing departments located in Gangwon Province and Gyeonggi Province. More concrete and reliable results are expected if more students from more geographic regions are investigated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1507-1518
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• 2020

High-quality images have a lot of information, so sensitive data is stored by encryption for private company, military etc. Encrypted images can only be decrypted with a secret key, but the original data cannot be retained when attacked by the Shear attack and Noise pollution attack techniques that overwrite some pixel data with arbitrary values. Important data is the more necessary a countermeasure for the recovery method against attack. In this paper, we propose a random number generator PingPong256 and a shuffling method that rearranges pixels to resist Shear attack and Noise pollution attack techniques so that image and video encryption can be performed more quickly. Next, the proposed PingPong256 was examined with SP800-22, tested for immunity to various noises, and verified whether the image to which the shuffling method was applied satisfies the Anti-shear attack and the Anti-noise pollution attack.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.538-547
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• 2022

Recently introduced image classification methods using Transformers show remarkable performance improvements over conventional neural network-based methods. In order to effectively consider regional features, research has been actively conducted on how to apply transformers by dividing image areas into multiple window areas, but learning of inter-window relationships is still insufficient. In this paper, to overcome this problem, we propose a transformer structure that can reflect the relationship between windows in learning. The proposed method computes the importance of each window region through compression and a fully connected layer based on self-attention operations for each window region. The calculated importance is scaled to each window area as a learned weight of the relationship between the window areas to re-calibrate the feature value. Experimental results show that the proposed method can effectively improve the performance of existing transformer-based methods.

• Journal of the Korean Home Economics Association
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• v.38 no.12
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• pp.257-269
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• 2000

예술작품은 의상디자인에 영감 혹은 영향을 줌으로써 상업적 의상으로 재생산된다. 오늘날까지도 60년대의 많은 팝 아티스트 작품들이 그대로 T셔츠 등에 프린트되는 것을 쉽게 볼 수 있다. 이러한 직접적 영향에는 자주 맹목적 표절이라는 논란을 불러 일으켰으나 긍정적이든 부정적이든 예술작품과 의상 디자인은 20세기초부터 밀접한 관계를 가져왔다. Sonia Delaunay는 예술을 대중과 결합시키는 가장 좋은 방법은 의상을 통해서라고 생각하였다. 그녀는 "만일 예술작품을 생활 속에 들어가게 하려면 그건 여성들 자신이 입고 다니는 방법뿐이다". 라고 말하였다. 결국 이러한 예술의 대중화에 대한 이론은 60년대에 와서 팝 아트 패션의 출현으로 그 결실을 보게 된다. 상류층을 대상으로 한 의상이 대중화되는 과정에서 60년대 경제호황으로 인한 젊은이들의 소비자층 형성과 미술양식에서의 팝아트의 출현은 자연스러운 시대적 조류로 나타났다. 이러한 상황은 팝 아트가 이 시대의 미술 양식에 혁신적일 뿐 아니라, 사회 전반에 팝 아트의 특성(소비 문화적, 대중 문화적, 재현적, 통속적, 기계적, 획일적)을 유행시키고, 대중에게 순수 예술과 복식에 참여 할 수 있는 기회를 부여했다고 볼 수 있다. 따라서 본 연구에서는 가장 혁명적이고도 대중적이라고 할 수 있는 팝 아트 이미지의 작품 제작과 분석을 통하여 현재 논의되고 있는 전시회나 패션쇼에서만 볼 수 있다는 다소 아방가르드 적인 의상 작품들의 대중화 방안에 대한 해결책을 모색하고자 하였다. 실제 의상 디자인 창작에 초점을 맞추었으며, 제작을 위해서 팝 아트에서 주요 소재로 삼았고 대중적 이미지의 심볼이라고도 할 수 있는 Coca Cola label을 표현 모티브로 삼아 개성적이고도 독창적인 의상 디자인을 한 후 분석하였다. 또한 독특한 의상 표현의 개발을 위하여 표현 기법으로는 현대 미술에서 새로운 재료와 여러 가지 재료를 화면에 도입시키는 표현 방법으로서 사용된 콜라주 기법을 사용하였다. 본 연구를 통하여 의상 창작에 있어 조형예술과 연결하여 대중적인 이미지를 도입함으로써 착용자가 예술에 대한 친근하고 익숙한 느낌을 갖게 하며, 예술과 상품 그 자체에 대한 상업적 홍보 목적으로도 사용할 수 있으며, 대중적인 이미지를 표현함에 있어 콜라주 기법은 염색 기법을 사용하지 않고서도 작가가 원하는 표현 효과를 낼 수 있다는 측면을 발견할 수 있었다. 즉 사용된 대중적 상표 이미지는 주인에서 흔히 볼 수 있는 현대 도시의 인공적 환경들로, 의상을 독특하고 개성 있게 표현할 수 있는 모티브의 역할을 하면서 또한 그 예가 무한하여 다양한 디자인 창출의 가능성을 갖고 있으며, 의상을 통해 예술과 대중을 융합시켰다는 예술의 대중화, 민주화라는 중요한 역할을 하였다. 전시회나 패션쇼에서 만 볼 수 있는 예술적 성격을 띠는 아방가르드 작품의 대중 확산 방법으로 제시될 수 있는 이상적인 방법으로는 예술성이 짙은 도저히 입을 수 없다고 생각되어지는 아방가르드한 의상을 일반 대중 브랜드들이 단순한 모방이 아닌 새로운 패러디 작업으로 일반화시켜 상업성을 띤 의상으로 재조정되어 여성들의 몸에 걸치게 하는 것이다. 이와 같은 순환으로써, 조형예술 작품은 의상 디자인 참작에 영향, 영감을 주면서 여러 번의 형태 변화를 거치는 패러디를 통해 각 계층의 누구나가 좋아하고 접할 수 있는 또 다른 창조를 맞아 대중의 손까지 갈 수 있는 것이다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1054-1062
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• 2022

The population of Korea's solar salt-producing regions is rapidly aging, resulting in a decrease in the number of productive workers. In solar salt production, salt collection is the most labor-intensive operation because existing salt collection vehicles require human operators. Therefore, we intend to develop an unmanned solar salt collection vehicle to reduce manpower requirements. The unmanned solar salt collection vehicle is designed to identify the salt collection status and location in the salt plate via color detection, the color detection performance is a crucial consideration. Therefore, an image processing algorithm was developed to improve color detection performance. The algorithm generates an around-view image by using resizing, rotation, and perspective transformation of the input image, set the RoI to transform only the corresponding area to the HSV color model, and detects the color area through an AND operation. The detected color area was expanded and noise removed using morphological operations, and the area of the detection region was calculated using contour and image moment. The calculated area is compared with the set area to determine the location case of the collection vehicle within the salt plate. The performance was evaluated by comparing the calculated area of the final detected color to which the algorithm was applied and the area of the detected color in each step of the algorithm. It was confirmed that the color detection performance is improved by at least 25-99% for salt detection, at least 44-68% for red color, and an average of 7% for blue and an average of 15% for green. The proposed approach is well-suited to the operation of unmanned solar salt collection vehicles.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.33-44
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• 2010

This paper propose the structure of real-time face detection hardware architecture for robot vision processing applications. The proposed architecture is robust against illumination changes and operates at no less than 60 frames per second. It uses Modified Census Transform to obtain face characteristics robust against illumination changes. And the AdaBoost algorithm is adopted to learn and generate the characteristics of the face data, and finally detected the face using this data. This paper describes the face detection hardware structure composed of Memory Interface, Image Scaler, MCT Generator, Candidate Detector, Confidence Comparator, Position Resizer, Data Grouper, and Detected Result Display, and verification Result of Hardware Implementation with using Virtex5 LX330 FPGA of Xilinx. Verification result with using the images from a camera showed that maximum 32 faces per one frame can be detected at the speed of maximum 149 frame per second.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.205-211
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• 2009

The Weightless Neural Network(WNN) has the advantage of the processing speed, less computability than weighted neural network which readjusts the weight. Especially, The behavior information such as sequential gesture has many serial correlation. So, It is required the high computability and processing time to recognize. To solve these problem, Many algorithms used that added preprocessing and hardware interface device to reduce the computability and speed. In this paper, we proposed the Ram based Sequential Cumulative Neural Network(SCNN) model which is sign language recognition system without preprocessing and hardware interface. We experimented with using compound words in continuous korean sign language which was input binary image with edge detection from camera. The recognition system of sign language without preprocessing got 93% recognition rate.