• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.481-492
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• 1997

This paper presents an algorithm for recognizing surface mount device(SMD) IC pattern based on the error back propoagation(EBP) neural network and discrete cosine transform(DCT). In this approach, we chose such parameters as frequency, angle, translation and amplitude for the shape informantion of SMD IC, which are calculated from the coefficient matrix of DCT. These feature parameters are normalized and then used for the input vector of neural network which is capable of adapting the surroundings such as variation of illumination, arrangement of objects and translation. Learning of EBP neural network is carried out until maximum error of the output layer is less then 0.020 and consequently, after the learning of forty thousand times, the maximum error have got to this value. Experimental results show that the rate of recognition is 100% in case of the random pattern taken at a similar circumstance as well as normalized training pattern. It also show that proposed method is not only relatively relatively simple compare with the traditional space domain method in extracting the feature parameter but also able to re recognize the pattern's class, position, and existence.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.228-246
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• 2011

A 3D model based approach for a face representation and recognition algorithm has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper, we propose a novel 3D face representation algorithm based on a pixel to vertex map (PVM) to optimize the number of vertices. We explore shape and texture coefficient vectors of the 3D model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that the proposed face representation and recognition algorithm is efficient in computation time while maintaining reasonable accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.418-420
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• 2012

스테레오 매칭의 지역적 방법은 구현의 용이성과 낮은 계산복잡도로 인하여 많은 연구가 진행되고 있다. 하지만 대부분의 지역적 방법들은 영상이 외부환경에 변형되었을 때의 경우를 고려하지 않고 있기 때문에, 외부환경에 의해 많이 변형된 영상에 대해서는 제대로 된 변이 정보를 추출해내지 못한다. 본 논문에서는 양쪽 영상에서 곡선접합을 이용하여 서로 대응되는 영역을 찾는 스테레오 매칭 기법을 제안한다. 제안하는 기법은 조명과 같은 외부요소에 강인한 특징을 가진다. 이 기법은 전 처리나 후처리 과정에서 부가적인 작업의 수행 없이 기법 자체만으로 외부요소에 대한 보상을 실행한다는 면에서 장점을 가진다. 비록 다양한 영상에서 변이를 추출하는 실험 결과, 거시적인 특성을 반영하는 곡선접합만으로도 조명에 의해 변형된 영상에 대해서 변이결과를 추출해내었다. 차후 미시적인 방법과의 결합을 통해, 변이정보의 추출의 정확도를 올릴 수 있을 것 이라고 기대된다.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.211-220
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• 2021

The process of inspecting SMDs on the PCB boards improves the product quality, performance and reduces frequent issues in this field. However, undesirable scenarios such as assembly failure and device breakdown can occur sometime during the assembly process and result in costly losses and time-consuming. The detection of these components with a model based on deep learning may be effective to reduce some errors during the inspection in the manufacturing process. In this paper, YOLO models were used due to their high speed and good accuracy in classification and target detection. A SMD detection and classification method using YOLO networks based on robust data preprocessing and augmentation techniques to deal with various types of variation such as illumination and geometric changes is proposed. For 9 different components of data provided from a PCB manufacturer company, the experiment results show that YOLOv4 is better with fast detection and classification than YOLOv3.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.871-891
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• 2019

Visual tracking is a challenging task that needs learning an effective model to handle the changes of target appearance caused by factors such as pose variation, illumination change, occlusion and motion blur. In this paper, a novel tracking algorithm based on weighted local sub-space reconstruction error is presented. First, accounting for the appearance changes in the tracking process, a generative weight calculation method based on structural reconstruction error is proposed. Furthermore, a template update scheme of occlusion-aware is introduced, in which we reconstruct a new template instead of simply exploiting the best observation for template update. The effectiveness and feasibility of the proposed algorithm are verified by comparing it with some state-of-the-art algorithms quantitatively and qualitatively.

• American Studies
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• v.44 no.1
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• pp.155-182
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• 2021

This study explores how American war films modify or reenact American exceptionalism depending on political and social situations in the United States. To this end, it analyzes the two war films, Saving Private Ryan and We Were Soldiers that were released before and after the 9/11 attacks as a critical juncture in the twenty first century, respectively. While the former conducts a partial modification on American exceptionalism in order to restore the national identity and moral authority of the United States lost ever since the Vietnam War, the latter demonstrates a complete reenactment of American exceptionalism in accordance with the foreign policy of the Bush administration and neoconservatism. It concludes with the illumination that the examination of American war films within the framework of American exceptionalism is efficacious in understanding from a broad perspective how American exceptionalism is utilized depending on political and social situations in the United States.

• Current Optics and Photonics
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• v.7 no.5
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• pp.537-544
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• 2023

Differential phase contrast (DPC) microscopy, a central quantitative phase imaging (QPI) technique in cell biology, facilitates label-free, real-time monitoring of intrinsic optical phase variations in biological samples. The existing DPC imaging theory, while important for QPI, is grounded in paraxial diffraction theory. However, this theory lacks accuracy when applied to high numerical aperture (NA) systems that are vital for high-resolution cellular studies. To tackle this limitation, we have, for the first time, formulated a nonparaxial DPC imaging equation with a transmission cross-coefficient (TCC) for high NA DPC microscopy. Our theoretical framework incorporates the apodization of the high NA objective lens, nonparaxial light propagation, and the angular distribution of source intensity or detector sensitivity. Thus, our TCC model deviates significantly from traditional paraxial TCCs, influenced by both NA and the angular variation of illumination or detection. Our nonparaxial imaging theory could enhance phase retrieval accuracy in QPI based on high NA DPC imaging.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3490-3507
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• 2014

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.171-179
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• 2013

Recognition and image processing technology depends on illumination variation. One of the most important factors is the parameters of algorithms. When it comes to select these values, the system has different types of recognition accuracy. In this paper, we propose performance improvement of the eye tracking system that depends on some environments such as, people, location, and illumination. Optimized threshold parameter was decided by using reinforcement learning. When the system accuracy goes down, reinforcement learning used to train the value of parameters. According to the experimental results, the performance of eye tracking system can be improved from 3% to 14% by using reinforcement learning. The improved eye tracking system can be effectively used for human-computer interaction.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.643-650
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• 2013

In this paper, we propose a novel method for enhancing the detection speed and rate by reducing the computation in Hough Circle Transform on real-time iris detection of smartphone camera image. First of all, we find a face and eyes from input image to detect iris and normalize the iris region into fixed size to prevent variation of size for iris region according to distance from camera lens. Moreover, we carry out histogram equalization to get regular image in bright and dark illumination from smartphone and calculate minimal iris range that contains iris with the distance between corner of the left eye and corner of the right eye on the image. Subsequently, we can minimize the computation of iris detection by applying Hough Circle Transform on the range including the iris only. The experiment is carried out in two case with bright and dark illumination. Our proposed method represents that detection speed is 40% faster and detection rate is 14% better than existing methods.