• Journal of the Korea Computer Graphics Society
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• v.2 no.2
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• pp.21-28
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• 1996

The nature of complexity of medical images makes them difficult to segment using standard techniques. Therefore the usual approaches to segment images continue to predominantly involve manual interaction. But it tediously consumes a good deal of time and efforts of the experts. Hereby a nonmanual parameters estimation which can replace the manual interaction is needed to solve the problem of redundant manual works for an image segmentation. This paper attempts to estimate parameters for an image region segmentation using Scale Space Filtering. This attempt results in estimating the number of regions, their boundary and each representatives to be segmented 2-dimensionally and 3-dimensionally. Using this algorithm, we may diminish the problem of wasted time and efforts for finding prerequisite segmentation parameters, and lead the relatively reasonable result of region segmentation.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1587-1598
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• 2000

For the object-based video compression at very low bit rate, vieo segmentation is an essential part. In this paper, we propose a temporal video segmentation algorithms for motion pictures which is based on blocks. The algorithm is composed of three steps: (1) the change-detection, (2) the block merging, and (3) the block segmentation. The first step separates the change-detected region from background. Here, a new method for removing the uncovered region without motion estimation is presented. The second step, which is further divided into three substeps, estimates motions for the change-detected region and merges blocks with similar motions. The merging conditions for each substep as criteria are also given. The final step, the block segmentation, segments the boundary block that is excluded from the second step on a pixel basis. After describing our algorithm in detail, several experimental results along the processing order are shown step by step. The results demonstrate that the proposed algorithm removes the uncovered region effectively and produced objects that are segmented well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1418-1433
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• 2019

In computer graphics, 3D mesh segmentation is a challenging research field. This paper presents a 3D mesh model segmentation algorithm that focuses on removing exterior salient parts from the original 3D mesh model based on prominent feature points and marching plane. To begin with, the proposed approach uses multi-dimensional scaling to extract prominent feature points that reside on the tips of each exterior salient part of a given mesh. Subsequently, a set of planes intersect the 3D mesh; one is the marching plane, which start marching from prominent feature points. Through the marching process, local cross sections between marching plane and 3D mesh are extracted, subsequently, its corresponding area are calculated to represent local volumes of the 3D mesh model. As the boundary region of an exterior salient part generally lies on the location at which the local volume suddenly changes greatly, we can simply cut this location with the marching plane to separate this part from the mesh. We evaluated our algorithm on the Princeton Segmentation Benchmark, and the evaluation results show that our algorithm works well for some categories.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.4
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• pp.327-336
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• 2007

The 3D building modeling is one of crucial components in building 3D geospatial information. The existing methods for 3D building modeling depend mainly on manual photogrammetric processes by stereoplotter compiler, which indeed take great amount of time and efforts. In addition, some automatic methods that were proposed in research papers and experimental trials have limitations of describing the details of buildings with lack of geometric accuracy. It is essential in automatic fashion that the boundary and shape of buildings should be drawn effortlessly by a sophisticated algorithm. In recent years, airborne LiDAR data representing earth surface in 3D has been utilized in many different fields. However, it is still in technical difficulties for clean and correct boundary extraction without human intervention. The usage of airborne LiDAR data will be much feasible to reconstruct the roof tops of buildings whose boundary lines could be taken out from existing digital maps. The paper proposed a method to reconstruct the roof tops of buildings using airborne LiDAR data with building boundary lines from digital map. The primary process is to perform octree-based segmentation to airborne LiDAR data recursively in 3D space till there are no more airborne LiDAR points to be segmented. Once the octree-based segmentation has been completed, each segmented patch is thereafter merged based on geometric spatial characteristics. The experimental results showed that the proposed method were capable of extracting various building roof components such as plane, gable, polyhedric and curved surface.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.163-174
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• 2004

The 3D tooth model in which each tooth can be manipulated individualy is essential component for the orthodontic simulation and implant simulation in dental field. For the reconstruction of such a tooth model, we need an image segmentation algorithm capable of separating individual tooth from neighboring teeth and alveolar bone. In this paper we propose a CT image normalization method and adaptive optimal thresholding algorithm for the segmenation of tooth region in CT image slices. The proposed segmentation algorithm is based on the fact that the shape and intensity of tooth change gradually among CT image slices. It generates temporary boundary of a tooth by using the threshold value estimated in the previous imge slice, and compute histograms for the inner region and the outer region seperated by the temporary boundary. The optimal threshold value generating the finnal tooth region is computed based on these two histogram.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.303-309
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• 2004

In this paper, an adaptive Bayesian approach to image segmentation was developed for boundary detection. Both image intensities and texture information were used for obtaining better quality of the image segmentation by using the C programming language. Fuzzy c-mean clustering was applied fer the conditional probability density function, and Gibbs random field model was used for the prior probability density function. To simply test the algorithm, a synthetic image (256$\times$256) with a set of low gray values (50, 100, 150 and 200) was created and normalized between 0 and 1 n double precision. Results have been presented that demonstrate the effectiveness of the algorithm in segmenting the synthetic image, resulting in more than 99% accuracy when noise characteristics are correctly modeled. The algorithm was applied to the Antarctic mosaic that was generated using 1963 Declassified Intelligence Satellite Photographs. The accuracy of the resulting vector map was estimated about 300-m.

• Journal of Broadcast Engineering
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• v.13 no.2
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• pp.236-244
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• 2008

We propose a new building detection and description algorithm for Lidar data and photogrammetric imagery using color segmentation, line segments matching, perceptual grouping. Our algorithm consists of two steps. In the first step, from the initial building regions extracted from Lidar data and the color segmentation results from the photogrammetric imagery, we extract coarse building boundaries based on the Lidar results with split and merge technique from aerial imagery. In the secondstep, we extract precise building boundaries based on coarse building boundaries and edges from aerial imagery using line segments matching and perceptual grouping. The contribution of this algorithm is that color information in photogrammetric imagery is used to complement collapsed building boundaries obtained by Lidar. Moreover, linearity of the edges and construction of closed roof form are used to reflect the characteristic of man-made object. Experimental results on multisensor data demonstrate that the proposed algorithm produces more accurate and reliable results than Lidar sensor.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.141-144
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• 2008

Many studies have been conducted on extracting buildings from ALS(Airborne Laser Scanning) data. After segmentation or classification of building points, additional steps such as generalization is required to get straight boundary lines that better approximate the real ones. In much research, orthogonal constraints are used to improve accuracies and qualities. All the lines of the building boundaries are assumed to be either parallel or perpendicular mutually. However, this assumption is not valid in many cases and more complex shapes of buildings have been increased. A new algorithm is presented that is applicable to various complex buildings. It consists of three steps of boundary tracing, grouping, and regularization. The performance of our approach was evaluated by applying the algorithm to some buildings and the results showed that our proposed method has good potential for extracting building boundaries of various shapes.

• ETRI Journal
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• v.33 no.4
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• pp.547-557
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• 2011

This study presents an approach for extracting boundaries of various buildings, which have concave boundaries, inner yards, non-right-angled corners, and nonlinear edges. The approach comprises four steps: building point segmentation, boundary tracing, boundary grouping, and regularization. In the second and third steps, conventional algorithms are improved for more accurate boundary extraction, and in the final step, a new algorithm is presented to extract nonlinear edges. The unique characteristics of airborne light detection and ranging (LIDAR) data are considered in some steps. The performance and practicality of the presented algorithm were evaluated for buildings of various shapes, and the average omission and commission error of building polygon areas were 0.038 and 0.033, respectively.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.342-352
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• 2014

This paper proposes an automatic segmentation method of cortical bone and trabecular bone and describes an implementation of structural analysis method of trabecular bone in micro-CT images. The proposed segmentation method extract bone region with binarization using a threshold value. Next, it finds adjacent contour lines from outer boundary line into inward direction and sets candidate regions of cortical bone. Next it remove cortical bone region by finding the candidate cortical region of which the average pixel value is maximum. We implemented the method which computes four structural indicators BV/TV, Tb.Th, Tb.Sp, Tb.N by using VTK(Visualization ToolKit) and sphere fitting algorithm. We applied the implemented method to twenty proximal femur of mouses and compared with the manual segmentation method. Experimental result shows that the average error rates between the proposed segmentation method and the manual segmentation method are less than 3% for the four structural indicatiors. This result means that the proposed method can be used instead of the combersome and time consuming manual segmentation method.