• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.9
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• pp.952-958
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• 2014

In this paper, we propose a parametric sparse recovery algorithm(SRA) applied to a radar signal model, based on the compressive sensing(CS), for the ISAR(Inverse Synthetic Aperture Radar) image reconstruction from an incomplete radar-cross-section(RCS) data and for the estimation of rotation rate of a target. As the SRA, the iteratively-reweighted-least-square(IRLS) is combined with the radar signal model including chirp components with unknown chirp rate in the cross-range direction. In addition, the particle swarm optimization(PSO) technique is considered for searching correct parameters related to the rotation rate. Therefore, the parametric SRA based on the IRLS can reconstruct ISAR image and estimate the rotation rate of a target efficiently, although there exists missing data in observed RCS data samples. The performance of the proposed method in terms of image entropy is also compared with that of the traditional interpolation methods for the incomplete RCS data.

• Journal of Internet Computing and Services
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• v.19 no.4
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• pp.7-13
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• 2018

The methods for detecting copy move frogery (CMF) are divided into two categories, block-based methods and keypoint-based methods. Block-based methods have a high computational cost because a large number of blocks should be examined for CMF detection. In addition, the forgery detection may fail if a tampered region undergoes geometric transformation. On the contrary, keypoint-based methods can overcome the disadvantages of the block-based approach, but it can not detect a tampered region if the CMF forgery occurs in the low entropy region of the image. Therefore, in this paper, we propose a method to detect CMF forgery in all areas of image by combining keypoint-based and block-based methods. The proposed method first performs keypoint-based CMF detection on the entire image. Then, the areas for which the forgery check is not performed are selected and the block-based CMF detection is performed for them. Therefore, the proposed CMF detection method makes it possible to detect CMF forgery occurring in all areas of the image. Experimental results show that the proposed method achieves better forgery detection performance than conventional methods.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.83-93
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• 2012

This paper proposes an image segmentation framework that modifies the objective function of Fuzzy C-Means (FCM) to improve the performance and computational efficiency of the conventional FCM-based image segmentation. The proposed image segmentation framework includes a locally weighted fuzzy c-means (LWFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. Distance between a center pixel and a neighboring pixels are calculated within a window and these are basis for determining weights to indicate the importance of the memberships as well as to improve the clustering performance. We analyzed the segmentation performance of the proposed method by utilizing four eminent cluster validity functions such as partition coefficient ($V_{pc}$), partition entropy ($V_{pe}$), Xie-Bdni function ($V_{xb}$) and Fukuyama-Sugeno function ($V_{fs}$). Experimental results show that the proposed LWFCM outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with locally weighted information, fast generation FCM) in the cluster validity functions as well as both compactness and separation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.1
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• pp.60-69
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• 2019

In this paper, we deal with forward-looking imaging, and focus on forward-looking synthetic inverse scattering imaging for a vehicle with curved motion. For image formation, time domain correlation(TDC) is used and a 2D image of the ground in front of the vehicle is generated. Because TDC is a technique that implements matched filtering for a space-variant system, it is robust to Gaussian additive noise of measurements. Furthermore, comparison and analysis between images from linear motion and curved motion show that the resolution of the image is improved; however, the entropy of the image is increased owing to curved motion.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.4
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• pp.419-433
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• 2014

The Mars Exploration Rover Opportunity investigated plains at Meridiani Planum, where laminated sedimentary rocks are present. The Opportunity rover's Athena morphological investigation showed microstructures organized in intertwined filaments of microspherules: a texture we have also found on samples of terrestrial (biogenic) stromatolites and other microbialites. We performed a quantitative image analysis to compare images (n=45) of microbialites with the images (n=30) photographed by the rover (corresponding, approximately, to 25,000/15,000 microstructures). Contours were extracted and morphometric indexes were obtained: geometric and algorithmic complexities, entropy, tortuosity, minimum and maximum diameters. Terrestrial and Martian textures present a multifractal aspect. Mean values and confidence intervals from the Martian images overlapped perfectly with those from the terrestrial samples. The probability of this occurring by chance is $1/2^8$, less than p<0.004. Terrestrial abiogenic pseudostromatolites showed a simple fractal structure and different morphometric values from those of the terrestrial biogenic stromatolite images or Martian images with a less ordered texture (p<0.001). Our work shows the presumptive evidence of microbialites in the Martian outcroppings: i.e., the presence of unicellular life on the ancient Mars.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.698-701
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• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• Journal of Multimedia Information System
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• v.2 no.2
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• pp.193-206
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• 2015

In this paper a new approach for image encryption based on quadruple encryption with dual chaotic maps is proposed. The encryption process is performed with quadruple encryption by invoking the encrypt and decrypt routines with different keys in the sequence EDEE. The decryption process is performed in the reverse direction DDED. The key generation for the quadruple encryption is achieved with a 1D Circle map. The chaotic values for the encrypt and decrypt routines are generated by using a 2D Henon map. The Encrypt routine E is composed of three stages i.e. permutation, pixel value rotation and diffusion. The permutation is achieved by: row and column scrambling with chaotic values, exchanging the lower and the upper principal and secondary diagonal elements based on the chaotic values. The second stage circularly rotates all the pixel values based on the chaotic values. The last stage performs the diffusion in two directions (forward and backward) with two previously diffused pixels and two chaotic values. The security and performance of the proposed scheme are assessed thoroughly by using the key space, statistical, differential, entropy and performance analysis. The proposed scheme is computationally fast with security intact.

• Journal of Broadcast Engineering
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• v.1 no.1
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• pp.35-43
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• 1996

In this paper, we present efficient six-connected contour coding algorithms which can uniquely reconstruct any contour image and efficiently compress the contour data. We first design chain difference codes using two onward direction states, based on the fact that the probability distribution of the direction vectors of horiwntal/vertical direction state is different from that of the direction vectors of diagonal direction state. In order to increase coding efficiency, we also design chain difference codes using five states which are classified according to current and previous onward direction vectors. In addition, we also remove the END codeword to reduce total codeword occurrency. Experimental results show that when using 2 states and 5 states without END codeword total entropy decreases by about 12% and 14% for real images and by about 10% and 26% for a synthetic image, respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.82-88
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• 2000

In this paper, We compare the Harr wavelet of the S+P transform with various integer coefficient filter banks and apply 9/7 ICFB to the wavelet transform. In addition, we propose a entropy-coding method that exploits the multiresolution structure and the feedback of the prediction error, and can efficiently compress the transformed image for progressive transmission. Simulation results are included to compare to the compression ratio using the S+P transform with different types of images.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.33-39
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• 2007

Hyperspectral remote sensing data contain plenty of information about objects, which makes object classification more precise. In this paper, we proposed a new spectral similarity measure, called Spectral Mutual Information (SMI) for hyperspectral image classification problem. It is derived from the concept of mutual information arising in information theory and can be used to measure the statistical dependency between spectra. SMI views each pixel spectrum as a random variable and classifies image by measuring the similarity between two spectra form analogy mutual information. The proposed SMI was tested to evaluate its effectiveness. The evaluation was done by comparing the results of preexisting classification method (SAM, SSV). The evaluation results showed the proposed approach has a good potential in the classification of hyperspectral images.