• Proceedings of the IEEK Conference
• /
• 2001.06d
• /
• pp.97-100
• /
• 2001

In this paper, We propose a new semiautomatic segmentation method using spatio-temporal similarity. In the proposed scheme, segmentation is performed using gradual region merging and hi-direction at spatio-temporal refinement. Simulation results show the efficiency of the proposed method in semantic object extraction.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.7
• /
• pp.520-523
• /
• 2009

There are many ways to judge whether the input image is adult-image or not. Until now, adult image detection has been examined by the ratio of skin area in full image. In this paper, we propose a method to extract skin region in YCbCr. Skin region shows unique distribution in YCbCr, and we will separate the skin region from background using the distribution. First, we are going to find Eye zone using Eye-Map. Then we will find out the color value for the distribution of skin region using the color of Eye zone. Next, we will find the distribution of the area through the skin region in full-image.

• Journal of Biomedical Engineering Research
• /
• v.25 no.4
• /
• pp.235-242
• /
• 2004

In this paper, we proposed the algorithm for detection of microtalcification by automatic threshold decision based on region growing method. The region for optimal threshold is grown from local maximum pixel by increasing repeatedly threshold in microralcification candidate region. Then, the optimal threshold is automatically decided at the maximum value of the contrast and edge sharpness in this region. Microcalcifications could be efficiently detected as satisfied result that true positive ratio is 81.5% and average false positive numbers are 1.1 about total 299 microcalcifirations in real image. In a result, we showed that this algorithm can be used to aid diagnostic-radiologist for the diagnosis of the early phase of breast cancer.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.9
• /
• pp.887-893
• /
• 2012

In this paper, we propose a new approach to extract directional features based on directional patterns of each region in fingerprint images. The proposed approach computes the center of gravity to extract features from fingerprint images of various shapes. According to it, we divide a fingerprint image into four regions and compute the directional values of each region. To extract directional features of each region from a fingerprint image, we spilt direction values of ridges in a region into 18 classes and compute frequency distribution of each region. Through the result of our experiment using FVC2002 DB database acquired by electronic devices, we show that directional features are effectively extracted from various fingerprint images of exceptional inputs which lost all or part of singularities. To verify the performance of the proposed approach, we explained the process to model Arch, Left, Right and Whorl class using the extracted directional features of four regions and analyzed the classification result.

• Journal of Cadastre & Land InformatiX
• /
• v.49 no.1
• /
• pp.103-112
• /
• 2019

Extraction of region of interest for satellite image classification is one of the important techniques for efficient management of the national land space. However, recent studies on satellite image classification often depend on the information of the selected image in selecting the region of interest. This study propose an effective method of selecting the area of interest using the continuous digital topographic map constructed from high resolution images. The spatial information used in this research is based on the digital topographic map from 2013 to 2017 provided by the National Geographical Information Institute and the 2015 Sejong City land cover map provided by the Ministry of Environment. To verify the accuracy of the extracted area of interest, KOMPSAT-3A satellite images were used which taken on October 28, 2018 and July 7, 2018. The baseline samples for 2015 were extracted using the unchanged area of the continuous digital topographic map for 2013-2015 and the land cover map for 2015, and also extracted the baseline samples in 2018 using the unchanged area of the continuous digital topographic map for 2015-2017 and the land cover map for 2015. The redundant areas that occurred when merging continuous digital topographic maps and land cover maps were removed to prevent confusion of data. Finally, the checkpoints are generated within the region of interest, and the accuracy of the region of interest extracted from the K3A satellite images and the error matrix in 2015 and 2018 is shown, and the accuracy is approximately 93% and 72%, respectively. The accuracy of the region of interest can be used as a region of interest, and the misclassified region can be used as a reference for change detection.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.16 no.4
• /
• pp.246-253
• /
• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

• Journal of Korea Multimedia Society
• /
• v.12 no.7
• /
• pp.941-946
• /
• 2009

As supplying of automatic surveillance or patrol systems based on image processing, the needs on object extraction technology from images increases. The extraction is more difficult when the lighting condition is changed from time to time. There are many approaches to extract objects from images excluding shadow. They have a common problem something like loss of object region according with shadow removal. In this paper a restoration method using color information of objects to complement the problem is presented. The usefulness of the method is verified using images taken from different lighting conditions and selected from well-known DB.

• Journal of Intelligence and Information Systems
• /
• v.8 no.1
• /
• pp.27-39
• /
• 2002

A locating text technique capable of locating and extracting text blocks in various Web images is presented here. Until now this area of work has been ignored by researchers even if this sort of text may be meaningful for internet users. The algorithms associated with the technique work without prior knowledge of the text orientation, size or font. In the work presented in this research, our text extraction algorithm utilizes useful edge detection followed by histogram analysis on the genuine characteristics of letters defined by text clustering region, to properly perform extraction of the text region that does not depend on font styles and sizes. By a number of experiments we have showed impressively acceptable results.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.28 no.2
• /
• pp.53-62
• /
• 1986

An Idea of Standard Rural Area(SRA), the rural areas which have higher ruralities of the rice cropping region and also higher urban characteristics, was conceptualized to develop the tentative basic indices necessary for rural settlement design in reclaimed land. The SRA's were determined by a technique of the principal component analysis with relevant data from 81 counties or cities located in the west side of Korea(Chon-Nam,Chon-Buk, Chung-Nam, Kyung-Ki Do).By the definition of the SRA, the principal component analysis is seperately carried out by two subworks, analyses of rurality and urban characteristics. From the analysis, rurality of the SRA is characterized by four components which appears to describe the scale of farm management, intensive farming, soundness of farming and farming basis on rice cropping, while urban characteristics of the SRA by three components to describe the accessibility, keeping ratio of infrastructures and level of medical services. Through grouping and synthesizing two characteristics of all counties by each component score, 24 counties were classified as urban-rural harmonized region which is the same result as that obtained from the extraction index being more than 50% of available area to total area except 1 county. Therefore, SRA is defined as the group of counties having more than 50% of available area to total area.

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.610-614
• /
• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.