• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1448-1455
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• 1990

In this paper, a range image segmentation method is proposed. This method consists of an initial segmentation stage by discontinuous edge detection and surface type labeling based on the sign of the principal curvatures. Initially type labeled image is oversegmented, this image is merged via stepwise optimal region merging stage based on polynomial function approxiamtion. The successful segmentation results are presented for two synthetic range images with noise and a real-world ERIM range image.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.123-129
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• 1994

The segmentation of range image is essential to recognize the three dimensional object. Generally, surface curvature is well-known feature for segmentation and classification of the fange image, but it is sensitive to noies. In this paper, we propose the structure of hierarchical neural network using surface curvature for segmentation of range images. The hierarchical structure of neural networks is robust to noise and the result of segmentaion is better than conventional optimization method of single level.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.103-111
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• 2020

Liver cancer is the most fatal cancer that occurs worldwide. In order to diagnose liver cancer, the patient's physical condition was checked by using a CT technique using radiation. Segmentation was needed to diagnose the liver on the patient's abdominal CT scan, which the radiologists had to do manually, which caused tremendous time and human mistakes. In order to automate, researchers attempted segmentation using image segmentation algorithms in computer vision field, but it was still time-consuming because of the interactive based and the setting value. To reduce time and to get more accurate segmentation, researchers have begun to attempt to segment the liver in CT images using CNNs, which show significant performance in various computer vision fields. The pixel value, or numerical value, of the CT image is called the Hounsfield Unit (HU) value, which is a relative representation of the transmittance of radiation, and usually ranges from about -2000 to 2000. In general, deep learning researchers reduce or limit this range and use it for training to remove noise and focus on the target organ. Here, we observed that the range of HU values was limited in many studies but different in various liver segmentation studies, and assumed that performance could vary depending on the HU range. In this paper, we propose the possibility of considering HU value range as a hyper parameter. U-Net and ResUNet were used to compare and experiment with different HU range limit preprocessing of CHAOS dataset under limited conditions. As a result, it was confirmed that the results are different depending on the HU range. This proves that the range limiting the HU value itself can be a hyper parameter, which means that there are HU ranges that can provide optimal performance for various models.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.111-123
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• 1996

In this paper, a new edge-based segmentation algorithm for range image using pseudo reflectance images (PRIs) is proposed. A model of pseudo reflectance which is useful in analyzing three dimensional scene and objects is introduced and then three PRIs are generated by the model. For generating three PRIs, bels and jain's differential window operator is selected and three different light source directions are determined. Three edge images are extracted from each PRI and a fused (logical ORing) edge image is constructed for the benefit of enhanced edge formation. The final segmentation results of the proposed algoritm are obtained after the processing of thinning, labeling and correcting erroeneous regions with the fused edge image. The good performance of edge detection and segmentation is confirmed via computer simulation with synthetic and real range images.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.8
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• pp.1074-1084
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• 1995

In this paper, we investigate edge extraction and segmentation of range images. We first discuss problems that arise in the conventional region-based segmentation methods and edge-based ones using principal curvatures, then we propose an edge-based algorithm. In the proposed algorithm, we extract edge contours by using the Gaussian filter and directional derivatives, and segment a range image based on extracted edge contours, Also we present the problem that arises in the conventional thresholding, then we propose a new threshold selection method. To solve the problem that local maxima of the first- and second- order derivatives gather near step edges, we first find closed roof edge contours, fill the step edge region, and finally thin edge boundaries. Computer simulations with several range images show that the proposed method yields better performance than the conventional one.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.1
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• pp.120-129
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• 1990

In this paper, 3-dimensional object segmentation and classification are proposed. Planar object is segmented surface using jump boundary and internal boundary. Curved object is segmented surfaces by maximin clustering method. Segmented surfaces are classified by depth trends and angle measurement of normal vectors. Classified surfaces are merged according to adjacent surfaces and compared to Guassian curvature and mean curvature method. The proposed methods have been successfully applied to the synthetic range images and shows good classification.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.8
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• pp.1275-1283
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• 1990

Perception of surfaces from range images plays a key role in 3-D object recognition. Recognition of 3-D objects from range images is performed by matching the perceived surface descriptions with stored object models. The first step of the 3-d object recognition from range images is image segmentation. In this paper, an approach for segmenting 3-D range images into symbolic surface descriptions using spatial Gabor filter is proposed. Since the phase of data has a lot of important information, the phase information with magnitude information can effectively segment the range imagery into regions satisfying a common homogeneity criterion. The phase and magnitude of Gabor filter can represent a unique featur vector at a point of range data. As a result, range images are trnasformed into feature vectors in 3-parameter representation. The methods not only to extract meaningful features but also to classify a patch information from range images is presented.

• Journal of International Society for Simulation Surgery
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• v.4 no.1
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• pp.21-23
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• 2017

Purpose In this paper, we proposed alternate neighborhood system and reverse spread-direction approach for accurate and fast cellular automata-based image segmentation method. Materials and Methods On the basis of a simple but effective interactive image segmentation technique based on a cellular automaton, we propose an efficient algorithm by using Moore and designed neighborhood system alternately and reversing the direction of the reference pixels for spreading out to the surrounding pixels. Results In our experiments, the GrabCut database were used for evaluation. According to our experimental results, the proposed method allows cellular automata-based image segmentation method to faster while maintaining the segmentation quality. Conclusion Our results proved that proposed method improved accuracy and reduced computation time, and also could be applied to a large range of applications.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.70-78
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• 1992

In this paper, an approach for 3-D object segmentation and classification, which is based on eigen-values of polynomial function as their surface features, using neural network is proposed. The range images of 3-D objects are classified into surface primitives which are homogeneous in their intrinsic eigenvalue properties. The misclassified regions due to noise effect are merged into correct regions satisfying homogeneous constraints of Hopfield neural network. The proposed method has advantage of processing both segmentation and classification simultaneously.

• International Journal of Contents
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• v.8 no.1
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• pp.39-46
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• 2012

Character segmentation is a preprocessing step in many offline handwriting recognition systems. In this paper, Chinese characters are categorized into seven different structures. In each structure, the character size with the range of variations is estimated considering typical handwritten samples. The component removal and merge criteria are presented to remove punctuation symbols or to merge small components which are part of a character. Finally, the criteria for segmenting the adjacent characters concerning each other or overlapped are proposed.