• Journal of radiological science and technology
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• v.36 no.4
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• pp.319-326
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• 2013

The purpose of this study is to evaluate usefulness of Hybrid image and Inverse image about detection of tumor shadow in chest radiography using ROC analysis. Original images of 60 cases are selected from Standards digital image date base issued by the Japanese Society of Radiological Technology. Through computer language of C, Inverse images of 60 cases and Hybrid image of 30 cases are made. The continues reading experiment was conducted. In the case of inverse image were observed by 5 radiographer and 2 radiologist. In the case of In case of Hybrid image were observed by 3 student radiographer and 2 experienced radiographer. ROC curve are constructed using ROCKIT Program made by Metz. In Inverse image, a Az of average ROC curve was increases from 0.742 of original image to 0.775 of inverse image. In normal cases, the effect of the detrimental is same to that of the beneficial, however In abnormal cases, the beneficial effect is greater than detrimental effect. However in Hybrid image, a Az of average ROC curve was decreases from 0.5253 of original image to 0.4868 of Hybrid image. In Normal cases, the effect of the detrimental is greater than that of the Beneficial, however In abnormal cases, the Beneficial effect is greater than detrimental effect. The inverse image can be more positively considered for the detecting of tumor than the hybrid image.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.6
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• pp.19-28
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• 2007

The purpose of this paper is to present a new measure for fingerprint image quality assessment that has a considerable effect on evaluation of fingerprint databases. This paper introduces a hybrid segmentation method for measuring an image quality and evaluates the experimental results using various fingerprint databases. This study compares the performance of the proposed hybrid segmentation using variance and coherence of fingerprints against the NIST's NFIQ program. Although NFIQ is a most widely used tool, it classifies the image quality into 5 levels. However, the proposed hybrid method is developed to be conformant to the ISO standards and accordant to human visual perception. The experimental results demonstrate that the hybrid method is able to produce finer quality measures.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.175-175
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• 2003

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.6
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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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• v.22 no.6
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.9
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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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.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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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.

• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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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.

• Proceedings of the IEEK Conference
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• 1999.06a
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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.