• Title, Summary, Keyword: image feature

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Recent Advances in Feature Detectors and Descriptors: A Survey

  • Lee, Haeseong;Jeon, Semi;Yoon, Inhye;Paik, Joonki
    • IEIE Transactions on Smart Processing and Computing
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    • v.5 no.3
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    • pp.153-163
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    • 2016
  • Local feature extraction methods for images and videos are widely applied in the fields of image understanding and computer vision. However, robust features are detected differently when using the latest feature detectors and descriptors because of diverse image environments. This paper analyzes various feature extraction methods by summarizing algorithms, specifying properties, and comparing performance. We analyze eight feature extraction methods. The performance of feature extraction in various image environments is compared and evaluated. As a result, the feature detectors and descriptors can be used adaptively for image sequences captured under various image environments. Also, the evaluation of feature detectors and descriptors can be applied to driving assistance systems, closed circuit televisions (CCTVs), robot vision, etc.

Feature Extraction for Endoscopic Image by using the Scale Invariant Feature Transform(SIFT) (SIFT를 이용한 내시경 영상에서의 특징점 추출)

  • Oh, J.S.;Kim, H.C.;Kim, H.R.;Koo, J.M.;Kim, M.G.
    • Proceedings of the KIEE Conference
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    • pp.6-8
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    • 2005
  • Study that uses geometrical information in computer vision is lively. Problem that should be preceded is matching problem before studying. Feature point should be extracted for well matching. There are a lot of methods that extract feature point from former days are studied. Because problem does not exist algorithm that is applied for all images, it is a hot water. Specially, it is not easy to find feature point in endoscope image. The big problem can not decide easily a point that is predicted feature point as can know even if see endoscope image as eyes. Also, accuracy of matching problem can be decided after number of feature points is enough and also distributed on whole image. In this paper studied algorithm that can apply to endoscope image. SIFT method displayed excellent performance when compared with alternative way (Affine invariant point detector etc.) in general image but SIFT parameter that used in general image can't apply to endoscope image. The gual of this paper is abstraction of feature point on endoscope image that controlled by contrast threshold and curvature threshold among the parameters for applying SIFT method on endoscope image. Studied about method that feature points can have good distribution and control number of feature point than traditional alternative way by controlling the parameters on experiment result.

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Face Recognition using the Feature Space and the Image Vector (세그멘테이션에 의한 특징공간과 영상벡터를 이용한 얼굴인식)

  • 김선종
    • Journal of Institute of Control, Robotics and Systems
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    • v.5 no.7
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    • pp.821-826
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    • 1999
  • This paper proposes a face recognition method using feature spaces and image vectors in the image plane. We obtain the 2-D feature space using the self-organizing map which has two inputs from the axis of the given image. The image vector consists of its weights and the average gray levels in the feature space. Also, we can reconstruct an normalized face by using the image vector having no connection with the size of the given face image. In the proposed method, each face is recognized with the best match of the feature spaces and the maximum match of the normally retrieval face images, respectively. For enhancing recognition rates, our method combines the two recognition methods by the feature spaces and the retrieval images. Simulations are conducted on the ORL(Olivetti Research laboratory) images of 40 persons, in which each person has 10 facial images, and the result shows 100% recognition and 14.5% rejection rates for the 20$\times$20 feature sizes and the 24$\times$28 retrieval image size.

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An approach for improving the performance of the Content-Based Image Retrieval (CBIR)

  • Jeong, Inseong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.30 no.6_2
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    • pp.665-672
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    • 2012
  • Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

Enhancement of Stereo Feature Matching using Feature Windows and Feature Links (특징창과 특징링크를 이용한 스테레오 특징점의 정합 성능 향상)

  • Kim, Chang-Il;Park, Soon-Yong
    • The KIPS Transactions:PartB
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    • v.19B no.2
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    • pp.113-122
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    • 2012
  • This paper presents a new stereo matching technique which is based on the matching of feature windows and feature links. The proposed method uses the FAST feature detector to find image features in stereo images and determines the correspondences of the detected features in the stereo images. We define a feature window which is an image region containing several image features. The proposed technique consists of two matching steps. First, a feature window is defined in a standard image and its correspondence is found in a reference image. Second, the corresponding features between the matched windows are determined by using the feature link technique. If there is no correspondence for an image feature in the standard image, it's disparity is interpolated by neighboring feature sets. We evaluate the accuracy of the proposed technique by comparing our results with the ground truth of in a stereo image database. We also compare the matching accuracy and computation time with two conventional feature-based stereo matching techniques.

Matching Of Feature Points using Dynamic Programming (동적 프로그래밍을 이용한 특징점 정합)

  • Kim, Dong-Keun
    • The KIPS Transactions:PartB
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    • v.10B no.1
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    • pp.73-80
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    • 2003
  • In this paper we propose an algorithm which matches the corresponding feature points between the reference image and the search image. We use Harris's corner detector to find the feature points in both image. For each feature point in the reference image, we can extract the candidate matching points as feature points in the starch image which the normalized correlation coefficient goes greater than a threshold. Finally we determine a corresponding feature points among candidate points by using dynamic programming. In experiments we show results that match feature points in synthetic image and real image.

Image Feature Extraction Using Energy field Analysis (에너지장 해석을 통한 영상 특징량 추출 방법 개발)

  • 김면희;이태영;이상룡
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.404-406
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    • 2002
  • In this paper, the method of image feature extraction is proposed. This method employ the energy field analysis, outlier removal algorithm and ring projection. Using this algorithm, we achieve rotation-translation-scale invariant feature extraction. The force field are exploited to automatically locate the extrema of a small number of potential energy wells and associated potential channels. The image feature is acquired from relationship of local extrema using the ring projection method.

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Comparative Analysis of the Performance of SIFT and SURF (SIFT 와 SURF 알고리즘의 성능적 비교 분석)

  • Lee, Yong-Hwan;Park, Je-Ho;Kim, Youngseop
    • Journal of the Semiconductor & Display Technology
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    • v.12 no.3
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    • pp.59-64
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    • 2013
  • Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

Panoramic Image Stitching Using Feature Extracting and Matching on Embedded System

  • Lee, June-Hwan
    • Transactions on Electrical and Electronic Materials
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    • v.18 no.5
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    • pp.273-278
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    • 2017
  • Recently, one of the areas where research is being actively conducted is the Internet of Things (IoT). The field of using the Internet of Things system is increasing, coupled with a remarkable increase of the use of the camera. However, general cameras used in the Internet of Things have limited viewing angles as compared to those available to the human eye. Also, cameras restrict observation of objects and the performance of observation. Therefore, in this paper, we propose a panoramic image stitching method using feature extraction and matching based on an embedded system. After extracting the feature of the image, the speed of image stitching is improved by reducing the amount of computation using the necessary information so that it can be used in the embedded system. Experimental results show that it is possible to improve the speed of feature matching and panoramic image stitching while generating a smooth image.

Depth Image Based Feature Detection Method Using Hybrid Filter (융합형 필터를 이용한 깊이 영상 기반 특징점 검출 기법)

  • Jeon, Yong-Tae;Lee, Hyun;Choi, Jae-Sung
    • IEMEK Journal of Embedded Systems and Applications
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    • v.12 no.6
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    • pp.395-403
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    • 2017
  • Image processing for object detection and identification has been studied for supply chain management application with various approaches. Among them, feature pointed detection algorithm is used to track an object or to recognize a position in automated supply chain systems and a depth image based feature point detection is recently highlighted in the application. The result of feature point detection is easily influenced by image noise. Also, the depth image has noise itself and it also affects to the accuracy of the detection results. In order to solve these problems, we propose a novel hybrid filtering mechanism for depth image based feature point detection, it shows better performance compared with conventional hybrid filtering mechanism.