• Journal of KIISE:Computer Systems and Theory
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• v.26 no.3
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• pp.282-290
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• 1999

본 논문에서는 영상 데이터베이스에서의 색인화를 위해 화소간의 엔트로피(inter-pixel entropy)측면에서 영상을 해석하여 특징을 추출하는 색인기법을 제안하였다. 엔트로피를 정량적으로 나타내기 위하여 PIM(picture information measure)에 기반한 특징을 각각의 영상들로부터 추출하여 색인으로 사용하였다. 엔트로피에 기반 하는 특징은 한 영상에 대하여 3가지를 추출하였다. 첫 번째 특징은 원 영상으로부터 직접 값들을 추출하였고 영상의 해상도와 그레이 레벨을 낮추어 가면서 얻어지는 영상들로부터 두 번째, 세번째 의 엔트로피 특징을 각각 추출하였다. 일반적으로 영상의 경우에 엔트로피가 높은 영역에 정보가 집중되는 경향이 있고 엔트로피가 낮은 영역에서는 배경 영역 등의 정보를 추출하게된다. 이러한 이유로 색채 특쟁은 엔트로피에 따라서 분리된 영역에서 추출하였다. 전역검색(global search)은 엔트로피 특징을 이용하였고 색채 특징을 이용하여 지역 검색(local search)을 시도하였다. 실험은 색채와 엔트로피로 식별이 유용한 꽃 영상을 사용하였는데 원치 않는 영상이 상위 단계에서 나타나는 빈도가 기존의 타 기법에 비해 줄어들었다.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.329-334
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• 1997

A tomography algorithm to maximize the entropy of image using Lagrangian multiplier technique and conjugate gradient method has been designed for the measurement of 2D spatial distribution of intense neutral beams of KSTAR NBI(Korea Superconducting Tokamak Advanced Research Neutral Beam Injector) which is now being designed. A possible detection system was assumed and a numerical simulation has been implemented to test the reconstruction quality of given beam profiles. This algorithm has the good applicability for sparse projection data and thus, can be used for the neutral beam tomography.

• ETRI Journal
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• v.39 no.2
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• pp.173-180
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• 2017

Multimodal medical image fusion is the process of retrieving valuable information from medical images. The primary goal of medical image fusion is to combine several images obtained from various sources into a distinct image suitable for improved diagnosis. Complexity in medical images is higher, and many soft computing methods are applied by researchers to process them. Intuitionistic fuzzy sets are more appropriate for medical images because the images have many uncertainties. In this paper, a new method, based on Sugeno's intuitionistic fuzzy set (SIFS), is proposed. First, medical images are converted into Sugeno's intuitionistic fuzzy image (SIFI). An exponential intuitionistic fuzzy entropy calculates the optimum values of membership, non-membership, and hesitation degree functions. Then, the two SIFIs are disintegrated into image blocks for calculating the count of blackness and whiteness of the blocks. Finally, the fused image is rebuilt from the recombination of SIFI image blocks. The efficiency of the use of SIFS in multimodal medical image fusion is demonstrated on several pairs of images and the results are compared with existing studies in recent literature.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1425-1430
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• 2018

In this paper, we propose a CNN based deep learning algorithm for semantic segmentation of images. In order to improve the accuracy of semantic segmentation, we combined pixel level object classification and image level object classification. The image level object classification is used to accurately detect the characteristics of an image, and the pixel level object classification is used to indicate which object area is included in each pixel. The proposed network structure consists of three parts in total. A part for extracting the features of the image, a part for outputting the final result in the resolution size of the original image, and a part for performing the image level object classification. Loss functions exist for image level and pixel level classification, respectively. Image-level object classification uses KL-Divergence and pixel level object classification uses cross-entropy. In addition, it combines the layer of the resolution of the network extracting the features and the network of the resolution to secure the position information of the lost feature and the information of the boundary of the object due to the pooling operation.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.113-116
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• 1995

Medical image compression using the wavelet transform has been tried. Due to the flexibility in representing nonstationary image signal in both time and frequency domains and its ability to adapt human visual characteristics, wavelet transform has unique advantage in images compression. In the proposed wavelet compression original image is decomposed into multi-scale bands. Different scale factors are employed in the quantization of wavelet decomposed images in different bands. For the lowest band, a predictor is designed and error signal is entropy coded. For high scale bands, runlength coding for toro run is used with Huffman coding. From simulation with magnetic resonance images($256\times256$ size, 256 graylevels) the proposed algorithm is superior to the JPEG by more than 2.5 dB in near lossless compression (CR = 8 - 10).

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.601-604
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• 1999

This Paper proposes the simple and efficient image retrieval algorithm using subregional texture features. In order to retrieve images in terms of its contents, it is required to obtain a precise segmentation. However, it is very difficult and takes a long computing time. Therefore. this paper proposes a simple segmentation method, which is to divide an image into high and low entropy regions by using Picture Information Measure (PIM). Also, in order to describe texture characteristics of each region, this paper suggest six different texture features produced on the basis of co-occurrence matrix. For an image retrieval system, a normalised correlation is adopted as a similarity function, which is not dependent on the range of each texture feature values. Finally, this proposed algorithm is applied to a various images and produces competitive results.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.270-273
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• 2007

In this paper, a SAR-despeck1ing approach of adaptive iteration based a Bayesian model using the lognormal distribution for image intensity and a Gibbs random field (GRF) for image texture is proposed for noise removal of the images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. The iterative approach based on MRF is very effective for the inner areas of regions in the observed scene, but may result in yielding false reconstruction around the boundaries due to using wrong information of adjacent regions with different characteristics. The proposed method suggests an adaptive approach using variable parameters depending on the location of reconstructed area, that is, how near to the boundary. The proximity of boundary is estimated by the statistics based on edge value, standard deviation, entropy, and the 4th moment of intensity distribution.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1106-1111
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• 1999

Discrete cosine Transform (DCT) is most popular block transform coding in lossy mode. DCT is close to statistically optimal transform - the Karhunen Loeve transform. In this paper, a module for still image encoder is implemented with TMS320C80 based on JPEG, which are international standards for image compression. Th still image encoder consists of three parts- a transformer, a vector quantizer and an entropy encoder.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.5
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• pp.889-898
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• 1997

Most general content-based image retrieval techniques use color and texture as retrieval indices. Spatial information is not used to color histogram and color pair based on color retrieval techniques. This paper proposes the selection of a set of representative in the duplicated color histogram, the analysis of spatial information of the selected colors and the image retrieval process based on the duplicated color histogram and spatial information. Two color historgrams for background and object are used in order to decide on color selection in the duplicated color histogram. Spatial information is obtained using a maximum entropy discretization. A retrieval process applies to duplicated color histogram and spatial to retrieve input images and relevant images. As the result of experiment of the image retrieval, improved color his togram and spatial information method hs increased the retrieval effectiveness more the color histogram method and color pair method.

• Computers and Concrete
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• v.34 no.1
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• pp.79-91
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• 2024

In symmetric cryptography, a cryptographically secure Substitution-Box (S-Box) is a key component of a block cipher. S-Box adds a confusion layer in block ciphers that provide resistance against well-known attacks. The generation of a cryptographically secure S-Box depends upon its generation mechanism. In this paper, we propose a novel framework for the construction of cryptographically secure S-Boxes. This framework uses a combination of linear fractional transformation and permutation functions. S-Boxes security is analyzed against well-known security criteria that include nonlinearity, bijectiveness, strict avalanche and bits independence criteria, linear and differential approximation probability. The S-Boxes can be used in the encryption of any grayscale digital images. The encrypted images are analyzed against well-known image analysis criteria that include pixel changing rates, correlation, entropy, and average change of intensity. The analysis of the encrypted image shows that our image encryption scheme is secure.