• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1153-1158
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• 2006

The classification of the background and cell areas is very important research area because of the ambiguous boundary. In this paper, the region of cell is extracted from an image of uterine cervical cytodiagnosis using the region growing method that increases the region of interest based on similarity between pixels. Segmented image from background and cell areas is binarized using a threshold value. And then 8-directional tracking algorithm for contour lines is applied to extract the cell area. First, the extracted nucleus is transformed to RGB color that is the original image. Second, the K-means clustering algorithm is employed to classify RGB pixels to the R, G, and B channels, respectively. Third, the Hue information of nucleus is extracted from the HSI models that is the transformation of the clustering values in R, G, and B channels. The backpropagation algorithm is employed to classify and identify the normal or abnormal nucleus.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.923-935
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• 2022

This paper deals with detection and classification of leaf diseases for phenomics systems. As the smart farm systems of plants are increased, It is important to determine quickly the abnormal growth of plants without supervisors. This paper considers the color distribution and shape information of leaf diseases, and designs two deep leaning networks in training the leaf diseases. In the first step, color distribution of input image is analyzed for possible diseases. In the second step, the image is first partitioned into small segments using mean shift clustering, and the color information of each segment is inspected by the proposed Color Network. When a segment is determined as disease, the shape parameters of the segment are extracted and inspected by proposed Shape Network to classify the leaf disease types in the third step. According to the experiments with two types of diseases (frogeye/rust and tipburn) for apple leaves and iceberg, the leaf diseases are detected with 92.3% recall for a segment and with 99.3% recall for an input image where there are usually more than two disease segments. The proposed method is useful for detecting leaf diseases quickly in the smart farm environment, and is extendible to various types of new plants and leaf diseases without additional learning.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.151-159
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• 2013

The building information should be up-to-date information and propagated rapidly for urban modeling, terrain analysis, life information, navigational system, and location-based services(LBS), hence the most recent and updated data of the building information have been required of researchers. This paper presents the developed system to extract the 3-dimension spatial information from aerial orthoimage and LiDAR data of HSI color model. In particular, this paper presents the image processing algorithm to extract the outline of specific buildings and generate the building polygon from the image using HIS color model, recursive backtracking algorithm and the search maze algorithm. Also, this paper shows the effectivity of the HIS color model in the image segmentation.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.216-217
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• 2007

• Journal of Broadcast Engineering
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• v.8 no.1
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• pp.91-100
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• 2003

In this paper, to improve the retrieval Performance, an efficient combination of the MPEG-7 visual descriptors, such as the edge histogram descriptor (EHD), the color layout descriptor (CLD), and the homogeneous texture descriptor (HTD), is proposed in the framework of the relevance feedback approach. The EHD represents spatial distribution of edges in local image regions and it is considered as an important feature to represent the content of the image. The CLD specifies spatial distribution of colors and is widely used in image retrieval due to its simplicity and fast operation speed. The HTD describes precise statistical distribution of the image texture. Both the feature vector for the query image and the weighting factors among the combined descriptors are adaptively determined during the relevance feedback. Experimental results show that the proposed method improves the retrieval performance significantly tot natural images.

• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.151-157
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• 2014

Recently UAV(unmanned aerial vehicle) is frequently used not only for military purpose but also for civil purpose. UAV automatically navigates following the coordinates input in advance using GPS information. However it is impossible when GPS cannot be received because of jamming or external interference. In order to solve this problem, we propose a real-time segmentation and classification algorithm for the specific regions from UAV image in this paper. We use the super-pixels algorithm using graph-based image segmentation as a pre-processing stage for the feature extraction. We choose the most ideal model by analyzing various color models and mixture color models. Also, we use support vector machine for classification, which is one of the machine learning algorithms and can use small quantity of training data. 18 color and texture feature vectors are extracted from the UAV image, then 3 classes of regions; river, vinyl house, rice filed are classified in real-time through training and prediction processes.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.435-440
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• 2003

본 논문에서는 실시간 출입통제시스템에 적용이 가긍한 복잡한 배경에서의 다중 얼굴 영역 검출과 추적을 통한 얼굴 인식 알고리즘을 제안하였다. 제안된 알고리즘에서는 배경영상과 입력된 연속적인 프레임간의 차영상을 적용함으로써 물체의 움직임을 감지한 후. IISI컬러 좌표모델을 이용하여 얼굴의 1차 후보 영역을 검출하고, 잡음제거를 위해 모폴로지 연산을 수행하였다 또한 Line Projection을 이용한 객체 분할법(Object Segmentation)으로 객체를 분할함으로써 다중 얼굴 영역을 추출하였다. 또한 추출된 얼굴영역에서 눈 영역 검출을 통해 각각의 얼굴 영역들을 검증하였으며 검증된 얼굴들의 최외각 4개의 좌표를 이용하여 얼굴 추적율을 높였다. 마지막으로 얼굴 인식은 추출된 얼굴 영역으로부터 주성분 분석(PCA ： Principle Component Analysis)방법을 이용함으로써 97～98％의 높은 인식율을 보였다.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.30-38
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• 2006

This paper is used the color space distribution HMMD model presented in MPEG-7 in order to segment and detect the wanted image parts as a real time without the user's manufacturing in the video object segmentation. Here, it is applied the wavelet morphology to remove a small part that is regarded as a noise in image and a part excepting for the face image. Also, it did the optimal composition by the rough set. In this paper, tile proposed video object detection algorithm is confirmed to be superior as detecting the face object exactly than the conventional algorithm by applying those to the different size images.put the of paper here.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.634-638
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• 2018

Haze is a factor that degrades the performance of various image processing algorithms, such as those for detection, tracking, and recognition using an electro-optical sensor. For robust operation of an electro-optical sensor-based unmanned system used outdoors, an algorithm capable of effectively removing haze is needed. As a haze removal method using a single electro-optical sensor, the dark channel prior using statistical properties of the electro-optical sensor is most widely known. Previous methods used a square filter in the process of obtaining a transmission using the dark channel prior. When a square filter is used, the effect of removing haze becomes smaller as the size of the filter becomes larger. When the size of the filter becomes excessively small, over-saturation occurs, and color information in the image is lost. Since the size of the filter greatly affects the performance of the algorithm, a relatively large filter is generally used, or a small filter is used so that no over-saturation occurs, depending on the image. In this paper, we propose an improved haze removal method using color image segmentation. The parameters of the color image segmentation are automatically set according to the information complexity of the image, and the over-saturation phenomenon does not occur by estimating the amount of transmission based on the parameters.

• Journal of Digital Contents Society
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• v.7 no.4
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• pp.287-294
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• 2006

Mass-customization is fast growing a segment of the apparel market. 2D Virtual wearing system is one of visual support tools that make possible to sell apparel before producing and reduce the time and costs related to product development and manufacturing in the world of apparel mass-customization. This paper is related to fabric color mapping method for 2D image-based virtual wearing system. In proposed method, clothing shape section of interest is segmented from a clothes model image using a region growing method, and then mapping a new fabric color selected by user into it based on its intensity difference map is processed. With the proposed method in 2D virtual wearing system, regardless of color or intensity of model clothes, it is possible to virtually change the fabric color with holding the illumination and shading properties of the selected clothing shape section, and also to quickly and easily simulate, compare, and select multiple fabric color combinations for individual styles or entire outfits.