• 한국정보통신학회논문지
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• 제6권4호
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• pp.624-631
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• 2002

본 논문에서는 이진영상 데이터의 저작권 보호를 위한 디지털 워터마킹 방법을 제안하였다. 제안 방법은 먼저 이진영상을 기하학적 특징이 포함된 특징영역과 그 외의 일반영역으로 분리한다. 그리고 기하학적 특징이 포함된 이진영상의 특징영역을 보존하면서 인증을 위한 워터마크를 일반영역에만 삽입, 검출하는 효과 적인 워터마킹 방법을 연구하였다. 특징영역은 특수런을 사용한 런길이부호화를 이용하여 일반영역과 분리하였다. 워터마크의 비가시성을 위해 워터마크는 일반영역중에서 화소변화에 대한 민감도를 고려하여 삽입하였다. 제안 방법을 문자, 서명, 도장, 지문영상에 적용하여 성능을 평가하였다. 실험 결과 제안 방법은 원영상의 중요한 특징영역을 보존하고, 또한 워터마킹된 영상의 비가시성이 높음을 알 수 있었다.

• 한국정밀공학회지
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• 제30권2호
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• pp.177-184
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• 2013

This paper proposes an enhanced Simultaneous Localization and Mapping (SLAM) algorithm based on matching laser image and Extended Kalman Filter (EKF). In general, laser information is one of the most efficient data for localization of mobile robots and is more accurate than encoder data. For localization of a mobile robot, moving distance information of a robot is often obtained by encoders and distance information from the robot to landmarks is estimated by various sensors. Though encoder has high resolution, it is difficult to estimate current position of a robot precisely because of encoder error caused by slip and backlash of wheels. In this paper, the position and angle of the robot are estimated by comparing laser images obtained from laser scanner with high accuracy. In addition, Speeded Up Robust Features (SURF) is used for extracting feature points at previous laser image and current laser image by comparing feature points. As a result, the moving distance and heading angle are obtained based on information of available points. The experimental results using the proposed laser slam algorithm show effectiveness for the SLAM of robot.

• 정보처리학회논문지B
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• 제10B권6호
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• pp.681-686
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• 2003

대량의 일반 이미지 집합에서 사용자가 원하는 이미지를 효율적으로 찾아내는 것이 내용기반 이미지 검색 연구의 주된 목적이나 특정한 분야에 속하지 않은 일반 이미지를 대상으로 하는 연구는 아직까지 만족스럽지 못한 실정이다. 이 논문에서는 이미지의 색상과 형태의 특징 정보들을 추출하여 자동으로 색인하고 검색하는 시스템을 제안하였다. 특징 추출은 인간의 이미지 인식 과정에 기반하여 전체적인 정보와 세부적인 정보로 구분하여 수행하였다. 추출된 특징 정보들은 전역 칼라, 부분 영역 칼라, 전역 형태, 부분 영역 형태 정보로 구분하였다. 실험 결과 제안한 방법은 기존의 방법과 비슷한 시간 내에 비교적 높은 Precision과 Retail로 이미지를 검색함을 알 수 있었다.

• 반도체디스플레이기술학회지
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• 제15권4호
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• pp.97-102
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• 2016

Image stitching is a process of combining two or more images with overlapping area to create a panorama of input images, which is considered as an active research area in computer vision, especially in the field of augmented reality with 360 degree images. Image stitching techniques can be categorized into two general approaches: direct and feature based techniques. Direct techniques compare all the pixel intensities of the images with each other, while feature based approaches aim to determine a relationship between the images through distinct features extracted from the images. This paper proposes a novel image stitching method based on feature pixels with approximated clustering filter. When the features are extracted from input images, we calculate a meaning of the minutiae, and apply an effective feature extraction algorithm to improve the processing time. With the evaluation of the results, the proposed method is corresponding accurate and effective, compared to the previous approaches.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2002년도 춘계종합학술대회
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• pp.177-180
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• 2002

본 논문에서는 이진영상 데이터의 저작권 보호를 위한 디지털 워터마킹 방법을 제안하였다. 제안 방법은 먼저 이진영상을 기하학적 특징이 포함된 특징영역과 그 외의 일반영역으로 분리한다. 그리고 기하학적 특징이 포함된 이진영상의 특징영역을 보존하면서 인증을 위한 워터마크를 일반영역에만 삽입, 검출하는 효과적인 워터마킹 방법을 연구하였다. 특징영역은 특수런을 사용한 런길이부호화를 이용하여 일반영역과 분리하였다. 워터마크의 비가시성을 위해 워터마크는 일반영역 중에서 화소변화에 대한 민감도를 고려하여 삽입하였다. 제안 방법을 문사, 서명, 도장, 지문영상에 적용하여 성능을 평가하였다. 실험 결과 제안 방법은 원 영상의 중요한 특징영역을 보존하고, 또한 워터마킹된 영상의 비가시성이 높음을 알 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제21권11호
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• pp.17-21
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• 2016

Heterogeneity assessment of tumor in oncology is important for diagnosis of cancer and therapy. The aim of this study was performed assess heterogeneity tumor region in PET image using texture analysis. For assessment of heterogeneity tumor in PET image, we inserted sphere phantom in torso phantom. Cu-64 labeled radioisotope was administrated by 156.84 MBq in torso phantom. PET/CT image was acquired by PET/CT scanner (Discovery 710, GE Healthcare, Milwaukee, WI). The texture analysis of PET images was calculated using occurrence probability of gray level co-occurrence matrix. Energy and entropy is one of results of texture analysis. We performed the texture analysis in tumor, liver, and background. Assessment textural features of region-of-interest (ROI) in torso phantom used in-house software. We calculated the textural features of torso phantom in PET image using texture analysis. Calculated entropy in tumor, liver, and background were 5.322, 7.639, and 7.818. The further study will perform assessment of heterogeneity using clinical tumor PET image.

• 대한임베디드공학회논문지
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• 제3권1호
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• pp.8-18
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• 2008

In this paper, we propose an efficient algorithm using wavelet transform for still image database retrieval. Especially, it uses only the lowest frequency sub-band in multi-level wavelet transform so that a retrieval system uses a smaller quantity of memory and takes a faster processing time. We extract different textured features, statistical information such as mean, variance and histogram, from low frequency sub-band. Then we measure the distances between the query image and the images in a database in terms of these features. To obtain good retrieval performance, we use the first feature (mean and variance of wavelet coefficients) to filter out most of the unlikely images. The rest of the images are considered to be candidate images. Then we apply the second feature (histogram of wavelet coefficient) to rank all the candidate images. To evaluate the algorithm, we create various distorted image databases using MIT VisTex texture images and PICS natural images. Through simulations, we demonstrate that our method can achieve performance satisfactorily in terms of the retrieval accuracy as well as the both memory requirement and computational complexity. Therefore it is expected to provide good retrieval solution for JPEG-2000 using wavelet transform.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.143-147
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• 2024

Image noise classification is a classical problem in the field of image processing, machine learning, deep learning and computer vision. In this paper, image noise classification is performed using deep learning. Keras deep learning library of TensorFlow is used for this purpose. 6900 images images are selected from the Kaggle database for the classification purpose. Dataset for labeled noisy images of multiple type was generated with the help of Matlab from a dataset of non-noisy images. Labeled dataset comprised of Salt & Pepper, Gaussian and Sinusoidal noise. Different training and tests sets were partitioned to train and test the model for image classification. In deep neural networks CNN (Convolutional Neural Network) is used due to its in-depth and hidden patterns and features learning in the images to be classified. This deep learning of features and patterns in images make CNN outperform the other classical methods in many classification problems.

• International Journal of Control, Automation, and Systems
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• 제6권6호
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• pp.800-808
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• 2008

A fingerprint verification system based on a set of invariant moment features and a nonlinear Back Propagation Neural Network(BPNN) verifier is proposed. An image-based method with invariant moment features for fingerprint verification is used to overcome the demerits of traditional minutiae-based methods and other image-based methods. The proposed system contains two stages: an off-line stage for template processing and an on-line stage for testing with input fingerprints. The system preprocesses fingerprints and reliably detects a unique reference point to determine a Region-of-Interest(ROI). A total of four sets of seven invariant moment features are extracted from four partitioned sub-images of an ROI. Matching between the feature vectors of a test fingerprint and those of a template fingerprint in the database is evaluated by a nonlinear BPNN and its performance is compared with other methods in terms of absolute distance as a similarity measure. The experimental results show that the proposed method with BPNN matching has a higher matching accuracy, while the method with absolute distance has a faster matching speed. Comparison results with other famous methods also show that the proposed method outperforms them in verification accuracy.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.367-369
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• 2004

In this paper we propose an effective and reliable hand detection method using neural network with ICA(Independent Component Analysis) Features. Many algorithms of hand detection have been proposed yet. Among them, ICA is the one of the interesting topics in image processing. ICA can not only separate mixed signals but also efficiently extract low-dimensional features in signals. ICA features are able to represent the characteristic of the images well. The object of this paper is to use effectively ICA that has above advantage. That is, by the proper number of Independent component the arithmetic speed is faster and by normalization of ICA feature the performance of detection is more reliable. For this, we adopt the algorithm, the Proportion of variance, which select the ICA feature by comparing the ratio of variance of ICA feature. By this method, we can extract the feature that is good at classifying hand and non-hand. Our experimental results show that by using ICA features, we obtained a better performance in hand detection than by only training NN on the image. And we can use hand detection system effectively and reliably by our proposal.