• 대한방사선기술학회지:방사선기술과학
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• 제36권4호
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• pp.319-326
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• 2013

본 연구에서는, 흉부단순사진에 있어서 종양그림자의 검출에 대한 흑백 Inverse image과 Hybrid image의 유용성을 ROC해석으로 검토하였다. 증례의 선택은 일본방사선기술학회가 발행한 표준 Digital 영상 Date Base로부터 30장을 선택 하여 original image로 하였다. c언어를 통해 Inverse image는 60장, Hybrid image는 30장 제작하였다. 실험방법으로 연속 판독실험을 하였고, ROC실험 display program은 Matlap을 통하여 작성하였다. 관찰자의 수는 Inverse image의 경우 방사선사 5명과 방사선의 2명, 합계 7명으로 실험하였다. Hybrid 영상의 경우, 방사선 전공자 3명과 숙련된 방사선사 2명, 합계 5명으로 실험하였다. ROC곡선은 Metz가 작성한 ROCKIT Program을 이용하여 구하였다. Inverse image의 경우 관찰자 7명 전원, 방사선과의 2명, 방사선사 5명의 평균 ROC곡선의 Az는 각각 original image의 0.742, 0.793, 0.721에서, Inverse image의 0.775, 0.821, 0.753까지로, 통계적 유의차로 증가하였다. Hybrid image의 경우 관찰자 5명 전원, 숙련된 방사선사 2명, 방사선학 전공자 3명의 평균 ROC곡선의 Az는 각각 original image의 0.525, 0.491, 0.5478에서, Hybrid 영상의 0.4868, 0.539, 0.450로 변화 하였다. 결론적으로, 흉부단순사진에서 종양의 검출에 관하여, Inverse image은 유의하지만, Hybrid 영상의 경우 유의한 차이가 나타나지 않았다.

• 정보보호학회논문지
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• 제17권6호
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• pp.19-28
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• 2007

본 논문은 지문 데이터베이스를 평가하는데 가장 큰 영향을 미치는 image quality를 측정하는 새로운 방법을 제안한다. 본 논문에서는 image quality를 측정하는 hybrid segmentation 방법을 소개하고, 다양한 지문 데이터베이스에 대해 실험한 결과를 분석한다. 개발한 방법의 객관적인 평가를 위해 NIST에서 제공하는 NFIQ 프로그램을 통해 얻은 결과와 variance와 coherence의 fusion을 이용한 hybrid segmentation 결과를 비교한다. NFIQ는 지문 영상의 품질을 정확하게 측정하지만 결과가 $1{\sim}5$로 세분화되어 있지 못한 문제점을 가지고 있다. 반면 제안하는 hybrid 방법은 NFIQ보다 더 정확하고 세분화된 평가 결과를 제공한다. 두 방법에 의해 실험한 데이터베이스들을 평가한 결과, 동일한 영상에 대해 NFIQ와 hybrid segmentation의 결과가 유사하며 지문 영상의 품질을 세분화하여 측정할 수 있는 점에서 NFIQ보다 뛰어나다고 할 수 있다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 추계학술대회 논문요약집
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• pp.175-175
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• 2003

• 대한임베디드공학회논문지
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• 제12권6호
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• pp.395-403
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• 2017

Image processing for object detection and identification has been studied for supply chain management application with various approaches. Among them, feature pointed detection algorithm is used to track an object or to recognize a position in automated supply chain systems and a depth image based feature point detection is recently highlighted in the application. The result of feature point detection is easily influenced by image noise. Also, the depth image has noise itself and it also affects to the accuracy of the detection results. In order to solve these problems, we propose a novel hybrid filtering mechanism for depth image based feature point detection, it shows better performance compared with conventional hybrid filtering mechanism.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• 한국통신학회논문지
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• 제21권9호
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• pp.2317-2327
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• 1996

Original binary image can be reconstructed without any distortion by MS(morphological skeleton) image. Though we reduce some points in a MS image, there is no problem to reconstruct original image by it. And then, there are two methods of LMS and GMS which reduce the redundant points of a MS image. The redundancy degree of a GMS image is zero and it is less than that of LMS. And then, GMS image is the best thing of the three kinds of morphological skeleton image to enhance the compression efficienty by the Elias code. But there are continous SKF=1 points in a GMS image whenever using 2 dimensional structureing element. Those points in a GMS image gives rise to a bad compression efficiency. And then, solving this problem, this paper proposes hybrid structuring elements algorithms for binary image compression.

• 제어로봇시스템학회논문지
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• 제17권2호
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• pp.157-163
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• 2011

This paper proposes a hybrid image stabilizing system which uses both optical image stabilizing system based on EKF (Extended Kalman Filter) and digital image stabilization based on SURF (Speeded Up Robust Feature). Though image information is one of the most efficient data for object recognition, it is susceptible to noise which results from internal vibration as well as external factors. The blurred image obtained by the camera mounted on a robot makes it difficult for the robot to recognize its environment. The proposed system estimates shaking angle through EKF based on the information from inclinometer and gyro sensor to stabilize the image. In addition, extracting the feature points around rotation axis using SURF which is robust to change in scale or rotation enhances processing speed by removing unnecessary operations using Hessian matrix. The experimental results using the proposed hybrid system shows its effectiveness in extended frequency range.

• 반도체디스플레이기술학회지
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• 제10권3호
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• 반도체디스플레이기술학회지
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• 제21권1호
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• pp.142-147
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• 2022

After introducing Hybrid Bonding technology into image sensors using stacked sensors and image processors, large quantity production became possible. As a result, it is currently used in most of the CMOS image market in smartphones and other image-based devices worldwide, and almost all stacked CIS manufacturing sites have focused on miniaturization using hybrid bonding. In this study, an upper wafer handling module for Wafer to Wafer Hybrid Bonding developed to increase the alignment and precision between wafers when wafer bonding. The module was divided two parts to reduce error of both the alignment and degree of precision during wafer bonding. Wafer handling module developed both new Tip/Tilt system controlling θx,θy of upper wafer and striker to push upper wafer. Based on this, it was confirmed through the stability evaluation that the upper wafer handling module can be controlled without any problem during W2W hybrid bonding.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.1025-1028
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• 1999

After detecting the edge which is applying the morphological operators to the hybrid FCNN, we could analyze and compare. The hybrid FCNN is completely removed to the noise in the image, and worked in order to obtain the result image which is closest to the original image. Also, the morphological operator is applied to the image as the method in order to detect more good the edge than the conventional edge. FCNN which is the pipeline type is completely suitable to detecting the image processing as well as the hardware size. In this paper. we would make the structure elements of the morphological operator the variable template and the static template, and compare with the edge enhancement of two images. After being the result which is applying the variable template morphological operator and the static template morphological operator to the image, we could know that the edge images applying the variable template is superior in a edge enhancement side.