• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.763-765
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• 2005

In the area of remote sensing, an immense number of images are continuously generated by various remote sensing systems. These images must then be managed by a database system efficient storage and retrieval. There are many types of image database systems, among which the content-based image retrieval (CBIR) system is the most advanced. CBIR utilizes the metadata of images including the feature data for indexing and searching images. Therefore, the performance of image retrieval is significantly affected by the storage method of the image metadata. There are many features of images such as color, texture, and shape. We mainly consider the shape feature because shape can be identified in any remote sensing while color does not always necessarily appear in some remote sensing. In this paper, we propose a metadata representation and storage method for image search based on shape features. First, we extend MPEG-7 to describe the shape features which are not defined in the MPEG-7 standard. Second, we design a storage schema for storing images and their metadata in a relational database system. Then, we propose an efficient storage method for managing the shape feature data using a Wavelet technique. Finally, we provide the performance results of our proposed storage method.

• Proceedings of the KSRS Conference
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.321-324
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• 2005

In these days, the management and visualization of 3D geo-spatial information is regarded as one of an important issue in GiS and remote sensing fields. 3D GIS is considered with the database issues such as handling and managing of 3D geometry/topology attributes, whereas 3D visualization is basically concerned with 3D computer graphics. This study focused on the design and implementation for the OpenGL API-based rendering system for the complex types of 3D geo-spatial features. In this approach 3D features can be separately processed with the functions of authoring and manipulation of terrain segments, building segments, road segments, and other geo-based things with texture mapping. Using this implementation, it is possible to the generation of an integrated scene with these complex types of 3D features. This integrated rendering system based on the feature-based 3D-GIS model can be extended and effectively applied to urban environment analysis, 3D virtual simulation and fly-by navigation in urban planning. Furthermore, we expect that 3D-GIS visualization application based on OpenGL API can be easily extended into a real-time mobile 3D-GIS system, soon after the release of OpenGLIES which stands for OpenGL for embedded system, though this topic is beyond the scope of this implementation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권5호
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• pp.910-924
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• 2010

As Internet usage has increased, the risk of adolescents being exposed to adult content and harmful information on the Internet has also risen. To help prevent adolescents accessing this content, a novel detection method for adult images is proposed. The proposed method involves three steps. First, the Image Of Interest (IOI) is extracted from the image background. Second, the IOI is distinguished from the segmented image using a novel weighting mask, and it is determined to be acceptable or unacceptable. Finally, the features (color and texture) of the IOI or original image are compared to a critical value; if they exceed that value then the image is deemed to be an adult image. A Receiver Operating Characteristic (ROC) curve analysis was performed to define this optimal critical value. And, the textural features are identified using a gray level co-occurrence matrix. The proposed method increased the precision level of detection by applying a novel weighting mask and a receiver operating characteristic curve. To demonstrate the effectiveness of the proposed method, 2850 adult and non-adult images were used for experimentation.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.703-715
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• 2017

Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray-level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

• KIPS Transactions on Software and Data Engineering
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• 제7권3호
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• pp.91-98
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• 2018

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. In particular, porcine respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this paper, we propose a noise-robust system for the early detection and recognition of pig wasting diseases using sound data. In this method, first we convert one-dimensional sound signals to two-dimensional gray-level images by normalization, and extract texture images by means of dominant neighborhood structure technique. Lastly, the texture features are then used as inputs of convolutional neural networks as an early anomaly detector and a respiratory disease classifier. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (low-cost sound sensor) and accurately (over 96% accuracy) even under noise-environmental conditions, either as a standalone solution or to complement known methods to obtain a more accurate solution.

• Biomedical Science Letters
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• 제18권4호
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• pp.399-405
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• 2012

The brain is the body's most organized and controlled organ, and it governs various psychological and mental functions. A brain abnormality could greatly affect one's physical and mental abilities, and consequently one's social life. Brain disorders can be broadly categorized into three main afflictions: stroke, brain tumor, and dementia. Among these, stroke is a common disease that occurs owing to a disorder in blood flow, and it is accompanied by a sudden loss of consciousness and motor paralysis. The main types of strokes are infarction and hemorrhage. The exact diagnosis and early treatment of an infarction are very important for the patient's prognosis and for the determination of the treatment direction. In this study, texture features were analyzed in order to develop a prototype auto-diagnostic system for infarction using computer auto-diagnostic software. The analysis results indicate that of the six parameters measured, the average brightness, average contrast, flatness, and uniformity show a high cognition rate whereas the degree of skewness and entropy show a low cognition rate. On the basis of these results, it was suggested that a digital CT image obtained using the computer auto-diagnostic software can be used to provide valuable information for general CT image auto-detection and diagnosis for pre-reading. This system is highly advantageous because it can achieve early diagnosis of the disease and it can be used as supplementary data in image reading. Further, it is expected to enable accurate medical image detection and reduced diagnostic time in final-reading.

• Journal of Digital Contents Society
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• 제18권6호
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• pp.1033-1040
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• 2017

Artists use artistic features of paintings to provide various emotions in paintings. These features may be simply color and texture, but they can move on to form a composition or a symmetry. Through these features, people can feel various emotions when enjoying paintings. Even though they are using these features, there are paintings that are not readily accessible to non-extractable experts. This is because the analysis of features is not intuitive. In this paper, we want to produce content that matches paintings and music. This helps user to understand painting easily with paintings and matched music.

• Journal of Korean Institute of Industrial Engineers
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• 제33권4호
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• pp.393-401
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• 2007

Affective aspects of design attributes such as color, Pattern, and texture are important to the overall impression and the success of interior products. Among all the interior materials, wallpapers and flooring materials take up largest construction area and they are main components in creating affective impression for customers. This study aims to investigate the relationship between luxuriousness and related affective variables and design elements of wallpapers and flooring materials. The approach consists of 3 steps: (1) selecting related affective features and product design attributes through a literature survey, opinion of expert panel, and focus group interview, (2) conducting evaluation experiments, and (3) developing Kansei models using multivariate statistical analysis and analyzing critical attributes. Evaluation experiment was conducted using a questionnaire made up of 7-point scale and 100-point scale and 30 housewives and 20 interior designers participated in the evaluation experiment. The result of evaluation was analyzed through principal component regression and quantification I analysis. As a result of analyzing the survey data, the relationship between luxuriousness and related affective features and product design attributes was identified, moreover a optimal combination of the design component was identified. Consequently, it is expected that the results of the study would be a basis of the concept of emotion-based design by giving insights about how customers perceive the luxuriousness and suggesting the optimal combination, and providing specific quantitative design guidelines.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 한국펄프종이공학회 2005년도 추계학술발표논문집
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• pp.45-53
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• 2005

A novel method for evaluating the anisotropy of the deterministic features in a stochastic 2D data is introduced. The ability of the wavelet transform for the identification of the abrupt discontinuities could be used to characterize the boundary of the deterministic area in a 2D stochastic data, such as flocs in paper structure. The one-dimensional wavelet transform with a small-scale range in MD and CD could quantify the amount of the edge in both directions, depending on the intensity of each floc. The flocs that are aligned in the MD direction result in a higher value of local wavelet energy in the CD direction. Therefore, the ratio of the total wavelet energy in CD and MD directions can be used as a new anisotropy index. This index is a measure of the floc-orientation and can provide an excellent tool to obtain the orientation distribution and the major oriented angle of flocs. Various simulated images and real stochastic data such as local gloss variation of printed image and formation image, have been tested and the results show this analysis method is very reliable to measure the anisotropy of the deterministic features.

• Journal of Korea Multimedia Society
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• 제7권7호
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• pp.877-885
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• 2004

In this paper, we propose an enhancement shot change detection method using the neural net and the robust feature selection out of multiple features. The previous shot change detection methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using robust features, which are supplementary each other, rather than using single feature. In this paper, we use the typical CART (classification and regression tree) of data mining method to select the robust features, and the backpropagation neural net to determine the threshold of the each selected features. And to evaluation the performance of the robust feature selection, we compare the proposed method to the PCA(principal component analysis) method of the typical feature selection. According to the experimental result. it was revealed that the performance of our method had better that than the PCA method.