• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.157-165
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• 2009

Existing methods for image segmentation using diffusion can't preserve contour information, or noises with high gradients become more salient as the umber of times of the diffusion increases, resulting in over-segmentation when applied to watershed. This thesis proposes a method for image segmentation by applying morphology operation together with robust anisotropic diffusion. For an input image, transformed into LUV color space, closing by reconstruction and anisotropic diffusion are applied to obtain a simplified image which preserves contour information with noises removed. With gradients computed from this simplifed images, watershed algorithm is applied. Experiments show that color images are segmented very effectively without over-segmentation.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.224-229
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• 2009

Digital image recognition has been used for steel bridge surface assessment since late 1990s. However, the non-uniform illumination problems such as shades, shadows, and highlights are still challenges in image processing to date. Therefore, this paper develops a new approach to tackle the non-uniform illumination problem for rust image adjustment. The inhomogeneous illumination problem is divided into shades/shadows and highlights in this paper. The proposed BEMD-morphology approach (BMA) utilizes the bidimensional empirical mode decomposition to mitigate the shade/shadow effect, and the morphological processing to detect and replace the highlight area. Finally, the rust image processed with the BMA will be segmented by the K-Means algorithm, one of the most popular and effective methods, to show the effectiveness of illumination adjustment.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.37-40
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• 2001

The most important issues in gesture recognition are the simplification of algorithm and the reduction of processing time. The mathematical morphology based on geometrical set theory is best used to perform the real-time processing. A key idea of the algorithm proposed in this paper is to apply morphological shape decomposition. The primitive elements extracted from a hand gesture have very important information including the directivity of the hand gestures. Based on this algorithm, we proposed the morphological hand-gesture recognition algorithm using feature vectors extracted from lines connecting the center points of a main-primitive element and sub-primitive elements. Through the experiments, we applied to the video contents browsing system with natural interactions and demonstrated the efficiency of this algorithm.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.1002-1005
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• 1995

This paper presents an enhanced preprocessing and recognition algorithm for automatic vehicle license plate recognition system. The algorithm first applies horizontal gradient filter followed by thresholding and mathematical morphology operation for preprocessing. The final stage of the preprocessing is the application of connected component analysis in order to estimate the license plate region. For the recognition of the serial numbers of the plates, we developed a very effective algorithm. We call this zerocrossing count algorithm. This paper presents a detail of this algorithm and compare the performance with a template matching algorithm which utilizes correlation coefficient.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.2
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• pp.85-90
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• 2004

This paper proposes the wavelet image coding using the significant cluster extraction by morphology and the adaptive quantization. In the conventional MRWD method, the additional seed data takes large potion of the total data bits. The proposed method extracts the significant cluster using morphology to improve the coding efficiency. In addition, the adaptive quantization is proposed to reduce the number of redundant comparative operations which are indispensably occurred in the MRWD quantization. The experimental result shows that the proposed algorithm has the improved coding efficiency and computational cost while preserving superior PSNR

• Journal of Korean Dental Science
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• v.16 no.2
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• pp.156-163
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• 2023

Purpose: The aim of this study was to evaluate the feasibility of deep learning techniques to classify the morphology and severity of peri-implantitis bone defects based on periapical radiographs. Materials and Methods: Based on a pre-trained and fine-tuned ResNet-50 deep learning algorithm, the morphology and severity of peri-implantitis bone defects on periapical radiographs were classified into six groups (class I/II and slight/moderate/severe). Accuracy, precision, recall, and F1 scores were calculated to measure accuracy. Result: A total of 971 dental images were included in this study. Deep-learning-based classification achieved an accuracy of 86.0% with precision, recall, and F1 score values of 84.45%, 81.22%, and 82.80%, respectively. Class II and moderate groups had the highest F1 scores (92.23%), whereas class I and severe groups had the lowest F1 scores (69.33%). Conclusion: The artificial intelligence-based deep learning technique is promising for classifying the morphology and severity of peri-implantitis. However, further studies are required to validate their feasibility in clinical practice.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1725-1731
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• 2004

The use of gestures provides an attractive alternate to cumbersome interface devices for human-computer interaction. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures. The most important issues in gesture recognition are the simplification of algorithm and the reduction of processing time. The mathematical morphology based on geometrical set theory is best used to perform the processing. A key idea of proposed algorithm in this paper is to apply morphological shape decomposition. The primitive elements extracted to a hand gesture include in very important information on the directivity of the hand gestures. Based on this characteristic, we proposed the morphological gesture recognition algorithm using feature vectors calculated to lines connecting the center points of a main-primitive element and sub-primitive elements. Through the experiment, we demonstrated the efficiency of proposed algorithm. Coupling natural interactions such as hand gesture with an appropriately designed interface is a valuable and powerful component in the building of TV switch navigating and video contents browsing system.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.81-92
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• 1999

In this paper, we propose a new image segmentation algorithm which can be use din object-based image coding applications such as MPGA-4. Since the conventional objet segmentation methods based on mathematical morphology tend to yield oversegmented results, they normally need a postprocess which merges small regions to obtain a larger one. To solve this oversegmentation problem, in this paper, we prosed a block-based segmentation algorithm that can identify large texture regions in the image. Also, by applying the watershed algorithm to the image blocks between the homogeneous regions, we can obtain the exact pixel-based contour. Experimental results show that the proposed algorithm yields larger segments, particularly in the textural area, and reduces the computational complexities.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.311-317
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• 2001

The study on Shilla monumental inscription has beer accomplished by many historians. However, the research on enhancement of monumental inscription image using digital image processing technique is not sufficient. The preprocessing using computer is needed fur accurate interpretation of history. In this paper, digital image enhancement algorithm based on mathematical morphology for noise reduction and character clearness is proposed. In the experiment, the subjective image quality is improved using the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.6
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• pp.1160-1169
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• 1997

This paper presents an algorithm to separate vowels from consonants in Korean characters captured in noisy environment andto recognize them. The algorithm has been originally developed for recognition of the usage code (which is represented by a single Korean character) in the license plates of Korean vehicles. It, however, could be easily adopted to other applications with minor changes, in which character recognition is needed and the environment is noisy. The key ideas of the algorithm are to localize the vowels utilizing Hough transformation and to separate the vowels from consonants utilizing mathematical morphology. We observed that the presented algorithm effectively separates vowels even if the vowels and consonants are joined together after thresholding. We also observed that our algorithm outperforms some conventional algorithms especially when the input images are noisy. The details of the comparison study are presented in the paper.