• Journal of Multimedia Information System
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• 제5권2호
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• pp.131-138
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• 2018

This work describes a binary image based fast Difference of Gaussian (DoG) filter using zero-dimensional (0-d) convolution and state machine look up tables (LUTs) for image and video stitching hardware platforms. The proposed approach for using binary images to obtain DoG filtering can significantly reduce the data size compared to conventional gray scale based DoG filters, yet binary images still preserve the key features of the image such as contours, edges, and corners. Furthermore, the binary image based DoG filtering can be realized with zero-dimensional convolution and state machine LUTs which eliminates the major portion of the adder and multiplier blocks that are generally used in conventional DoG filter hardware engines. This enables fast computation time along with the data size reduction which can lead to compact and low power image and video stitching hardware blocks. The proposed DoG filter using binary images has been implemented with a FPGA (Altera DE2-115), and the results have been verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1448-1463
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• 2021

With the continuous development of cloud storage, plenty of redundant data exists in cloud storage, especially multimedia data such as images and videos. Data deduplication is a data reduction technology that significantly reduces storage requirements and increases bandwidth efficiency. To ensure data security, users typically encrypt data before uploading it. However, there is a contradiction between data encryption and deduplication. Existing deduplication methods for regular files cannot be applied to image deduplication because images need to be detected based on visual content. In this paper, we propose a secure image deduplication scheme based on hashing and clustering, which combines a novel perceptual hash algorithm based on Local Binary Pattern. In this scheme, the hash value of the image is used as the fingerprint to perform deduplication, and the image is transmitted in an encrypted form. Images are clustered to reduce the time complexity of deduplication. The proposed scheme can ensure the security of images and improve deduplication accuracy. The comparison with other image deduplication schemes demonstrates that our scheme has somewhat better performance.

• 한국멀티미디어학회논문지
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• 제24권12호
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• pp.1624-1631
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• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.359-360
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• 2021

영상의 픽셀기반처리는 한 픽셀의 값을 변환하는데 다른 픽셀의 값에 관계없이 단지 현재의 픽셀의 값에만 의존하여 변환하는 처리를 의미한다. 픽셀기반 처리는 영상 변환, 영상 개선, 영상 합성 등의 많은 분야에서 가장 기초적인 연산으로 사용된다. 산술연산, 히스토그램 평활화, 명암대비 스트레칭 등의 처리 방법들이 있다. 본 논문에서는 드론으로 촬영된 서해안 갯벌 영상에서 갯벌 영역을 명확하게 구분하기 위하여 전처리 과정 중 경계검출부분에서 픽셀기반처리를 이용하여 효율적인 윤곽선을 찾기 위한 방법을 모색한다.

• 정보처리학회논문지B
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• 제16B권2호
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• pp.93-100
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• 2009

본 논문에서는 차영상 엔트로피 기반의 시선 인식 시스템을 제안한다. 차영상 엔트로피는 현재 입력된 영상과 참조 영상 또는 시선의 위치별 평균 영상들로부터 차영상을 획득하고, -255부터 +255까지의 히스토그램 빈도수를 이용하여 계산한다. 차영상 엔트로피 기반의 시선 인식방법은 2가지 방법이다. 1) 첫 번째 방법은 현재 입력된 영상과 시선 위치별 45개의 평균 영상들과의 차영상 엔트로피를 계산하여 현재 응시하고 있는 방향을 인식하고, 2) 두 번째 방법은 현재 입력된 영상과 45개의 참조 영상들과의 차영상 엔트로피를 계산하여 현재의 응시 방향을 인식한다. 참조 영상은 네 방향의 영상을 입력받아 시선 위치별 45개의 평균 영상을 이용하여 생성한다. 제안한 시스템의 성능을 평가하기 위해 PCA 기반의 시선 인식 시스템과 비교 실험을 하였고, 인식 방향은 좌상, 우상, 좌하, 우하 네 방향으로 하였으며, 45개의 참조 영상 또는 평균영상에 대하여 인식 영역을 변경하여 실험하였다. 실험 결과 차영상 엔트로피는 97.00%, PCA는 95.50%의 인식률을 보여 차영상 엔트로피 기반의 시스템이 PCA 기반의 시스템보다 1.50% 더 높은 인식률을 나타내었다.

• 복식
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• 제58권10호
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• pp.135-147
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• 2008

The purposes of this study were to segment consumers by Korean image perceptions and to investigate the differences among the segmented groups in regard to Korean image preferences in casual wear The subjects of the study were 653 women consumers who lived in Seoul. Data were collected from July to September, 2007. Statistical analyses used in the study were factor analysis, cluster analysis, ANOVA, and Duncan test. The results showed that there were four factors of Korean image perceptions: natural, feminine, passionate, and simple images. Based on the four factors, the consumers were segmented into three groups. They were natural image perception group, low interest group, and feminine image perception group. The results showed that there were significant differences among the segmented groups in regard to Korean image preferences In casual wear. For example, natural image perception group prefer diverse Korean prints, blue and green colors, natural fabrics, and diverse casual types for their Korean image casual wear. In the meanwhile, feminine image perception group generally prefer soft fabrics, red and purple colors in their Korean image casual wear.

• 제어로봇시스템학회논문지
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• 제19권5호
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• pp.457-461
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• 2013

This paper proposes an image feature-based real-time RGB-D (Red-Green-Blue Depth) 3D SLAM (Simultaneous Localization and Mapping) system. RGB-D data from Kinect style sensors contain a 2D image and per-pixel depth information. 6-DOF (Degree-of-Freedom) visual odometry is obtained through the 3D-RANSAC (RANdom SAmple Consensus) algorithm with 2D image features and depth data. For speed up extraction of features, parallel computation is performed with GPU acceleration. After a feature manager detects a loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and builds a 3D point cloud based map.

• Transactions on Control, Automation and Systems Engineering
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• 제2권1호
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• pp.47-55
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• 2000

In this paper, a vision-based roadway detection algorithm for an automated vehicle control system, based on roadway sign information on roads, is proposed. First, in order to detect roadway signs, the color scene image is enhanced under hue-invariance. Fuzzy logic is employed to simplify the enhanced color image into a binary image and the binary image is morphologically filtered. Then, an effective algorithm of locating signs based on binary rank order transform (BROT) is utilized to extract signs from the image. This algorithm performs better than those previously presented. Finally, the inner shapes of roadway signs with curving roadway direction information are recognized by neural networks. Experimental results show that the new detection algorithm is simple and robust, and performs well on real sign detection. The results also show that the neural networks used can exactly recognize the inner shapes of signs even for very noisy shapes.

• Journal of Computational Design and Engineering
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• 제3권3호
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• pp.191-197
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• 2016

Since 3D measurement technologies have been widely used in manufacturing industries edge detection in a depth image plays an important role in computer vision applications. In this paper, we have proposed an edge detection process in a depth image based on the image based smoothing and morphological operations. In this method we have used the principle of Median filtering, which has a renowned feature for edge preservation properties. The edge detection was done based on Canny Edge detection principle and was improvised with morphological operations, which are represented as combinations of erosion and dilation. Later, we compared our results with some existing methods and exhibited that this method produced better results. However, this method works in multiframe applications with effective framerates. Thus this technique will aid to detect edges robustly from depth images and contribute to promote applications in depth images such as object detection, object segmentation, etc.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1996년도 학술대회
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• pp.175-180
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• 1996

In this paper, a new image segmentation method based on merging of two low contrast neighbor regions iteratively is proposed. It is suitable for very low bit rate coding. The proposed method reduces efficiently contour information and preserves subjective and objective image quality. It consists of image segmentation using 4-level hierarchical structure based on mathematical morphology and 1-level region merging structure using the contrast difference of two adjacent neighbor regions. For each segmented region of the third level, two adjacent neighbor regions having low contrast difference value in fourth level based on contrast difference value is merged iteratively. It preserves image quality and shows the noticeable reduction of the contour information, so that it can improve the bottleneck problem of segmentation-based coding at very low bit rate.