• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.7-16
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• 2002

En this paper, we propose the algorithm that has analyzed the map Information automatically to extract the contour lines and numbers, symbols from the map image. After converting the input image to binary one, thinned image is obtained by thinning algorithm. The contour elements in the thinned image are classified and the classified elements are analyzed to automatically classify the numbers from symbols. Finally, the broken parts are restored by reconstruction algorithm. The performance of proposed algorithm is verified through the simulation. The proposed one has good performance.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.406-415
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• 2021

The worldwide spread of the Internet and the digital information revolution have resulted in a rapid increase in the use and transmission of multimedia information due to the rapid development of communication technologies. It is important to protect images in order to prevent problems such as piracy and illegal distribution. To solve this problem, I propose a new digital color image encryption algorithm in this paper. I design a new pseudo-random number generator based on 1D five-neighborhood maximum length cellular automata (FN-MLCA) to change the pixel values of the plain image into unpredictable values. And then I use a 3D chaotic cat map to effectively shuffle the positions of the image pixel. In this paper, I propose a method to construct a new MLCA by modeling 1D FN-MLCA. This result is an extension of 1D 3-neighborhood CA and shows that more 1D MLCAs can be synthesized. The safety of the proposed algorithm is verified through various statistical analyses.

• Journal of Broadcast Engineering
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• v.11 no.1 s.30
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• pp.91-99
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• 2006

Pre-processing is one of image processing techniques to enhance image quality or appropriately convert a given image into another form for a specific purpose. An 8 bit depth map obtained by a depth camera usually contains a lot of noisy components caused by the characteristics of depth camera and edges are also more distorted by the quality of a source object and illumination condition comparing with edges in RGB texture image. To reduce this distortion, we use noise removing filters, but they are only able to reduce noise components, so that distorted edges of depth map can not be properly recovered. In this paper, we propose an algorithm that can reduce noise components and also enhance the quality of edges of depth map by using edges in RGB texture. Consequently, we can reduce errors in multi-view stereo image synthesis process.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.145-151
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• 2014

This paper proposes an image haze removal algorithm for a single image. The conventional Dark Channel Prior(DCP) algorithm estimates a transmission map using the dark information in an image, and the haze regions are then detected using a matting algorithm. However, since the DCP algorithm uses block-based processing, block artifacts are invariably formed in the transmission map. To solve this problem, the proposed algorithm generates a modified transmission map using a Hidden Markov Random Field(HMRF) and Expectation-Maximization(EM) algorithm. Experimental results confirm that the proposed algorithm is superior to conventional algorithms in image haze removal.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.49 no.3
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• pp.30-36
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• 2012

block truncation coding(BTC) image compression is known as a simple and efficient technology for image compression algorithm. In this paper, we propose RMC-BTC algorithm(RMC : reduction method chrominace data) for color image compression. To compress chrominace data, in every BTC block, the RMC-BTC coding employs chrominace data expressed with average of chrominace data and using method of luminance data bit-map to represented chrominance data bit-map. Experimental results shows efficiency of proposed algorithm, as compared with PSNR and compression ratio of the conventional BTC method.

• 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 Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1245-1252
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• 2013

As the usage of digital image contents increase, a security problem for the payed image data or the ones requiring confidentiality is raised. This paper propose a depth-map image contents encryption methodology to hide the depth information. This method is performed on the frequency coefficients in the Wavelet domain. This method, by selecting the level and threshold value for the wavelet transform, encryption at various strengths are possible. The experimental results showed that encrypting only 0.048% of the entire data was enough to hide the constants of the depth-map. The encryption algorithm expected to be used effectively on the researches on encryption and others for image processing.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.6
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• pp.15-22
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• 2023

In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) and Enhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differences between these two image formats. "FarmMap," initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital map of agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For the purpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as the attribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulated experiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental results indicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28% higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between the two images.

• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.2
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• pp.263-275
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• 1992

This study intends to provide strategic positioning of brand image analysed from the view point of perceptual dimensions of clothing consumers. Consumers are segmented on the basis of the attributes of brand image, and in each segment, perceptual map is composed according to multidimensional scaling. The results are as follows; 1. According to the Benefit Segmentation, it is statistically significant that the consumers are divided into 'product-factor oriented group 'and' image-factor oriented group'. 2. From the analysis of perceptual map upon the 'similarity of brand image,'image-factor oriented group 'perceives more differently than 'product-factor oriented group' 3. From the analysis of perceptual map with the evaluation of attributes of brand image, price, promotion and design are significant determinants in 'total consumer group'. In addition, store image is significant determinant in' image-factor oriented group' and quality is significant determinant in' product-factor oriented group'. According to the evaluation of consumers on 8 brands with determining attribute-vector, ranks of brands in each segment are similar in the vector of price and promotion but different in the vector of design between segment groups. 4. From the analysis of perceptual map upon the preference of brand image, the distribution of preference and position of ideal point are different between segment groups. 5. With evaluation of purchase habit, statistically significant differences are found between groups segmented in the degree of importance of attributes, purchasing motive, purchasing time, sources of information and expenses for clothes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.149-156
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• 2008

In this paper, we propose a method aiming at updating the building information of the digital maps using single high resolution satellite image and digital map. Firstly we produced a digital orthoimage through the automatic co-registration of QuickBird image and 1:1,000 digital map. Secondly we extracted building height information through the template matching of digital map's building vector data and the image's edges obtained by Canny operator. Finally we refined the shape of some buildings by using the result from template matching as the seed polygon of the greedy snake algorithm. In order to evaluate the proposed method's effectiveness, we estimated accuracy of the extracted building information using LiDAR DSM and 1:1,000 digital map. The evaluation results showed the proposed method has a good potential for extraction and revision of building information.