• Communications of the Korean Mathematical Society
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• v.31 no.3
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• pp.423-431
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• 2016

Images and inverse images of (almost) stable cubic sets are discussed. We show that the image and inverse image of stable cubic sets are also stable. Conditions for the image of almost cubic sets to be an almost cubic set are provided. The complement, the P-union and the P-intersection of (inverse) images of (almost) stable cubic sets are considered.

• Radiation Oncology Journal
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• v.35 no.2
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• pp.101-111
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• 2017

The number of imaging data sets has significantly increased during radiation treatment after introducing a diverse range of advanced techniques into the field of radiation oncology. As a consequence, there have been many studies proposing meaningful applications of imaging data set use. These applications commonly require a method to align the data sets at a reference. Deformable image registration (DIR) is a process which satisfies this requirement by locally registering image data sets into a reference image set. DIR identifies the spatial correspondence in order to minimize the differences between two or among multiple sets of images. This article describes clinical applications, validation, and algorithms of DIR techniques. Applications of DIR in radiation treatment include dose accumulation, mathematical modeling, automatic segmentation, and functional imaging. Validation methods discussed are based on anatomical landmarks, physical phantoms, digital phantoms, and per application purpose. DIR algorithms are also briefly reviewed with respect to two algorithmic components: similarity index and deformation models.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.85-94
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• 2023

Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.867-871
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• 2004

In this paper, a new analytic approach for shape preserving contrast enhancement is presented. Contrast enhancement is achieved by means of segmental histogram stretching modification which preserves the given image shape, not distorting the original shape. After global stretching, the image is partitioned into several level-sets according to threshold condition. The image information of each level-set is represented as typical value based on grouped differential values. The basic property is modified into common local schemes, thereby introducing the enhanced effect through extreme discrimination between subsets. The scheme is based on stretching the histogram of subsets in which the intensity gray levels between connected pixels are approximately same In spite of histogram widening, stretched by local image information, it neither creates nor destroys the original image, thereby preserving image shape and enhancing the contrast. By designing local histogram stretching operations, we can preserve the original shape of level-sets of the image, and also enhance the global intensity. Thus it can hold the main properties of both global and local image schemes, which leads to versatile applications in the field of digital epigraphy.

• Korean Journal of Remote Sensing
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• v.24 no.3
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• pp.251-256
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• 2008

Morphing method, one of classic image processing algorithms, has been used in various application fields. The motivation of this work is to investigate its applicability in consideration to geo-spatial data including airborne or space-borne images. For this purpose, the Beier and Neely morphing algorithm is tentatively implemented in the form of a prototype with user interface. As the results, this feature-based morphing with paired image sets can be used for general users: image simulation using two or more images and construction of color-blending image between source image and destination image in different types. Some simple application cases were demonstrated. This scheme is the simple and useful approach for those who want to utilize both geo-spatial data sets and airborne/space-borne image sets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.100-104
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• 2006

Fuzzy techniques can be applied in many domains of computer vision community. The definition of an adequate similarity measure for measuring the similarity between fuzzy sets is of great importance in the field of image processing, image retrieval and pattern recognition. This paper proposes a new class of the similarity measures. The properties, sensitivity and effectiveness of the proposed measures are investigated and tested on real data. Experimental results show that these similarity measures can provide a useful way for measuring the similarity between fuzzy sets.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.563-566
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• 2003

Quality measures play an important role in the field of image processing. Such measures are commonly used to assess the performance of different algorithms that are designed to perform a specific image processing task. In this paper we propose two novel measures for image quality assessment based on the notion of correlation between fuzzy sets. Two different definitions fur the correlation between fuzzy sets have been used. In order to calculate the proposed quality measures two approaches were evaluated, one with direct application of the measures to the image′s pixels and the other using the fuzzy set corresponding to the normalized histogram of the image. A comparative study of the proposed measures is performed by investigating their behavior using images with different types of distortions, such as impulsive "salt at pepper" noise, additive white Gaussian noise, multiplicative speckle noise, blurring, gamma distortion, and JPEG compression.

• Management & Information Systems Review
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• v.1
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• pp.401-412
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• 1997

Traditionally, classification of remote sensed image data is one of the important works for image data analysis procedure. So, many researchers have been devoted their endeavor to increasing accuracy of analysis, also, many classification algorithms have been proposed. In this paper, we propose new bands selection method for multispectral bands of remote sensed image data that use rough set theory. Using indiscernibility relations in rough sets, we show that can select the efficient bands of multispectral image data, automatically.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.205-208
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• 1997

Traditionally, classification of remote sensed image data is one of the important works for image data analysis procedure. So, many researchers devote their endeavor to increasing accuracy of analysis, also, many classification algorithms have been proposed. In this paper, we propose new classification method for remote sensed image data that use rough set theory. Using indiscernibility relation of rough sets, we show that can classify image data very easily.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.339-350
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• 2017

In recent, cloud computing paradigm and open source as a huge trend in the Information Communication Technology (ICT) are widely applied, being closely interrelated to each other in the various applications. The integrated services by both technologies is generally regarded as one of a prospective web-based business models impacting the concerned industries. In spite of progressing those technologies, there are a few application cases in the geo-based application domains. The purpose of this study is to develop a cloud-based service system for satellite image processing based on the pure and full open source. On the OpenStack, cloud computing open source, virtual servers for system management by open source stack and image processing functionalities provided by OTB have been built or constructed. In this stage, practical image processing functions for KOMPSAT within this service system are thresholding segmentation, pan-sharpening with multi-resolution image sets, change detection with paired image sets. This is the first case in which a government-supporting space science institution provides cloud-based services for satellite image processing functionalities based on pure open source stack. It is expected that this implemented system can expand with further image processing algorithms using public and open data sets.