• Journal of the Korea Computer Graphics Society
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• v.15 no.3
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• pp.11-18
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• 2009

In this paper, we introduce a novel method to extend the JPEG standard, which is the most widely used for lossy image compression, in order to improve compression ratio. To employ two of the most successful methodologies for the data compression: differential coding and quantization simultaneously, we propose a line-based approach. For each line in a block, we apply one-dimensional discrete cosine transformation to the increments instead of the pixel values. Those values are quantized and entropy-coded similarly to the JPEG standard. To further increase compression ratio, the proposed method is plugged into the JPEG standard to form a new compression method, in which the proposed method are applied to only selected JPEG blocks. In our experiment, we found that the proposed method outperform the JPEG standard when the qualities of the coded images are set to be high. We believe the proposed method can be simply plugged into the standard to improve its compression ratio for higher quality images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2409-2416
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• 2015

This paper presents an orientation-based adaptive prediction method for effective lossless image compression. For a robust prediction, the proposed method estimates the directional information and the property near the current pixel in a support region-based fashion, not a pixel-based one which is sensitive to a small variation. We improve the prediction performance effectively by selection of the prediction pixel adaptively according to the similarity between support regions of the current pixel and the neighboring pixels. Comprehensive experiments demonstrate that the proposed scheme achieves excellent prediction performance measured in entropy of the prediction error compared to a number of conventional prediction methods such as MED, GAP, and EDP. Moreover the complexity of the proposed algorithm measured by average execution time is low compared to MED which is the simplest prediction method.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.49-57
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• 2012

Conventional fuzzy c-means (FCM) algorithms have achieved a good clustering performance. However, they do not fully utilize the spatial information in the image and this results in lower clustering performance for images that have low contrast, vague boundaries, and noises. To overcome this issue, we propose an enhanced spatial fuzzy c-means (ESFCM) algorithm that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors in a $3{\times}3$ square window. To evaluate between the proposed ESFCM and various FCM based segmentation algorithms, we utilized clustering validity functions such as partition coefficient ($V_{pc}$), partition entropy ($V_{pe}$), and Xie-Bdni function ($V_{xb}$). Experimental results show that the proposed ESFCM outperforms other FCM based algorithms in terms of clustering validity functions.

• Current Optics and Photonics
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• v.4 no.4
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• pp.286-292
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• 2020

Imaging through multicore fiber (MCF) is of great significance in the biomedical domain. Although several techniques have been developed to image an object from a signal passing through MCF, these methods are strongly dependent on the surroundings, such as vibration and the temperature fluctuation of the fiber's environment. In this paper, we apply a new, strong technique called deep learning to reconstruct the phase image through a MCF in which each core is multimode. To evaluate the network, we employ the binary cross-entropy as the loss function of a convolutional neural network (CNN) with improved U-net structure. The high-quality reconstruction of input objects upon spatial light modulation (SLM) can be realized from the speckle patterns of intensity that contain the information about the objects. Moreover, we study the effect of MCF length on image recovery. It is shown that the shorter the fiber, the better the imaging quality. Based on our findings, MCF may have applications in fields such as endoscopic imaging and optical communication.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.101-108
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• 2024

AI-OCR (Artificial Intelligence Optical Character Recognition) combines OCR technology with Artificial Intelligence to overcome limitations that required human intervention. To enhance the performance of AI-OCR, training on diverse data sets is essential. However, the recognition rate declines when image colors have similar brightness levels. To solve this issue, this study employs Homomorphic filtering as a preprocessing step to clearly differentiate color levels, thereby increasing text recognition rates. While Homomorphic filtering is ideal for text extraction because of its ability to adjust the high and low frequency components of an image separately using a gamma value, it has the downside of requiring manual adjustments to the gamma value. This research proposes a range for gamma threshold values based on tests involving image contrast, brightness, and entropy. Experimental results using the proposed range of gamma values in Homomorphic filtering suggest a high likelihood for effective AI-OCR performance.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.362-369
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• 2014

This study aimed to propose an accurate diagnostic method for osteoporosis by realizing a computer-aided diagnosis system with the application of the statistical analysis of texture features using digital images of lateral lumbar spine of patients with osteoporosis and providing reliable supplementary diagnostic information by model experimental research for early diagnosis of diseases. For these purposes, digital images of lateral lumbar spine of normal individuals and patients with osteoporosis were used in the experiments, and the values of statistical texture features on the set ROI were expressed in six parameters. Among the texture feature values of the six parameters of osteoporosis, the highest and lowest recognition rates of 95 and 80% were shown in average gray level and uniformity, respectively. Moreover, all the six parameters showed recognition rates of over 80% for osteoporosis: 82.5% in average contrast, 90% in smoothness, 87.5% in skewness, and 87.5% in entropy. Therefore, if a program developing into a computer-aided diagnosis system for medical images is coded based on the results of this study, it is considered possible to be applied to preliminary diagnostic data for automatic detection of lesions and disease diagnosis using medical images, to provide information for definite diagnosis of diseases, to diagnose by limited device, and to be used to shorten the time to analyze medical images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.70-77
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• 2008

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.583-598
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• 2022

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system's data for deep learning model learning.

• Land and Housing Review
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• v.14 no.2
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• pp.125-135
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• 2023

Drones are used in various fields, including land survey, transportation, forestry/agriculture, marine, environment, disaster prevention, water resources, cultural assets, and construction, as their industrial importance and market size have increased. In this study, image data for deep learning was collected using a mavic3 drone capturing images at a shooting altitude was 20 m with ×7 magnification. Swin Transformer and UperNet were employed as the backbone and architecture of the deep learning model. About 800 sheets of labeled data were augmented to increase the amount of data. The learning process encompassed three rounds. The Cross-Entropy loss function was used in the first and second learning; the Tversky loss function was used in the third learning. In the future, when the crack detection model is advanced through convergence with the Internet of Things (IoT) through additional research, it will be possible to detect patching or potholes. In addition, it is expected that real-time detection tasks of drones can quickly secure the detection of pavement maintenance sections.

• Journal of IKEEE
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• v.13 no.4
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• pp.116-124
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• 2009

Recently the CPU performance of modem chipsets or multimedia processors of mobile phone is as high as notebook PC. That is why mobile phone has been emerged as a leading ICON on the convergence of consumer electronics. The various applications of mobile phone such as DMB, digital camera, video telephony and internet full browsing are servicing to consumers. To meet all the demands the image quality has been increasingly important. Mobile phone is a portable device which is widely using in both the indoor and outside environments, so it is needed to be overcome to deteriorate image quality depending on environmental light source. Furthermore touch window is popular on the mobile display panel and it makes contrast loss because of low transmittance of ITO film. This paper presents the image enhancement algorithm to be embedded on image enhancement SoC. In contrast enhancement, we propose Clipped histogram stretching method to make it adaptive with the input images, while S-shape curve and gain/offset method for the static application And CIELCh color space is used to sunlight readability enhancement by controlling the lightness and chroma components which is depended on the sensing value of light sensor. Finally the performance of proposed algorithm is evaluated by using histogram, RGB pixel distribution, entropy and dynamic range of resultant images. We expect that the proposed algorithm is suitable for image enhancement of embedded SoC system which is applicable for the small-sized mobile display.