• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.5-6
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• 2020

현대 의학에서 의료 영상은 수많은 영상처리 의료기기의 핵심이다. PACS(Picture Archiving Communication System)를 통해 관리되는 의료 영상 자료들은 요청에 따라 저장, 검색 및 전송을 수행하여 신속한 의료 서비스를 가능하게 한다. 그러나 만약에 관리자의 실수로 의료 영상 데이터가 바뀐다면 이는 사용자로 하여금 불편함과 낮은 신뢰성을 야기한다. 그리하여 본 논문에서는 서포트 벡터 머신 기반의 HOG(Histogram of Oriented Gradients) 특징 벡터를 이용하여 X-ray와 MRI(Magnetic Resonance Imaging) 사진을 분류하고 의료 영상 분류의 가능성을 제시하는 것을 목표로 한다.

• Journal of Broadcast Engineering
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• v.14 no.4
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• pp.397-408
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• 2009

In this paper, we present a non-static color histogram method to retrieve clothing images that are similar to a query clothing. Given clothing area, our method automatically extracts major colors by using the octree-based quantization approach[16]. Then, a color palette that is composed of the major colors is generated. The feature of each clothing, which can be either a query or a database clothing image, is represented as a color histogram based on its color palette. We define the match color bins between two possibly different color palettes, and unify the color palettes by merging or deleting some color bins if necessary. The similarity between two histograms is measured by using the weighted Euclidean distance between the match color bins, where the weight is derived from the frequency of each bin. We compare our method with previous histogram matching methods through experiments. Compared to HSV cumulative histogram-based approach, our method improves the retrieval precision by 13.7 % with less number of color bins.

• Journal of Korea Multimedia Society
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• v.13 no.7
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• pp.1088-1094
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• 2010

Image authentication algorithms have to discriminate forged contents in the various critical fields of military, medical services, digital documents. They must ensure perceptual invisibility and fragility against malicious attacks. It is desirable that watermarking algorithms support sufficient insertion capacity and blind feature. And, high reliable algorithms that can eliminate false-positive and false-negative errors are needed in the watermark extraction process. In this paper, we control coefficients of high frequency band in a DCT domain and compensate brightness histogram for high reliability. As a result, we found that the proposed algorithm guarantee various requirements such as perceptual invisibility with high PSNR values, fragility, high reliability and blind feature. In addition, experiment results show that the proposed algorithm can be used steganographic applications by sufficient capacity of watermark.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.1
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• pp.128-134
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• 2009

Iris recognition is a biometric technology which can identify a person using the iris pattern. Recently, using iris information is used in many fields such as access control and information security. But Perform complex operations to extract features of the iris. because High-end hardware for real-time iris recognition is required. This paper is appropriate for the embedded environment using local gradient histogram embedded system using iris feature extraction methods have implement. Experimental results show that the performance of proposed method is comparable to existing methods using Gabor transform noticeably improves recognition performance and it is noted that the processing time of the local gradient histogram transform is much faster than that of the existing method and rotation was also a strong attribute.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.218-221
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• 2006

본 논문은 인터넷의 대표적인 문제점중의 하나인 Adult Image 분류 연구에 대해 기술한다. 특히 우리는 이러한 Adult Image를 분류하기 위한 Data Set을 5가지 타입으로 구성한다. 이러한 각 Image에 대해 Color, Gradient, Edge Direction 특성의 Feature들을 추출하고 이를 Histogram으로 구성한다. 이렇게 구성된 Histogram을 Support Vector Machine에 적용하여 Adult Image를 분류한다. 그 결과, 우리는 8250개의 Test Set에 대하여 Recall(96.53%), Precision(97.33%), False Positive(2.96%), F-Measure(96.93%)의 성능 결과를 보여준다.

• The Journal of Korea Robotics Society
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• v.12 no.2
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• pp.217-227
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• 2017

Crosswalk detection is an important part of the Pedestrian Protection System in autonomous vehicles. Different methods of crosswalk detection have been introduced so far using crosswalk edge features, the distance between crosswalk blocks, laser scanning, Hough Transformation, and Fourier Transformation. However, most of these methods failed to detect crosswalks accurately, when they are damaged, faded away or partly occluded. Furthermore, these methods face difficulties when applying on real road environment where there are lot of vehicles. In this paper, we solve this problem by first using a region based binarization technique and x-axis histogram to detect the candidate crosswalk areas. Then, we apply Support Vector Machine (SVM) based classification method to decide whether the candidate areas contain a crosswalk or not. Experiment results prove that our method can detect crosswalks in different environment conditions with higher recognition rate even they are faded away or partly occluded.

• Journal of Multimedia Information System
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• v.4 no.1
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• pp.33-38
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• 2017

This article suggests a new algorithm for detecting two-wheelers on the road that have various shapes according to viewpoints. Because of complicated shapes, it is more difficult than detecting a human. In general, the Histograms of Oriented Gradients(HOG) feature is well known as a useful method of detecting a standing human. We propose a method of detecting a human on a two-wheelers using the spatial relocation of HOG (Histogram of Oriented Gradients) features. And this paper adapted the contrast method which is generally using in the image process to improve the detection rate. Our experimental results show that a two-wheelers detection system based on proposed approach leads to higher detection accuracy, less computation, and similar detection time than traditional features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4534-4555
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• 2015

We propose a robust object cataloging method using multiple locally distinct heterogeneous features for aiding image retrieval. Due to challenges such as variations in object size, orientation, illumination etc. object recognition is extraordinarily challenging problem. In these circumstances, we adapt local interest point detection method which locates prototypical local components in object imageries. In each local component, we exploit heterogeneous features such as gradient-weighted orientation histogram, sum of wavelet responses, histograms using different color spaces etc. and combine these features together to describe each component divergently. A global signature is formed by adapting the concept of bag of feature model which counts frequencies of its local components with respect to words in a dictionary. The proposed method demonstrates its excellence in classifying objects in various complex backgrounds. Our proposed local feature shows classification accuracy of 98% while SURF,SIFT, BRISK and FREAK get 81%, 88%, 84% and 87% respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.153-156
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• 2007

In this paper, we propose a fast face detection approach using PCA and SVM. In our detection system, first we filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, we use SVM classifier to detect whether there are faces present in the test image. Support Vector Machine (SVM) has great performance in classification task. PCA is used for dimension reduction of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.260-270
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• 2023

We introduce a novel Visual Inertial Odometry (VIO) algorithm designed to improve the performance of thermal-inertial odometry. Thermal infrared image, though advantageous for feature extraction in low-light conditions, typically suffers from a high noise level and significant information loss during the 8-bit conversion. Our algorithm overcomes these limitations by approximating a 14-bit raw pixel histogram into a Gaussian mixture model. The conversion method effectively emphasizes image regions where texture for visual tracking is abundant while reduces unnecessary background information. We incorporate the robust learning-based feature extraction and matching methods, SuperPoint and SuperGlue, and zero velocity detection module to further reduce the uncertainty of visual odometry. Tested across various datasets, the proposed algorithm shows improved performance compared to other state-of-the-art VIO algorithms, paving the way for robust thermal-inertial odometry.