• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.670-673
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• 2002

This paper proposes a digital algorithm for gray-scale/color image segmentation of real-time video signals and a cell-network-based implementation architecture in state-of-the-art CMOS technology. Through extrapolation of design and simulation results we predict that about 300$\times$300 pixels can be integrated on a chip at 100nm CMOS technology, realizing very high-speed segmentation at about 1600sec per color image. Consequently real-time color-video segmentation will become possible in near future.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.505-510
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• 2004

This paper suggests a new CT-image segmentation algorithm. This algorithm uses morphological filters and the watershed algorithms. The proposed CT-image segmentation algorithm consists of six parts: preprocessing, image simplification, feature extraction, decision making, region merging, and postprocessing. By combining spatial and temporal information, we can get more accurate segmentation results. The simulation results illustrate not only the segmentation results of the conventional scheme but also the results of the proposed scheme; this comparison illustrates the efficacy of the proposed technique. Furthermore, we compare the various medical images of the structuring elements. Indeed, to illustrate the improvement of coding efficiency in postprocessing, we use differential chain coding for the shape coding of results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.42-47
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• 2007

Video Segmentation is an essential part in region-based video coding and any other fields of the video processing. Among lots of methods proposed so far, the watershed method in which the region growing is performed for the gradient image can produce well-partitioned regions globally without any influence on local noise and extracts accurate boundaries. But, it generates a great number of small regions, which we call over segmentation problem. Therefore we proposes that adaptive trimmed mean filter for resolving the over segmentation of image. Simulation result, we confirm that proposed ATM filter improves the performance to remove noise and reduces damage for the clear degree of image in case of the noise ratio of 20% and over.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.339-348
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• 1997

In this paper, we propose a coding method to improve compression efficiency for MR image. This can be achieved by combining coding scheme and segmentation scheme which removes noisy background region, which is meaningless for diagnosis in the MR image. In segmentation algoritm, we use full-resolution wavelet transform to extract features of regions in image and Kohonen self-organizing map to classify the features. The subsequent wavelet coder encodes only diagnostically significant foreground regions refering to segmentation map. Our proposed algorithm provides about 15% of bit rate reduction when compared with the same coder which is not combined with segmentation scheme. And the proposed scheme shows better reconstructed image quality than JPEG at the same compression ratio.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.1-8
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• 2005

There are many image segmentation methods having bon published as the results of research so far, however these are for the noise images which can process an image under the general white noise environments. Therefore, these methods has the disadvantages because it is difficult to extract only the accurate parameters, which can distinguish between image and impulsive noise, from image with impulsive noises. So it has a problem about the potential decreasing of the performance according to the impulsive noise for all applications using the present quadtree segmentation. In this paper, we propose new quadtree segmentation using quartile deviation which can extract effectively the image information parameters from a noise image. Therefore our method can apply for various image processing fields because it has a advantage to distinguish an image information from noise image. As the result of simulation, we confirm that the proposed quadtree segmentation is more efficient than the present method when tested on impulsive noise image.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• IE interfaces
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• v.21 no.4
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• pp.394-402
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• 2008

The objective of this paper is to design segmentation algorithm for applying the breast ultrasound image to CAD(Computer Aided Diagnosis). This study is conducted after understanding limits, used algorithm and demands of CAD system by interviewing with a medical doctor and analyzing related works based on a general CAD framework that is consisted of five step-establishment of plan, analysis of needs, design, implementation and test & maintenance. Detection function of CAD is accomplished by Canny algorithm and arithmetic operations for segmentation. In addition to, long computing time is solved by extracting ROI (Region Of Interests) and applying segmentation technical methods based morphology algorithm. Overall course of study is conducted by verification of medical doctor. And validity and verification are satisfied by medical doctor's confirmation. Moreover, manual segmentation of related works, restrictions on the number of tumor and dependency of image resolution etc. was solved. This study is utilized as a support system aided doctors' subjective diagnosis even though a lot of future studies is needed for entire application of CAD system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.11b
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• pp.9-14
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• 1999

The object boundary of an image plays an important role for image interpretation. In this paper, we introduce a concept of hierarchical mesh-based image segmentation for finding object boundaries. In each hierarchical layer, we employ neighborhood searching and boundary tracking methods to refine the initial boundary estimate. We also apply a local region growing method to define closed contours. Experimental results indicate that reliable segmentation of objects can be accomplished by the pro-posed tow complexity technique.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2243-2245
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• 1998

In this paper, we treat image segmentation as a graph partitioning problem. and use the normalized cut for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different graphs as well as the total similarity within the groups. The minimization of this criterion can formulated as a generalized eigenvalues problem. We have applied this approach to segment static image. This criterion can be shown to be computed efficiently by a generalized eigenvalues problem

• International conference on construction engineering and project management
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• 2015.10a
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• pp.333-335
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• 2015

A computer vision-based scene recognition algorithm is proposed for monitoring construction sites. The system analyzes images acquired from a surveillance camera to separate regions and classify them as building, ground, and hole. Mean shift image segmentation algorithm is tested for separating meaningful regions of construction site images. The system would benefit current monitoring practices in that information extracted from images could embrace an environmental context.