• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.209-214
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• 2010

This paper proposes an algorithm of segmentation and title detection for document image. The automated title detection method that we have developed is composed of two phases, segmentation and title area detection. In the first phase, we extract and segment the document image. To perform this operation, the binary map is segmented by combination of morphological operation and CCA(connected component algorithm). The first phase provides segmented regions that would be detected as title area for the second stage. Candidate title areas are detected using geometric information, then we can extract the title region that is performed by removing non-title regions. After classification step that removes non-text regions, projection is performed to detect a title region. From the fact that usually the largest font is used for the title in the document, horizontal projection is performed within text areas. In this paper, we proposed a method of segmentation and title detection for various forms of document images using geometric features and projection profile analysis. The proposed system is expected to have various applications, such as document title recognition, multimedia data searching, real-time image processing and so on.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.687-690
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• 2015

Forward collision warning systems(FCWS) and lane change assist systems(LCAS) need regions of interest for detecting lanes and objects as road regions. Watershed segmentation is effective algorithm that classify the road. That algorithm is split results appear differently depending on Watershed line with local minimum in the early part of the seed. If not road regions or vehicles combined the road's seed, It segment road with the others. For compensate the that defect, It has to adaptive change by road environment. The method is that image segmentate the several of regions of interest. Then It is set in a straight line that is detected in regions of interest. If It was detected cars on seed, seed is adjusted the location. And If It wasn't include the line, seed is adjusted the length for final decision the seed. We can detect the road region using the final seed that selected according to the road environment.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.29-35
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• 2010

While diversification of the use of video in the prevalence of cheap video equipment, augmented reality can print additional real-world images and video image. Although many recent advent augmented reality techniques, currently attempting to correct the character recognition is performed. In this paper characters marked with a visual marker recognition, and the color to match the marker color of the characters finds. And, it was shown on the screen by the character recognition. In this paper, by applying the phoneme pattern segmentation algorithm by the horizontal projection, we propose to segment the phoneme to match the six types of Hangul representation. Throughout the experiment sample of phoneme segmentation using augmented reality showed proceeding result at each step, and the experimental results was found to be that detection rate was above 90%.

• Journal of the Korea Institute of Building Construction
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• v.15 no.3
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• pp.329-337
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• 2015

Drawings in construction industry are an aggregation of knowledge containing various information generated in a number of projects. Finish details are generated by architects when they select details and methods of construction by spaces or elements. However it is so difficult to re-use details in existing drawing management systems because there are so many details in a sheet and there are several many kinds of sheets in a file. In this paper, a drawing segmentation algorithm is suggested for managing building finish details individually. Based on this algorithm, a system module for making detail blocks from files of finish details is developed. Using the drawing segmentation module, interfaces of drawing management system are suggested in the paper. Management of individual detail blocks helps to reduce of the time for searching and increases productivity of designed drawings. Also it would contribute to increase quality of designed drawings by reusing of knowledge in detail blocks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1100-1105
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• 2006

Severe distortions of colors in the obtained images have made it difficult for color codes to expand their applications. To reduce the effect of color distortions on reading colors, it will be more desirable to statistically process as many pixels in the individual color region as possible, than relying on some regularly sampled pixels. This process may require segmentation, which usually requires edge detection. However, edges in color codes can be disconnected due tovarious distortions such as zipper effect and reflection, to name a few, making segmentation incomplete. Edge linking is also a difficult process. In this paper, a more efficient approach to reducing the effect of color distortions on reading colors, one that excludes precise edge detection for segmentation, was obtained by employing the k-means clustering algorithm. And, in detecting color codes, the properties of both six safe colors and grays were utilized. Experiments were conducted on 144, 4M-pixel, outdoor images. The proposed method resulted in a color-code detection rate of 100% fur the test images, and an average color-reading accuracy of over 99% for the detected codes, while the highest accuracy that could be achieved with an approach employing Canny edge detection was 91.28%.

• Journal of KIISE:Software and Applications
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• v.27 no.8
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• pp.815-826
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• 2000

This study is for the segmentation and volume calculation of the white matter and gray matter from brain MRI. In general, the volume of white and gray matter is reduced by contraction of each components in the case of mental retardation which are Alzheimer's disease and Down's syndrome. As results, it is useful for diagnostic and early detection for various mental retardation through the tracing of variation for its volume from the brain MRI. But, until now, it was very difficult to calculate the partial volume of each components existing in some thickness, because MR image was represented by single gray value after scanning by MR scanner. Accordingly, new segmentation algorithm proposed in this paper is to calculate the partial volume of the white and gray matter existing in some thickness through the analysis of the blurred gray value, and is to determine the threshold for segmentation of white and gray matter, and is to calculate the volume of each segmented component. And finally, proposed algorithm was applied the models which was created manually, and then acquired results was compared with that of original model.

• Korean Journal of Remote Sensing
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• v.22 no.6
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• pp.601-612
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• 2006

This study proposes RAG (Region Adjancency Graph)-based image segmentation for large imagery in remote sensing. The proposed algorithm uses CN-chain linking for computational efficiency and multi-window operation of sliding structure for memory efficiency. Region-merging due to RAG is a process to find an edge of the best merge and update the graph according to the merge. The CN-chain linking constructs a chain of the closest neighbors and finds the edge for merging two adjacent regions. It makes the computation time increase as much as an exact multiple in the increasement of image size. An RNV (Regional Neighbor Vector) is used to update the RAG according to the change in image configuration due to merging at each step. The analysis of large images requires an enormous amount of computational memory. The proposed sliding multi-window operation with horizontal structure considerably the memory capacity required for the analysis and then make it possible to apply the RAG-based segmentation for very large images. In this study, the proposed algorithm has been extensively evaluated using simulated images and the results have shown its potentiality for the application of remotely-sensed imagery.

• Journal of Biomedical Engineering Research
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• v.40 no.5
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• pp.179-188
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• 2019

In ultrasound telemedicine, it is important to reduce the size of the data by compressing the ultrasound image when sending it. Ultrasound images can be divided into image context and other information consisting of patient ID, date, and several letters. Between them, ultrasound context is very important information for diagnosis and should be securely preserved as much as possible. In several previous papers, ultrasound compression methods were proposed to compress ultrasound context and other information into different compression parameters. This ultrasound compression method minimized the loss of ultrasound context while greatly compressing other information. This paper proposed the method of automatic segmentation of ultrasound context to overcome the limitation of the previously described ultrasound compression method. This algorithm was designed to robust for various ultrasound device and to enable real-time operation to maintain the benefits of ultrasound imaging machine. The operation time of extracting ultrasound context through the proposed segmentation method was measured, and it took 311.11 ms. In order to optimize the algorithm, the ultrasound context was segmented with down sampled input image. When the resolution of the input image was reduced by half, the computational time was 126.84 ms. When the resolution was reduced by one-third, it took 45.83 ms to segment the ultrasound context. As a result, we verified through experiments that the proposed method works in real time.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.29-35
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• 2021

License plate recognition plays a key role in intelligent transportation systems. Therefore, it is a very important process to efficiently detect the number and character areas. In this paper, we propose a method to effectively detect license plate number area by applying deep learning and semantic image segmentation algorithm. The proposed method is an algorithm that detects number and text areas directly from the license plate without preprocessing such as pixel projection. The license plate image was acquired from a fixed camera installed on the road, and was used in various real situations taking into account both weather and lighting changes. The input images was normalized to reduce the color change, and the deep learning neural networks used in the experiment were Vgg16, Vgg19, ResNet18, and ResNet50. To examine the performance of the proposed method, we experimented with 500 license plate images. 300 sheets were used for learning and 200 sheets were used for testing. As a result of computer simulation, it was the best when using ResNet50, and 95.77% accuracy was obtained.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.792-799
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• 2022

Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.