• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.3
• /
• pp.246-254
• /
• 2001

In this paper, we described an approach to the automation of visual inspection of BGA solder joint defects of surface mounted components on printed circuit board by using neural network. Inherently, the BGA solder joints are located underneath its own package body, and this induces a difficulty of taking good image of the solder joints by using conventional imaging systems. To acquire the cross-sectional image of BGA sol-der joint, X-ray cross-sectional imaging method such as laminography and digital tomosynthesis has been cur-rently utilized. However, the cross-sectional image obtained by using laminography or DT methods, has inher-ent blurring effect and artifact. This problem has been a major obstacle to extract suitable features for classifi-cation. To solve this problem, a neural network based classification method is proposed int his paper. The per-formance of the proposed approach is tested on numerous samples of printed circuit boards and compared with that of human inspector. Experimental results reveal that the method provides satisfactory perform-ance and practical usefulness in BGA solder joint inspection.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.37 no.3
• /
• pp.63-71
• /
• 2000

Infrared (IR) detector chip, which detects the IR radiation from all of the objects and converts to image signal, is usually fabricated using hybrid bonding technology with detector away and readout integrated circuit (ROIC). In this study, we designed the readout circuit and simulated its operations. Fabricating readout circuit chips, we measured operation results satisfying its design requirements in 6V supply voltage. After we mount the IR detector chip in the manufactured thermal image system, thermal images were implemented. The obtained thermal images for high and room temperature target objects are sufficiently recognizable. Using the low noise thermal Image system, we expect to obtain thermal images with higher temperature resolution.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12spc
• /
• pp.556-564
• /
• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.9
• /
• pp.2882-2903
• /
• 2022

Image super-resolution (SR) processing is of great value in the fields of digital image processing, intelligent security, film and television production and so on. This paper proposed a densely connected deep learning network based on cascade architecture, which can be used to solve the problem of super-resolution in the field of image quality enhancement. We proposed a more efficient residual scaling dense block (RSDB) and the multi-channel cascade architecture to realize more efficient feature reuse. Also we proposed a hybrid loss function based on L₁ error and L_∞ error to achieve better L_∞ error performance. The experimental results show that the overall performance of the network is effectively improved on cascade architecture and residual scaling. Compared with the residual dense net (RDN), the PSNR / SSIM of the new method is improved by 2.24% / 1.44% respectively, and the L_∞ performance is improved by 3.64%. It shows that the cascade connection and residual scaling method can effectively realize feature reuse, improving the residual convergence speed and learning efficiency of our network. The L_∞ performance is improved by 11.09% with only a minimal loses of 1.14% / 0.60% on PSNR / SSIM performance after adopting the new loss function. That is to say, the L_∞ performance can be improved greatly on the new loss function with a minor loss of PSNR / SSIM performance, which is of great value in L_∞ error sensitive tasks.

• International Journal of Computer Science & Network Security
• /
• v.23 no.5
• /
• pp.53-64
• /
• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Nuclear Engineering and Technology
• /
• v.55 no.10
• /
• pp.3844-3853
• /
• 2023

Although radiation and chemotherapy methods for cancer therapy have advanced significantly, surgical resection is still recommended for most cancers. Therefore, intraoperative imaging studies have emerged as a surgical tool for identifying tumor margins. Intraoperative imaging has been examined using conventional imaging devices, such as optical near-infrared probes, gamma probes, and ultrasound devices. However, each modality has its limitations, such as depth penetration and spatial resolution. To overcome these limitations, hybrid imaging modalities and tracer studies are being developed. In a previous study, a multi-modal laparoscope with silicon photo-multiplier (SiPM)-based gamma detection acquired a 1 s interval gamma image. However, improvements in the near-infrared fluorophore (NIRF) signal intensity and gamma image central defects are needed to further evaluate the usefulness of multi-modal systems. In this study, an attempt was made to change the NIRF image acquisition method and the SiPM-based gamma detector to improve the source detection ability and reduce the image acquisition time. The performance of the multi-modal system using a complementary metal oxide semiconductor and modified SiPM gamma detector was evaluated in a phantom test. In future studies, a multi-modal system will be further optimized for pilot preclinical studies.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.5 s.305
• /
• pp.29-40
• /
• 2005

According as base of digital hologram has been magnified, discussion of compression technology is expected as a international standard which defines the compression technique of 3D image and video has been progressed in form of 3DAV which is a part of MPEG. As we can identify in case of 3DAV, the coding technique has high possibility to be formed into the hybrid type which is a merged, refined, or mixid with the various previous technique. Therefore, we wish to present the relationship between various image/video coding techniques and digital hologram In this paper, we propose an efficient coding method of digital hologram using standard compression tools for video and image. At first, we convert fringe patterns into video data using a principle of CGH(Computer Generated Hologram), and then encode it. In this research, we propose a compression algorithm is made up of various method such as pre-processing for transform, local segmentation with global information of object image, frequency transform for coding, scanning to make fringe to video stream, classification of coefficients, and hybrid video coding. Finally the proposed hybrid compression algorithm is all of these methods. The tool for still image coding is JPEG2000, and the toots for video coding include various international compression algorithm such as MPEG-2, MPEG-4, and H.264 and various lossless compression algorithm. The proposed algorithm illustrated that it have better properties for reconstruction than the previous researches on far greater compression rate above from four times to eight times as much. Therefore we expect that the proposed technique for digital hologram coding is to be a good preceding research.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.6
• /
• pp.445-452
• /
• 2019

In this study, parallel processing codes of k-means clustering algorithm were developed and implemented in a PC-cluster for unsupervised classification of large satellite images. We implemented intra-node code using multicores of CPU (Central Processing Unit) based on OpenMP (Open Multi-Processing), inter-nodes code using a PC-cluster based on message passing interface, and hybrid code using both. The PC-cluster consists of one master node and eight slave nodes, and each node is equipped with eight multicores. Two operating systems, Microsoft Windows and Canonical Ubuntu, were installed in the PC-cluster in turn and tested to compare parallel processing performance. Two multispectral satellite images were tested, which are a medium-capacity LANDSAT 8 OLI (Operational Land Imager) image and a high-capacity Sentinel 2A image. To evaluate the performance of parallel processing, speedup and efficiency were measured. Overall, the speedup was over N / 2 and the efficiency was over 0.5. From the comparison of the two operating systems, the Ubuntu system showed two to three times faster performance. To confirm that the results of the sequential and parallel processing coincide with the other, the center value of each band and the number of classified pixels were compared, and result images were examined by pixel to pixel comparison. It was found that care should be taken to avoid false sharing of OpenMP in intra-node implementation. To process large satellite images in a PC-cluster, code and hardware should be designed to reduce performance degradation caused by file I / O. Also, it was found that performance can differ depending on the operating system installed in a PC-cluster.

• Journal of Fashion Business
• /
• v.24 no.1
• /
• pp.88-101
• /
• 2020

The music videos of BTS go beyond the limit of media promoting music and shows their meaning in various ways and complete the visual message of music through fashion style. BTS' fashion style in the music videos shows a change in symbolic representation of the genre of each album and song, of which gender images are changing aligned with the music messages of BTS. The purpose of this study was to derive gender images of fashion style in BTS music videos and to interpret their meaning based on Judith Butler's theory that performativity creates discourse through iterative process. It is conducted as a research method, an analytical study was conducted in parallel with literature studies and empirical case analysis. The scope of the study was limited to 301 costumes that appeared in 21 official music videos from debut single album '2Cool 4 Skool' released in 2013 to the mini album 'Map of the Soul: Persona' released in 2019. As a result of the analysis, the controversial fashion style, challenging fashion style, boyish fashion style, hybrid fashion style, the playful fashion style were revealed. The conclusion of studying the gender image of BTS, interpreted by this analysis using Judith Butler's theory, is as follows. The gender image of BTS is the traditional image that identifies with the dominant gender discourse, the resistive gender image that intentionally distances mainstream culture, the eclectic image parodying the gender of the opposing term, and the deconstructive image that transcends the dominant gender discourse.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.2
• /
• pp.95-101
• /
• 2017

In this paper, the sizing removal condition for the pretreatment of composite materials is obtained numerically by applying an image processing algorithm and nickel-plated carbon fiber is fabricated by a dry process method to enhance its electromagnetic shielding performance. Sizings that are wrapped in a polymer type material during the manufacturing of carbon fiber should be removed for dry coating. A numerical value, that is the correlation, can be obtained by determining the regular pattern of the carbon fiber in the image taken by a scanning electron microscope (SEM) after the sizing is removed. The application of the proposed numerical method to the SEM image of the fiber after the sizing is removed with solution, compressed air, solution and compressed air (hybrid), showed that this method of eliminating the sizing is superior to the hybrid method. Then, by spreading the carbon fiber roll with the sizing removed, we were able to produce nickel plated carbon fiber by the roll-to-roll sputtering method. The electromagnetic shielding performance of the fabricated 30, 40 and 100 nickel coated carbon fibers was measured. The Korea Advanced Institute of Science and Technology evaluated the electromagnetic shielding performance of the 100 nickel-coated carbon fiber to have a maximum value of 73.2 (dB) and a minimum value of 66.7 (dB). This is similar to the electromagnetic shielding rate of copper and shows that this material can be used as a cable for EV / HEV automobiles.