• Annual Conference of KIPS
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• 2009.11a
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• pp.345-346
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• 2009

The segmentation of vessel including portal vein, hepatic vein and artery, from Computed Tomography (CT) images plays an important role in the therapeutic strategies for hepatic diseases. Representing segmented vessels in three dimensional spaces is extremely useful for doctors to plan liver surgery. In this paper, proposed method is focused on smoothing technique of segmented 3D liver vessels, which derived from 3D region growing approach. A pixel expand algorithm has been developed first to avoid vessel lose and disconnection cased by the next smoothing technique. And then a binary volume filtering technique has been implemented and applied to make the segmented binary vessel volume qualitatively smoother. This strategy uses an iterative relaxation process to extract isosurfaces from binary volumes while retaining anatomical structure and important features in the volume. Hard and irregular place in volume image has been eliminated as shown in the result part, which also demonstrated that proposed method is a suitable smoothing solution for post processing of fine vessel segmentation.

• Imaging Science in Dentistry
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• v.48 no.4
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• pp.261-268
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• 2018

Purpose: To determine the impact of an image processing technique on diagnostic accuracy of digital panoramic radiographs for the assessment of anatomical structures in paediatric patients with mixed dentition. Materials and Methods: The study consisted of 50 digital panoramic radiographs of children aged from 6 to 12 years, which were later on processed using a dedicated image processing method. A modified clinical image quality evaluation chart was used to evaluate the diagnostic accuracy of anatomical structures in maxillary and mandibular anterior and maxillary premolar region of processed images. Results: A statistically significant difference was observed between pre and post-processed evaluation of anatomical structures(P<0.05) in the maxillary and mandibular anterior region. The anterior region was found to be more accurate in post-processed images. No significant difference was observed in the maxillary premolar region (P>0.05). The Inter-observer and intra-observer reliability of both pre and post processed images were excellent (>0.82) for anterior region and good (>0.63) for premolar region. Conclusion: The application of image processing technique in digital panoramic radiography can be considered a reliable method for improving the quality of anatomical structures in paediatric patients with mixed dentition.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.407-414
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• 2014

The aim of the present study was to carry out quantitative analysis of spatial resolution for the influence of the focus size and digital image post-processing on the Computed Radiography (CR). The modulation transfer functions of an edge measuring method (MTF) was used for the evaluation of the spatial resolution. The focus size of X-ray tube was used the small focus (0.6 mm) and the large focus (1.2 mm). We evaluated the 50% and 10% of MTF for the enhancement of edge and contrast by using multi-scale image contrast amplification (MUSICA) in digital image post-processing. As a results, the edge enhancement than the contrast enhancement were significantly higher the spatial resolution of MTF 50% in all focus. Also the spatial resolution of the obtained images in a large focus were improved by digital image processing. In conclusion, the results of this study should serve as a basic data for obtain the high resolution clinical images, such as skeletal and chest images on the CR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1700-1721
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• 2017

The problem of blocking artifacts is very common in block-based image and video compression, especially at very low bit rates. In this paper, we propose a post-processing method for JPEG-coded image deblocking via sparse representation and adaptive residual threshold. This method includes three steps. First, we obtain the dictionary by online dictionary learning and the compressed images. The dictionary is then modified by the histogram of oriented gradient (HOG) feature descriptor and K-means cluster. Second, an adaptive residual threshold for orthogonal matching pursuit (OMP) is proposed and used for sparse coding by combining blind image blocking assessment. At last, to take advantage of human visual system (HVS), the edge regions of the obtained deblocked image can be further modified by the edge regions of the compressed image. The experimental results show that our proposed method can keep the image more texture and edge information while reducing the image blocking artifacts.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.413-419
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• 2014

In this paper, we develop an automatic coin calculation system by using digital image processing. Existing schemes have the problem that is not able to exclude non-circular shape from the calculation. We propose a method to detect only coins which have circular form by applying the circular Hough transform(CHT). However, the CHT has the drawback that detects multiple circles even for just one coin because of shadow noise, the patterns on coins, and non-circular edge detection. We propose a post processing algorithm to overcome these limitations. The proposed system was implemented and successfully calculated the coin amount in the case that non-circular objects are mixed with coins.

• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.117-122
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• 2009

We developed a multi-lingual namecard recognizer for building up a global client management systems. At first, we created the Unicode-based character image database for character recognition and learning of multi languages, and applied many color image processing techniques to get more correct data for namecard images which were acquired by various input devices. And by applying multi-layer perceptron neural network, individual character recognition applied for language types, and post-processing utilizing keyword databases made for individual languages, we increased a recognition rate for multi-lingual namecards.

• Spatial Information Research
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• v.19 no.5
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• pp.1-11
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• 2011

This study mainly focused on the method of accurately extracting damage information in the im agery change detection process using the constructed high resolution aerial im agery. Bongwha-gun in Gyungsangbuk-do which had been severely damaged from a localized torrential downpour at the end of July, 2008 was selected as study area. This study utilized aerial im agery having photographing scale of 30cm gray image of pre-disaster and 40cm color image of post-disaster. In order to correct errors from the differences of the image resolution of pre-/post-disaster and time series, the prelim inary phase of image processing techniques such as normalizing, contrast enhancement and equalizing were applied to reduce errors. The extent of the damage was calculated using one to one comparison of the intensity of each pixel of pre-/post-disaster im aged. In this step, threshold values which facilitate to extract the extent that damage investigator wants were applied by setting difference values of the intensity of pixel of pre-/post-disaster. The accuracy of optimal image processing and the result of threshold values were verified using the error matrix. The results of the study enabled the early exaction of the extents of the damages using the aerial imagery with identical characteristics. It was also possible to apply to various damage items for imagery change detection in case of utilizing multi-band im agery. Furthermore, more quantitative estimation of the dam ages would be possible with the use of numerous GIS layers such as land cover and cadastral maps.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.531-538
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• 2016

In this paper, we propose a novel post-processing method of semantic segmentation from indoor scenes with RGBD inputs. For accurate segmentation, various post-processing methods such as superpixel from color edges or Conditional Random Field (CRF) method considering neighborhood connectivity have been used, but these methods are not efficient due to high complexity and computational cost. To solve this problem, we maximize the efficiency of post processing by using depth superpixel extracted from disparity image to handle object silhouette. Our experimental results show reasonable performances compared to previous methods in the post processing of semantic segmentation.

• Annual Conference of KIPS
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• 2002.04a
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• pp.241-244
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• 2002

본 논문은 Post-PC 에 사용되는 DSP 프로세서 기반 디지털 카메라의 효율적이고 소형화된 영상 처리 플랫폼 설계에 대해 설명한다. 제한된 소량의 기억장치를 갖는 내장형 시스템의 제약조건을 만족시키기 위해서 제안된 플랫폼은 블록 처리의 개념을 사용하여 입력 영상을 처리한다. 먼저 입력 영상이 적당한 수의 데이터 블록으로 나누어진다. 그리고 나서 영상 블록들은 일련의 블록 기반 함수들에 의해서 처리된다. 처리된 블록들은 다시 하나의 결과 영상으로 모아진다. 블록 처리는 요구되는 메모리 크기를 줄여줄 뿐만 아니라 multithreading 과 병렬 처리를 통한 더 빠른 수행을 가능하도록 해준다. 플랫폼을 구성하는 대부분의 함수들은 이러한 블록 처리의 장점을 살려서 일련의 영상 블록들을 처리한다. 소개되는 플랫폼은 특화된 하드웨어를 사용하지 않고 사용자의 요구에 맞는 또다른 영상 처리와 압축 기법을 추가하는 것이 가능하게 해준다.

• Smart Structures and Systems
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• v.31 no.4
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.