• Journal of the Korean Institute of Telematics and Electronics
• /
• v.24 no.1
• /
• pp.60-66
• /
• 1987

This paper proposes an automatic image segmentation system for machine vision. In this an algorithm using the topological property on the multidimensional feature space for thresholding each primary segment in the image without prior information is presented. Also an effective filter for the removal of regional noises in a code valued image which are artifacts of the thresholding is presented. This method also may be applied for image enhancement or classification, which we show the possibility and the efficiency through computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.11
• /
• pp.2737-2753
• /
• 2013

In this study, we propose a novel salient object detection strategy based on regional contrast and relative spatial compactness. Our algorithm consists of four basic steps. First, we learn color names offline using the probabilistic latent semantic analysis (PLSA) model to find the mapping between basic color names and pixel values. The color names can be used for image segmentation and region description. Second, image pixels are assigned to special color names according to their values, forming different color clusters. The saliency measure for every cluster is evaluated by its spatial compactness relative to other clusters rather than by the intra variance of the cluster alone. Third, every cluster is divided into local regions that are described with color name descriptors. The regional contrast is evaluated by computing the color distance between different regions in the entire image. Last, the final saliency map is constructed by incorporating the color cluster's spatial compactness measure and the corresponding regional contrast. Experiments show that our algorithm outperforms several existing salient object detection methods with higher precision and better recall rates when evaluated using public datasets.

• Journal of the Korean association of regional geographers
• /
• v.22 no.3
• /
• pp.615-627
• /
• 2016

The purpose of this study is to investigate the effect of urban sprawl and their ramifications, i.e. segmentation and hierarchization on the spatial structure as well as populational composition, focusing on Daecheon - Village and Daecheoncheon - Network in Busan, and to examine not only the solidarity between constituents transcending the segmented spaces but also the internal values operating inside through the Daecheoncheon - Network. Due to the large - scale housing development in the 1980s to 1990s, Daecheon - Village has been transformed from a rural village to a town. In this process, the original single space became segmented into Daecheon - Village and apartment complex. This spatial segmentation divided the populational composition into old natives and young immigrants. However, the Daecheoncheon - Network created by solidarity between the bodies of two localities enabled the residents to resolve the urgent issues of localities, recognizing their own space of living not as segmented and hierarchic but as the communal site of life and one village where they will live together. Daecheoncheon-Network was the movement and network to connect natives and immigrants transcending the segmented space and went so far as to make a motive to create one community with the value of 'symbiosis.'

• The KIPS Transactions:PartB
• /
• v.13B no.4 s.107
• /
• pp.401-408
• /
• 2006

In this paper, we propose a new class of segmentation technique for feature extraction based on the statistical and regional classification at each vertical or horizontal line of digital image data. Data is processed and clustered at each line, different from the point or space process. They are designed to segment gray-scale sectional images using a horizontal and vertical line process due to their statistical and property differences, and to extract the feature. The techniques presented here show efficient results in case of the gray level overlap and not having threshold image. Such images are also not easy to be segmented by the global or local threshold methods. Line pixels inform us the sectionable data, and can be set according to cluster quality due to the differences of histogram and statistical data. The total segmentation on line clusters can be obtained by adaptive extension onto the horizontal axis. Each processed region has its own pixel value, resulting in feature extraction. The advantage and effectiveness of the line-cluster approach are both shown theoretically and demonstrated through the region-segmental carotid artery medical image processing.

• Journal of the Korean Society of Radiology
• /
• v.18 no.5
• /
• pp.557-566
• /
• 2024

Ultrasound-guided regional anesthesia is one of the most common techniques used in peripheral nerve blockade by enhancing pain control and recovery time. However, accurate Brachial Plexus (BP) nerve detection and identification remains a challenging task due to the difficulty in data acquisition such as speckle and Doppler artifacts even for experienced anesthesiologists. To mitigate the issue, we introduce a BP nerve small target segmentation network by incorporating BP object detection and U-Net based semantic segmentation into a single deep learning framework based on the multi-scale approach. To this end, the current BP detection and identification was estimated: 1) A RetinaNet model was used to roughly locate the BP nerve region using multi-scale based feature representations, and 2) U-Net was then used by feeding plural BP nerve features for each scale. The experimental results demonstrate that our proposed model produces high quality BP segmentation by increasing the accuracies of the BP nerve identification with the assistance of roughly locating the BP nerve area compared to competing methods such as segmentation-only models.

• Korean Journal of Remote Sensing
• /
• v.27 no.6
• /
• pp.717-728
• /
• 2011

An efficient method for the unsupervised classification of high resolution imagery is suggested in this paper. It employs pixel-linking and merging based on the adjacency graph. The proposed algorithm uses the neighbor lines of 8 directions to include information in spatial proximity. Two approaches are suggested to employ neighbor lines in the linking. One is to compute the dissimilarity measure for the pixel-linking using information from the best lines with the smallest non. The other is to select the best directions for the dissimilarity measure by comparing the non-homogeneity of each line in the same direction of two adjacent pixels. The resultant partition of pixel-linking is segmented and classified by the merging based on the regional and spectral adjacency graphs. This study performed extensive experiments using simulation data and a real high resolution data of IKONOS. The experimental results show that the new approach proposed in this study is quite effective to provide segments of high quality for object-based analysis and proper land-cover map for high resolution imagery of urban area.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.1
• /
• pp.272-287
• /
• 2016

Recently, various and effective contrast based salient object detection models to focus on a single target have been proposed. However, there is a lack of research on detection of multiple objects, and also it is a more challenging task than single target process. In the multiple target problem, we are confronted by new difficulties caused by distinct difference between properties of objects. The characteristic of existing models depending on the global maximum distribution of data point would become a drawback for detection of multiple objects. In this paper, by analyzing limitations of the existing methods, we have devised three main processes to detect multiple salient objects. In the first stage, regional features are extracted from over-segmented regions. In the second stage, the regional features are categorized into homogeneous cluster using the mean-shift algorithm with the kernel function having various sizes. In the final stage, we compute saliency scores of the categorized regions using only spatial features without the contrast features, and then all scores are integrated for the final salient regions. In the experimental results, the scheme achieved superior detection accuracy for the SED2 and MSRA-ASD benchmarks with both a higher precision and better recall than state-of-the-art approaches. Especially, given multiple objects having different properties, our model significantly outperforms all existing models.

• International conference on construction engineering and project management
• /
• 2017.10a
• /
• pp.62-72
• /
• 2017

In this study, liner unit root tests and panel unit root tests to the ratio of city to regional house price were applied to examine the ripple effects across 28 cities in the Yangtze River Delta region. Then invert LM unit root tests with two structural breaks for 10 representative cities were conducted. The results showed that there is overwhelming evidence of the existence of ripple effect in the Yangtze River Delta region, while segmentation is restricted to a small group of cities in which there is no long-run relationship with the Yangtze River Delta region average; compared to no- and one-break case, there is overwhelming evidence of a ripple effect with the LM test with two structural breaks. Furthermore, the results of the Granger causality test showed that changes in house prices in Shanghai, Nanjing and Hangzhou have led to changes in house prices in other cities. The findings of this research make certain contributions to the improvements of research system of ripple effect among regional house prices in the Yangtze River Delta Region,and could be referenced by other markets of other cities.

• Journal of the Institute of Convergence Signal Processing
• /
• v.9 no.4
• /
• pp.329-336
• /
• 2008

The facilities with new technologies in the recent power plants become larger and need a lot of high cost for maintenance due to stop operations and accidents from the operating machines. Therefore, it claims new systems to monitor the operating status and predict the faults of the machines. This research classifies the normal/abnormal status of the machines into 5 levels which are normal-level/abnormal-level/care-level/dangerous-level/fault and develops the new system that predicts faults without stop operation in power plants. We propose the regional segmentation technique in the frequency domain from the data of the operating machines and generate the Peak-codes similar to the Bar-codes, The high efficient and economic operations of the power plants will be achieved by carrying out the pre-maintenance at the care level of 5 levels in the plants. In order to be utilized easily at power plants, we developed the algorithm appling to a notebook computer from signal acquisition to the discrimination.

• Journal of Agricultural Extension & Community Development
• /
• v.23 no.3
• /
• pp.321-334
• /
• 2016

Food is one of the essential elements of the tourist experiences. The study aims to segment food tourists for benefit sought. This study attempted to segment tourists who had experienced local food at tourist destinations by their benefit sought to meet local food. A self-administered survey was obtained from 498 visitors in the study areas. Results from the factor analysis show that the most explained variances of benefit sought were food taste (17.2%) and refresh (12.9%). Five distinct segments were identified based on the benefits; family seeker (17.6%), passive seeker (15.1%), want-it-all seeker (23.1%), raw material seeker (31.8%), gastronomic seeker (12.4%). In addition, a significant difference in the characteristics of tourists who had tasted local food at tourist destinations was observed in terms of occupation, income, education, expenditure for food, and tour distance. Implications are discussed relative to marketing strategies.