• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.151-160
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• 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.

• Smart Media Journal
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• v.13 no.8
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• pp.39-48
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• 2024

Recently, various studies have emerged that utilize hyperspectral imaging for crop growth analysis and early disease diagnosis. However, the challenge of using numerous spectral bands or finding the optimal bands for crop area remains a difficult problem. In this paper, we propose a method of searching the optimized spectral band of crop area based on deep learning using the hyper-spectral image. The proposed method extracts RGB images within hyperspectral images to segment background and foreground area through a Vision Transformer-based Seformer. The segmented results project onto each band of gray-scale converted hyperspectral images. It determines the optimized spectral band of the crop area through the pixel comparison of the projected foreground and background area. The proposed method achieved foreground and background segmentation performance with an average accuracy of 98.47% and a mIoU of 96.48%. In addition, it was confirmed that the proposed method converges to the NIR regions closely related to the crop area compared to the mRMR method.

• Progress in Medical Physics
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• v.27 no.1
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• pp.1-7
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• 2016

In this work, we considered a compressed-sensing (CS)-based image deblurring scheme with a total-variation (TV) regularization penalty for improving image characteristics in digital tomosynthesis (DTS). We implemented the proposed image deblurring algorithm and performed a systematic simulation to demonstrate its viability. We also performed an experiment by using a table-top setup which consists of an x-ray tube operated at $90kV_p$, 6 mAs and a CMOS-type flat-panel detector having a $198-{\mu}m$ pixel resolution. In the both simulation and experiment, 51 projection images were taken with a tomographic angle range of ${\theta}=60^{\circ}$ and an angle step of ${\Delta}{\theta}=1.2^{\circ}$ and then deblurred by using the proposed deblurring algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. According to our results, the image sharpness of the recovered x-ray images and the reconstructed DTS images were significantly improved and the cross-plane spatial resolution in DTS was also improved by a factor of about 1.4. Thus the proposed deblurring scheme appears to be effective for the blurring problems in both conventional radiography and DTS and is applicable to improve the present image characteristics.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.936-946
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• 2009

A graph cuts method has recently attracted a lot of attentions for image segmentation, as it can globally minimize energy functions composed of data term that reflects how each pixel fits into prior information for each class and smoothness term that penalizes discontinuities between neighboring pixels. In previous approaches to graph cuts-based automatic image segmentation, GMM(Gaussian mixture models) is generally used, and means and covariance matrixes calculated by EM algorithm were used as prior information for each cluster. However, it is practicable only for clusters with a hyper-spherical or hyper-ellipsoidal shape, as the cluster was represented based on the covariance matrix centered on the mean. For arbitrary-shaped clusters, this paper proposes graph cuts-based image segmentation using mean shift analysis. As a prior information to estimate the data term, we use the set of mean trajectories toward each mode from initial means randomly selected in $L^*u^*{\upsilon}^*$ color space. Since the mean shift procedure requires many computational times, we transform features in continuous feature space into 3D discrete grid, and use 3D kernel based on the first moment in the grid, which are needed to move the means to modes. In the experiments, we investigate the problems of mean shift-based and normalized cuts-based image segmentation methods that are recently popular methods, and the proposed method showed better performance than previous two methods and graph cuts-based automatic image segmentation using GMM on Berkeley segmentation dataset.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.353-360
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• 2006

This paper presents a robust algorithm for segmenting a vehicle license plate area from a road image. We consider the features of license plates in three aspects : 1) edges due to the characters in the plate, 2) colors in the plate, and 3) geometric properties of the plate. In the preprocessing step, we compute the thresholds based on each feature to decide whether a pixel is inside a plate or not. A statistical approach is applied to the sample images to compute the thresholds. For a given road image, our algorithm binarizes it by using the thresholds. Then, we select three candidate regions to be a plate by searching the binary image with a moving window. The plate area is selected among the candidates with simple heuristics. This algorithm robustly detects the plate against the transformation or the difference of color intensity of the plate in the input image. Moreover, the preprocessing step requires only a small number of sample images for the statistical processing. The experimental results show that the algorithm has 97.8% of successful segmentation of the plate from 228 input images. Our prototype implementation shows average processing time of 0.676 seconds per image for a set of $1280{\times}960$ images, executed on a 3GHz Pentium4 PC with 512M byte memory.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06d
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• pp.444-448
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• 2008

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2:image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• The KIPS Transactions:PartA
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• v.16A no.3
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• pp.209-216
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• 2009

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker's architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human's location, which uses three images, took an average of 0.037 seconds. The SVM's feature of human's motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.

• Journal of radiological science and technology
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• v.26 no.2
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• pp.63-69
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• 2003

Filtering is necessary to reduce statistical noise and to increase image quality in SPECT images. Noises controled by low-pass filter designed to suppress high spatial frequency in SPECT image. Most SPECT filter function control the degree of high frequency supression by chosing a cut of frequency. The location of cut off frequency determines the affect image noise and spatial resolution. If select the low cut off frequency, its provide good noise suppression but insufficient image quantity and high cut off frequencies increase the image resolution but insufficient noise suppression. The purpose of this study was to determines the optimam cut off level with comparison of FWHM according to cut off level in each kiters-Band-limited, Sheep-logan, Sheep-logan Hanning, Generalized Hamming, Low pass cosine, Parazen and Butterworth filter in SPECT camera. We recorded image along the X, Y, Z-axis with $^{99m}TcO_4$ point source and measured FWHM by use profile curve. We find averaged length is $9.16\;mm{\sim}18.14\;mm$ of FWHM in X, Y, and Z-axis, and Band-limited and Generalized Hamming filters measures 9.16 mm at 0.7 cycle/pixel cut off frequency.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.941-950
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• 2008

The quality of projector output image is influenced by the surrounding conditions such as the shape and color of screen, and environmental light. Therefore, techniques that ensure desirable image quality, regardless of such surrounding conditions, have been in demand and are being steadily developed. Among the techniques, radiometric compensation is a representative one. In general, radiometric compensation is achieved by measuring the color of the screen and environmental light based on an analysis of camera image of projector output image and then adjusting the color of projector input image in a pixel-wise manner. This process is not time-consuming for small sizes of images but the speed of the process drops linearly with respect to image size. In large sizes of images, therefore, reducing the time required for performing the process becomes a critical problem. Therefore, this paper proposes a fast radiometric compensation method. The method uses color filters for eliminating the color mixing between projector and camera because the speed of radiometric compensation depends mainly on measuring color mixing between projector and camera. By using color filters, there is no need to measure the color mixing. Through experiments, the proposed method improved the compensation speed by 44 percent while maintaining the projector output image quality. This method is expected to be a key technique for widespread use of projectors for large-scale and high-quality display.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.438-456
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• 2010

Iris recognition uses a unique iris pattern of user to identify person. In order to enhance the performance of iris recognition, it is reported that the diameter of iris region should be greater than 200 pixels in the captured iris image. So, the previous iris system used zoom lens camera, which can increase the size and cost of system. To overcome these problems, we propose a new method of enhancing the accuracy of iris recognition on low-resolution iris images which are captured without a zoom lens. This research is novel in the following two ways compared to previous works. First, this research is the first one to analyze the performance degradation of iris recognition according to the decrease of the image resolution by excluding other factors such as image blurring and the occlusion of eyelid and eyelash. Second, in order to restore a high-resolution iris image from single low-resolution one, we propose a new method based on multiple multi-layered perceptrons (MLPs) which are trained according to the edge direction of iris patterns. From that, the accuracy of iris recognition with the restored images was much enhanced. Experimental results showed that when the iris images down-sampled by 6% compared to the original image were restored into the high resolution ones by using the proposed method, the EER of iris recognition was reduced as much as 0.133% (1.485% - 1.352%) in comparison with that by using bi-linear interpolation