• Management & Information Systems Review
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• v.36 no.4
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• pp.33-51
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• 2017

With the advent of 'The Forth Industrial Revolution' and the growing demand for quality of life due to economic growth, needs for the quality of medical services are increasing. Artificial intelligence has been introduced in the medical field, but it is rarely used in chronic skin diseases that directly affect the quality of life. Also, atopic dermatitis, a representative disease among chronic skin diseases, has a disadvantage in that it is difficult to make an objective diagnosis of the severity of lesions. The aim of this study is to establish an intelligent severity recognition model of atopic dermatitis for improving the quality of patient's life. For this, the following steps were performed. First, image data of patients with atopic dermatitis were collected from the Catholic University of Korea Seoul Saint Mary's Hospital. Refinement and labeling were performed on the collected image data to obtain training and verification data that suitable for the objective intelligent atopic dermatitis severity recognition model. Second, learning and verification of various CNN algorithms are performed to select an image recognition algorithm that suitable for the objective intelligent atopic dermatitis severity recognition model. Experimental results showed that 'ResNet V1 101' and 'ResNet V2 50' were measured the highest performance with Erythema and Excoriation over 90% accuracy, and 'VGG-NET' was measured 89% accuracy lower than the two lesions due to lack of training data. The proposed methodology demonstrates that the image recognition algorithm has high performance not only in the field of object recognition but also in the medical field requiring expert knowledge. In addition, this study is expected to be highly applicable in the field of atopic dermatitis due to it uses image data of actual atopic dermatitis patients.

• Journal of the Korean Graphic Arts Communication Society
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• v.21 no.2
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• pp.43-53
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• 2003

As information-communication network has been developed rapidly, internet users' circumstances also have been changed for the better, in result, more information can be applied than before. At this moment, there are many differences between real color and reappeared color on the CRT. When we observe a material object, our eyes perceive the multiplied form of light sources and nature spectral reflection. However, when the photographed signal is reappeared, illumination at that time of photographing and spectral reflection of a material object are converted into signal, and this converted RGB signal is observed on the CRT under another illumination. At this time, RGB signal is the reflected result of illumination at that time of photographing Therefore, this signal is influenced by the illumination at present, so it can be perceived another color. To accord the colro reflections of another color source, the study has been reported by S.C.Ahn$^{[1]}$, which study is about the color reapperarance system using neuron network. Furthermore, color reappearing method become independent of its circumstances has been reported by Y.Miyake$^{[2]}$. This method can make the same illuminations even if the observe circumstances are changed. To assume the light sources of observe circumstances, the study about color reappearing system using CCD sensor also have been studied by S.C.Ahn$^{[3]}$. In these studies, a population is fixed, first, on ab coordinates of CIE L${\ast}$a${\ast}$b${\ast}$. Then, color reappearing can be possible using every population and existing digital camera. However, the color is changed curvedly, not straightly, according to value's changes on the ab coordinates of CIE L${\ast}$a${\ast}$b. To solve these problems in this study, first of all, Labeling techniques are introduced. Next, basis color-it is based on Munsell color system-is divided into 10 color fields. And then, 4 special color- skin color, grass color, sky color, and gray-are added to the basis color. Finally, 14 color fields are fixed. After analyzing of the principle elements of new-defined-color fields' population, utility value and propriety value are going to be examined in 3-Band system from now on.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.764-781
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• 2013

In this paper, we propose a highly precise vehicle detection method with low false alarm using billboard sweep stereo matching and multi-stage hypothesis generation. First, we capture stereo images from cameras established in front of the vehicle and obtain the disparity map in which the regions of ground plane or background are removed using billboard sweep stereo matching algorithm. And then, we perform the vehicle detection and tracking on the labeled disparity map. The vehicle detection and tracking consists of three steps. In the learning step, the SVM(support vector machine) classifier is obtained using the features extracted from the gabor filter. The second step is the vehicle detection which performs the sobel edge detection in the image of the left camera and extracts candidates of the vehicle using edge image and billboard sweep stereo disparity map. The final step is the vehicle tracking using template matching in the next frame. Removal process of the tracking regions improves the system performance in the candidate region of the vehicle on the succeeding frames.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.157-163
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• 2017

The object segmentation approach has the merit of reducing the processing cost required to detect moths of interest, because it applies a moth detection algorithm to the segmented objects after segmenting the objects individually in the moth image. In this paper, an object segmentation method for moth detection in pheromone trap images is proposed. Our method consists of preprocessing, thresholding, morphological filtering, and object labeling processes. Thresholding in the process is a critical step significantly influencing the performance of object segmentation. The proposed method can threshold very elaborately by reflecting the local properties of the moth images. We performed thresholding using global and local versions of Ostu's method and, used the proposed method for the moth images of Carposina sasakii acquired on a pheromone trap placed in an orchard. It was demonstrated that the proposed method could reflect the properties of light and background on the moth images. Also, we performed object segmentation and moth classification for Carposina sasakii images, where the latter process used an SVM classifier with training and classification steps. In the experiments, the proposed method performed the detection of Carposina sasakii for 10 moth images and achieved an average detection rate of 95% of them. Therefore, it was shown that the proposed technique is an effective monitoring method of Carposina sasakii in an orchard.

• The Korean Journal of Nuclear Medicine
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• v.37 no.3
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• pp.162-170
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• 2003

The probabilistic anatomical maps are used to localize the functional neuro-images and morphological variability. The quantitative indicator is very important to inquire the anatomical position of an activated legion because functional image data has the low-resolution nature and no inherent anatomical information. Although previously developed MNI probabilistic anatomical map was enough to localize the data, it was not suitable for the Korean brains because of the morphological difference between Occidental and Oriental. In this study, we develop a probabilistic anatomical map for Korean normal brain. Normal 75 blains of T1-weighted spoiled gradient echo magnetic resonance images were acquired on a 1.5-T GESIGNA scanner. Then, a standard brain is selected in the group through a clinician searches a brain of the average property in the Talairach coordinate system. With the standard brain, an anatomist delineates 89 regions of interest (ROI) parcellating cortical and subcortical areas. The parcellated ROIs of the standard are warped and overlapped into each brain by maximizing intensity similarity. And every brain is automatically labeledwith the registered ROIs. Each of the same-labeled region is linearly normalize to the standard brain, and the occurrence of each legion is counted. Finally, 89 probabilistic ROI volumes are generated. This paper presents a probabilistic anatomical map for localizing the functional and structural analysis of Korean normal brain. In the future, we'll develop the group specific probabilistic anatomical maps of OCD and schizophrenia disease.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.256-262
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• 2004

A key component of an autonomous mobile robot is to localize itself and build a map of the environment simultaneously. In this paper, we propose a vision-based localization and mapping algorithm of mobile robot using fisheye lens. To acquire high-level features with scale invariance, a camera with fisheye lens facing toward to ceiling is attached to the robot. These features are used in mP building and localization. As a preprocessing, input image from fisheye lens is calibrated to remove radial distortion and then labeling and convex hull techniques are used to segment ceiling and wall region for the calibrated image. At the initial map building process, features we calculated for each segmented region and stored in map database. Features are continuously calculated for sequential input images and matched to the map. n some features are not matched, those features are added to the map. This map matching and updating process is continued until map building process is finished, Localization is used in map building process and searching the location of the robot on the map. The calculated features at the position of the robot are matched to the existing map to estimate the real position of the robot, and map building database is updated at the same time. By the proposed method, the elapsed time for map building is within 2 minutes for 50㎡ region, the positioning accuracy is ±13cm and the error about the positioning angle of the robot is ±3 degree for localization.

• Korean Journal of Remote Sensing
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• v.15 no.2
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• pp.147-158
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• 1999

Remote sensing technique has offered better understanding of our environment for the decades by providing useful level of information on the landcover. In many applications using the remotely sensed data, digital image processing methodology has been usefully employed to characterize the features in the data and develop the models. Random field models, especially Markov Random Field (MRF) models exploiting spatial relationships, are successfully utilized in many problems such as texture modeling, region labeling and so on. Usually, remotely sensed imagery are very large in nature and the data increase greatly in the problem requiring temporal data over time period. The time required to process increasing larger images is not linear. In this study, the methodology to reduce the computational cost is investigated in the utilization of the Markov Random Field. For this, multiresolution framework is explored which provides convenient and efficient structures for the transition between the local and global features. The computational requirements for parameter estimation of the MRF model also become excessive as image size increases. A Bayesian approach is investigated as an alternative estimation method to reduce the computational burden in estimation of the parameters of large images.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.183-192
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• 2023

The remotely sensed data, such as satellite imagery and aerial photos, can be used to extract and detect some objects in the image through image interpretation and processing techniques. Significantly, the possibility for utilizing digital map updating and land monitoring has been increased through automatic object detection since spatial resolution of remotely sensed data has improved and technologies about deep learning have been developed. In this paper, we tried to extract plastic greenhouses into aerial orthophotos by using fully convolutional densely connected convolutional network (FC-DenseNet), one of the representative deep learning models for semantic segmentation. Then, a quantitative analysis of extraction results had performed. Using the farm map of the Ministry of Agriculture, Food and Rural Affairsin Korea, training data was generated by labeling plastic greenhouses into Damyang and Miryang areas. And then, FC-DenseNet was trained through a training dataset. To apply the deep learning model in the remotely sensed imagery, instance norm, which can maintain the spectral characteristics of bands, was used as normalization. In addition, optimal weights for each band were determined by adding attention modules in the deep learning model. In the experiments, it was found that a deep learning model can extract plastic greenhouses. These results can be applied to digital map updating of Farm-map and landcover maps.