• 제어로봇시스템학회논문지
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• 제22권4호
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• pp.288-292
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• 2016

In this paper, we propose an infrared image based human victim recognition method for a search and rescue robot in dark environments, like general disaster situations. For recognizing a human victim, an infrared camera on a RGB-D camera, Microsoft Kinect, is used. The contrast and brightness of the infrared image are first improved by histogram equalization, and the noise on the image is removed by morphological operation and Gaussian filtering. For recognizing a human victim, the binarization and blob labeling methods are applied to the improved image. Finally, for verifying the effectiveness and feasibility of the proposed method, an experiment for human victim recognition is carried out in a dark environment.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.810-813
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• 2004

Indexing and Retrieving images from large and varied collections using image content as a key is a challenging and important problem in computer vision application. In this paper, a color Content-based Image Retrieval (CBIR) system using hierarchical Region of Interest (ROI) query and indexing is presented. During indexing process, First, The ROIs on every image in the image database are extracted using a region-based image segmentation technique, The JSEG approach is selected to handle this problem in order to create color-texture regions. Then, Color features in form of histogram and correlogram are then extracted from each segmented regions. Finally, The features are stored in the database as the key to retrieve the relevant images. As in the retrieval system, users are allowed to select ROI directly over the sample or user's submission image and the query process then focuses on the content of the selected ROI in order to find those images containing similar regions from the database. The hierarchical region-of-interest query is performed to retrieve the similar images. Two-level search is exploited in this paper. In the first level, the most important regions, usually the large regions at the center of user's query, are used to retrieve images having similar regions using static search. This ensures that we can retrieve all the images having the most important regions. In the second level, all the remaining regions in user's query are used to search from all the retrieved images obtained from the first level. The experimental results using the indexing technique show good retrieval performance over a variety of image collections, also great reduction in the amount of searching time.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.65-74
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• 2021

Digital image processing and retrieving have increasingly become very popular on the Internet and getting more attention from various multimedia fields. That results in additional privacy requirements placed on efficient image matching techniques in various applications. Hence, several searching methods have been developed when confidential images are used in image matching between pairs of security agencies, most of these search methods either limited by its cost or precision. This study proposes a secure and efficient method that preserves image privacy and confidentially between two communicating parties. To retrieve an image, feature vector is extracted from the given query image, and then the similarities with the stored database images features vector are calculated to retrieve the matched images based on an indexing scheme and matching strategy. We used a secure content-based image retrieval features detector algorithm called Speeded-Up Robust Features (SURF) algorithm over public cloud to extract the features and the Honey Encryption algorithm. The purpose of using the encrypted images database is to provide an accurate searching through encrypted documents without needing decryption. Progress in this area helps protect the privacy of sensitive data stored on the cloud. The experimental results (conducted on a well-known image-set) show that the performance of the proposed methodology achieved a noticeable enhancement level in terms of precision, recall, F-Measure, and execution time.

• 산경연구논집
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• 제11권6호
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• pp.41-53
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• 2020

Purpose: The purpose of this study is to review the previous studies on the characteristics of the image search service provided by using artificial intelligence, the social impact characteristics, and the moderating effect of perceived anthropomorphism, and conduct empirical analysis to identify the constituent factors affecting purchase intention. To clarify. Through this, I tried to present theoretical and practical implications. Research design, data, and methodology: Research design was that characteristics of image search service (ubiquity and information quality) and social impact characteristics (subjective norms, electronic word of mouth marketing) are affected by mediation of satisfaction and flow, therefore, control of perceived anthropomorphism have an effect on purchase intention to increase. For analysis, research conducted literature review, and developed questionnaires, so that EM firm which is a specialized research institute has collected data. This was conducted on 410 people between the 20s and 50s who have mobile shopping experiences. SPSS Statistics 23 and AMOS 23 had been used to perform necessary analysis such as exploratory factor analysis, reliability analysis, feasibility analysis, and structural equation modeling based on this data. Results: first, ubiquity, information quality and subjective norms were found to have a positive effect on purchase intention through satisfaction and flow parameters. Second, satisfaction and flow were found to have a mediating effect between ubiquity, information quality, and subjective norms and purchase intentions. However, there was no mediating effect between eWOM information and purchase intention. Third, perceived anthropomorphism was found to have a moderating effect between information quality and satisfaction, and it was found that there was no moderating effect on the relationship between information quality and flow. Conclusions: The information quality of image search services using artificial intelligence has a positive effect on satisfaction, and it has been found that there is a positive moderate effect of perceived anthropomorphism in this relationship, which may be an academic contribution to the distribution science utilizing artificial intelligence. Therefore, it is possible to propose a distribution strategy that improves purchase intention by utilizing image search service and anthropomorphism in practical business and providing a more enjoyable immersive experience to customers.

• 정보관리학회지
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• 제27권2호
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• pp.37-60
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• 2010

이 연구는 이미지 특성 범주와 관련하여 질의 재구성 패턴을 탐색하고자 하였다. 이러한 연구 목적을 수행하기 위해서 Excite 웹검색 엔진 로그 데이터가 사용되었으며, 총 592 세션과 2,445 질의어가 분석되었다. 데이터 분석은 Batley의 정보 형태 구분과 선행 연구에서 밝혀진 팻싯과 서브팻싯을 활용하여 수행되었다. 분석결과는 두가지 형태로 구분하여 제시되었다. 첫째, 질의 재구성에 관한 분석결과이다. 질의 분석 결과, 가장 많은 부분을 차지하는 범주는 특정어(specific)와 지칭어(nameable)이며, 이러한 경향은 다양한 정보 탐색 단계에서도 지속적으로 나타났다. 둘째, 질의 재구성 패턴과 관려하여, 평행이동이 가장 많이 나타났으며, 이러한 경향은 최초 혹은 직전 질의 범주에 따라 근소한 차이를 보였다. 범주 전환 분석에서는 높은 비율(60%-80%)로 검색 질의의 범주가 지속적으로 동일한 범주에 머무르는 경향을 밝혀내었다. 이러한 결과는 이미지 검색 시스템 설계와 구현에 있어서, 이용자의 질의 선정 과정에 도움을 제공하고 효과적인 시소러스 구축 등에 활용될 수 있을 것으로 기대된다.

• 한국산학기술학회논문지
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• 제11권7호
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• pp.2579-2585
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• 2010

본 논문은 단안 카메라로부터 입력된 영상에서 모션 기반의 검색을 사용한 동적인 사람 자세 추적 방법을 제안한다. 제안된 방법은 3차원 공간에서 하나의 사람 자세 후보를 생성하고, 생성된 자세 후보를 2차원 이미지 공간으로 투영하여, 투영된 사람 자세 후보와 입력 이미지와의 특징 값 유사성을 비교한다. 이 과정을 정해진 조건을 만족 할 때까지 반복하여 이미지와의 유사성과, 신체 부분간 연결성이 가장 좋은 3차원 자세를 추정한다. 제안된 방법에서는 입력 이미지에 적합한 3차원 자세를 검색할 때, 2차원 영상에서 추정된 신체 각 부분들의 모션 정보를 사용해 검색 공간을 정하고 정해진 검색 공간에서 탐색하여 사람의 자세를 추정한다. 2차원 이미지 모션은 비교적 높은 제약이 있어서 검색 공간을 의미있게 줄일 수 있다. 이 방법은 모션 추정이 검색 공간을 효율적으로 할당 해주고, 자세 추적이 여러 가지 다양한 모션에 적응할 수 있다는 장점을 가진다

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.135-138
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• 2020

This paper suggests a method of automatic clustering to search of relevant digital photos using geo-coded information. The provided scheme labels photo images with their corresponding global positioning system coordinates and date/time at the moment of capture, and the labels are used as clustering metadata of the images when they are in the use of retrieval. Experimental results show that geo-location information can improve the accuracy of image retrieval, and the information embedded within the images are effective and precise on the image clustering.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2010년도 한국컴퓨터종합학술대회논문집 Vol.37 No.1(A)
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• pp.246-247
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• 2010

This paper proposes a fast motion estimation algorithm based on motion speed and multiple initial center points. The proposed method predicts initial search points by means of the spatio-temporal neighboring motion vectors. A dynamic search pattern based on motion speed and the predicted initial center points is proposed to quickly obtain the motion vector. Due to the usage of the spatio-temporal information and the dynamic search pattern, the proposed method greatly accelerates the search speed while maintaining a good predicted image quality. Experimental results show that the proposed method has a good predicted image quality in terms of PSNR with less search time as compared to the Full Search, New Three-Step Search, and Four-Step Search.

• 한국비블리아학회지
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• 제25권1호
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• pp.19-37
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• 2014

본 연구는 이미지 기반 디지털 도서관의 이용자 검색 패턴을 효과적으로 분석하기 위해 이용자 검색 로그 데이터를 분석하였다. 기술 통계와 네트워크 분석 방법을 사용하여 한 달간 수집한 트랜잭션 로그 데이터를 분석하였다. 연구 결과는 이용자들이 특정 주제 내에서 검색 결과 보기와 이미지 아이템 평가를 반복적으로 수행하고 있음을 밝혀내었다. 본 연구는 이미지 자료 검색의 로그 분석을 위해 복합적 데이터 분석 방법을 이용하였다는 점에 의의가 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5692-5716
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• 2019

One of the best means to safeguard the confidentiality, security, and privacy of an image within the IoT-Cloud is through encryption. However, looking through encrypted data is a difficult process. Several techniques for searching encrypted data have been devised, but certain security solutions may not be used in IoT-Cloud because such solutions are not lightweight. We propose a lightweight scheme that can perform a content-based search of encrypted images, namely EEIRI. In this scheme, the images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two descriptor sets. To improve the search efficiency, we employ the k-means clustering technique to construct a searchable tree-based index. Our index construction process ensures the privacy of the stored data and search requests. When compared with more familiar techniques of searching images over plaintexts, EEIRI is considered to be more efficient, demonstrating a higher search cost of 7% and a decrease in search accuracy of 1.7%. Numerous empirical investigations are carried out in relation to real image collections so as to evidence our work.