• Journal of IKEEE
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• v.24 no.1
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• pp.254-259
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• 2020

This paper is the implementation of the conversion system from image to music based on intentional synesthesia. The input image based on color, texture, and shape was converted into melodies, harmonies and rhythms of music, respectively. Depending on the histogram of colors, the melody can be selected and obtained probabilistically to form the melody. The texture in the image expressed harmony and minor key with 7 characteristics of GLCM, a statistical texture feature extraction method. Finally, the shape of the image was extracted from the edge image, and using Hough Transform, a frequency component analysis, the line components were detected to produce music by selecting the rhythm according to the distribution of angles.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.65-70
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• 2007

Red tide is becoming hot issue of environmental problem worldwide since the 1990. Advanced nations, are progressing study that detect red tide area on early time using satellite for sea. But, our country most seashores bends serious. Also because there are a lot of turbid method streams on coast, hard to detect small red tide area by satellite for sea that is low resolution. Also, method by sea color that use one feature of satellite image for sea of existent red tide area detection was most. In this way, have a few feature in image with sea color and it can cause false negative mistake that detect red tide area. Therefore, in this paper, acquired texture information to use GLCM(Gray Level Co occurrence Matrix)'s texture 6 information about high definition land satellite south Coast image. Removed needless component reducing dimension through principal component analysis from this information. And changed into 2 principal component accumulation images, Experiment result 2 principal component conversion accumulation image's eigenvalues were 94.6%. When component with red tide area that uses only sea color image and all principal component image. displayed more correct result. And divided as quantitative,, it compares with turbid stream and the sea that red tide does not exist using statistical feature analysis about texture.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.137-138
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• 2008

This paper proposes the modified image contrast enhancement technique for small-sized display of mobile handset. Sample images are user interface images, in which scaled up wVGA($800{\times}480$) from qVGA($320{\times}240$) that we can see easily in mobile handset. The display size of mobile handset is relatively small, so the goal of this paper is to simplify image contrast enhancement algorithm based on conventional HE (Histogram Equalization) algorithm and improve computational effectiveness to minimize power consumption in real hardware IC. In this paper, we adopt HE technique, which is classical and widely used for image contrast enhancement. At first, the input frame image is partitioned to temporal sub-frames and then analyzes gray level histogram of each sub-frame. In case that the analyzed histogram of some sub-frames deviates so much from reference level (it means that the sub-frame image components consist of too bright ones or dark ones), apply DHE(Dynamic Histogram Equalization) algorithm. In the other case, apply classical Histogram Linearization (or Global HE) algorithm. Also we compare the HE technique with gamma LUT (Look-Up Table) method, which is known as the simplest technique to enhance image contrast.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.14-20
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• 2003

This paper presents a text extraction method of camera based document image. It is more difficult to recognize camera based document image in comparison with scanner based image because of segmentation problem due to variable lighting condition and versatile fonts. Both document binarization and character extraction are important processes to recognize camera based document image. After converting color image into grey level image, gray level normalization is used to extract character region independent of lighting condition and background image. Local adaptive binarization method is then used to extract character from the background after the removal of noise. In this character extraction step, the information of the horizontal and vertical projection and the connected components is used to extract character line, word region and character region. To evaluate the proposed method, we have experimented with documents mixed Hangul, English, symbols and digits of the ETRI database. An encouraging binarization and character extraction results have been obtained.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.29-37
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• 2006

Thresholding is a fundamental approach to segmentation that utilizes a significant degree of pixel popularity or intensity. Otsu's thresholding employed the normalized histogram as a discrete probability density function. Also it utilized a criterion that minimizes the between-class variance of pixel intensity to choose a threshold value for segmentation. However, the Otsu's method has a disadvantage of repeatedly searching optimal thresholds for the entire range. In this paper, a simple but fast multi-level thresholding approach is proposed by means of extending the Otsu's method. Rather than invoke the Otsu's method for the entire gray range, we advocate that the gray-level range of an image be first divided into smaller sub-ranges, and that the multi-level thresholds be achieved by iteratively invoking this dividing process. Initially, in the proposed method, the gray range of the object image is divided into 2 classes with a threshold value. Here, the threshold value for segmentation is selected by invoking the Otsu's method for the entire range. Following this, the two classes are divided into 4 classes again by applying the Otsu's method to each of the divided sub-ranges. This process is repeatedly performed until the required number of thresholds is obtained. Our experimental results for three benchmark images and fifty faces show a possibility that the proposed method could be used efficiently for pattern matching and face recognition.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• Korean Journal of Poultry Science
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• v.22 no.4
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• pp.233-238
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• 1995

A computer vision system was built to generate images of a single, stationary egg. This system includes a CGD camera, a frame grabber, and incandescent back lighting system. Image processing algorithms were developed to inspect egg shell and to sort eggs. Those values of both gray level and area of dark spots in the egg image were used as criteria to detect holes in egg and those values of both area and roundness of dark spots in the egg image were used to detect cracks in egg. For a sample of 300 eggs, this system was able to correctly analyze an egg for the presence of a defect 97.5% of the time. The weights of eggs were found to be linear to both the projected area and the perimeter of eggs viewed from above. Those two values were used as criteria to sort eggs. The coefficients of determination(r$^2$) for the regression equations between weights and those two values were 0.967 and 0.972 in the two sets of experiment. Accuracies in grading were found to be 95.6% and 96.7% as compared with results from sizing by electronic weight scale.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.2
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• pp.26-33
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• 2005

In this paper, an algorithm is presented to recognize road based on unpaved test courses image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, gray level slicing, masking and identification of unpaved test courses. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing unpaved road. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning or assistance system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.405-408
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• 2005

This paper proposed the method to images of losses using restorable wavelet transformation. The algorithm proposed in this work stars by processing the pre-quantizer on the original images to organize an image that matches the gray level. The wavelet transformation filter to the original image which is already pre-quantized in order to segment bands. Considering the lowest coding of bands influencing the most to the overall condition of the reconstructed image, it only uses the Huffman coding using prediction. Reconstructed images by proposed algorithm showed higher PSNR when coding images of JPEG or non pre-quantized images. Applying pre-quantizer can control the peak errors and is expected to be useful at mass image compression.

• Journal of Radiation Industry
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• v.17 no.4
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• pp.397-403
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• 2023

The purpose of this study is to obtain brain MRI images through Heavenly T₂ FLAIR and DWI techniques to find out strengths and weaknesses of each image. Data were analyzed on 13 normal people and 17 brain tumor patients. Philips Ingenia 3.0TCX was used as the equipment used for the inspection, and 32 Channel Head Coil was used to acquire data. Using Image J and Infinity PACS Data, 3mm² of gray matter, white matter, cerebellum, basal ganglia, and tumor areas were set and measured. Quantitative analysis measured SNR and CNR as an analysis method, and qualitative analysis evaluated overall image quality, lesion conspicuity, image distortion, susceptibility artifact and ghost artifact on a 5-point scale. The statistical significance of data analysis was that Wilcox-on Signed Rank Test and Paired t-test were executed, and the statistical program used was SPSS ver.22.0 and the p value was less than 0.05. In quantitative analysis, the SNR of gray matter, white matter, cerebellum, basal ganglia, and tumor of Heavily T₂ FLAIR is 41.45±0.13, 40.52±0.45, 41.44±0.51, 40.96±0.09, 35.28±0.46 and the CNR is 15.24±0.13, 16.75±0.23, 16.28±0.41, 15.83±0.17, 16.63±0.51. In DWI, SNR is 32.58±0.22, 36.75±0.17, 30.21±0.19, 35.83±0.11, 43.29±0.08, and CNR is 13.14±0.63, 14.21±0.31, 12.95±0.32, 11.73±0.09, 17.56±0.52. In normal tissues, Heavenly T₂ FLAIR obtained high results, but in disease evaluation, high results were obtained at DWI, b=1000 (p<0.05). In addition, in the qualitative analysis, overall image quality, lesion conspicuity, image distortion, susceptibility artifact and ghost artifact aspects of the Heavily T₂ FLAIR were evaluated, and 3.75±0.28, 2.29±0.24, 3.86±0.23, 4.08±0.21, 3.79±0.22 values were found, respectively, and 2.53±0.39, 4.13±0.29, 1.90±0.20, 1.81±0.21, 1.52±0.45 in DWI. As a result of qualitative analysis, overall image quality, image distortion, susceptibility artifact and ghost artifact were rated higher than DWI. However, DWI was evaluated higher in lesion conspicuity (p<0.05). In normal tissues, the level of Heavenly T₂ FLAIR was higher, but the DWI technique was higher in the evaluation of the disease (tumor). The two results were necessary techniques depending on the normal site and the location of the disease. In conclusion, statistically significant results were obtained from the two techniques. In quantitative and qualitative analysis, the two techniques had advantages and disadvantages, and in normal and disease evaluation, the two techniques produced useful results. These results are believed to be educational data for clinical basic evaluation and MRI in the future.