• Title/Summary/Keyword: image feature descriptor

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An automatic detection method for lung nodules based on multi-scale enhancement filters and 3D shape features

  • Hao, Rui;Qiang, Yan;Liao, Xiaolei;Yan, Xiaofei;Ji, Guohua
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.1
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    • pp.347-370
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    • 2019
  • In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.

Image Similarity Retrieval using an Scale and Rotation Invariant Region Feature (크기 및 회전 불변 영역 특징을 이용한 이미지 유사성 검색)

  • Yu, Seung-Hoon;Kim, Hyun-Soo;Lee, Seok-Lyong;Lim, Myung-Kwan;Kim, Deok-Hwan
    • Journal of KIISE:Databases
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    • v.36 no.6
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    • pp.446-454
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    • 2009
  • Among various region detector and shape feature extraction method, MSER(Maximally Stable Extremal Region) and SIFT and its variant methods are popularly used in computer vision application. However, since SIFT is sensitive to the illumination change and MSER is sensitive to the scale change, it is not easy to apply the image similarity retrieval. In this paper, we present a Scale and Rotation Invariant Region Feature(SRIRF) descriptor using scale pyramid, MSER and affine normalization. The proposed SRIRF method is robust to scale, rotation, illumination change of image since it uses the affine normalization and the scale pyramid. We have tested the SRIRF method on various images. Experimental results demonstrate that the retrieval performance of the SRIRF method is about 20%, 38%, 11%, 24% better than those of traditional SIFT, PCA-SIFT, CE-SIFT and SURF, respectively.

Texture Classification Using Local Neighbor Differences (지역 근처 차이를 이용한 텍스쳐 분류에 관한 연구)

  • Saipullah, Khairul Muzzammil;Peng, Shao-Hu;Park, Min-Wook;Kim, Deok-Hwan
    • Annual Conference of KIPS
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    • 2010.04a
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    • pp.377-380
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    • 2010
  • This paper proposes texture descriptor for texture classification called Local Neighbor Differences (LND). LND is a high discriminating texture descriptor and also robust to illumination changes. The proposed descriptor utilizes the sign of differences between surrounding pixels in a local neighborhood. The differences of those pixels are thresholded to form an 8-bit binary codeword. The decimal values of these 8-bit code words are computed and they are called LND values. A histogram of the resulting LND values is created and used as feature to describe the texture information of an image. Experimental results, with respect to texture classification accuracies using OUTEX_TC_00001 test suite has been performed. The results show that LND outperforms LBP method, with average classification accuracies of 92.3% whereas that of local binary patterns (LBP) is 90.7%.

Filtering Feature Mismatches using Multiple Descriptors (다중 기술자를 이용한 잘못된 특징점 정합 제거)

  • Kim, Jae-Young;Jun, Heesung
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.1
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    • pp.23-30
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    • 2014
  • Feature matching using image descriptors is robust method used recently. However, mismatches occur in 3D transformed images, illumination-changed images and repetitive-pattern images. In this paper, we observe that there are a lot of mismatches in the images which have repetitive patterns. We analyze it and propose a method to eliminate these mismatches. MDMF(Multiple Descriptors-based Mismatch Filtering) eliminates mismatches by using descriptors of nearest several features of one specific feature point. In experiments, for geometrical transformation like scale, rotation, affine, we compare the match ratio among SIFT, ASIFT and MDMF, and we show that MDMF can eliminate mismatches successfully.

A Shape Based Image Retrieval Method using Phase of ART (ART의 위상 정보를 이용한 형태기반 영상 검색 방법)

  • Lee, Jong-Min;Kim, Whoi-Yul
    • Journal of Broadcast Engineering
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    • v.17 no.1
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    • pp.26-36
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    • 2012
  • Since shape of an object in an image carries important information in contents based image retrieval (CBIR), many shape description methods have been proposed to retrieve images using shape information. Among the existing shape based image retrieval methods, the method which employs invariant Zernike moment desciptor (IZMD) showed better performance compared to other methods which employ traditional Zernike moments descriptor in CBIR. In this paper, we propose a new image retrieval method which applies invariant angular radial transform descriptor (IARTD) to obtain higher performance than the method which employs IZMD in CBIR. IARTD is a rotationally invariant feature which consists of magnitudes and alligned phases of angular radial transform coefficients. To produce rotationally invariant phase coefficients, a phase correction scheme is performed while extracting the IARTD. The distance between two IARTDs is defined by combining the differences of the magnitudes and the aligned phases. Through the experiment using MPEG-7 shape dataset, the average bull's eye performance (BEP) of the proposed method is 0.5806 while the average BEPs of the exsiting methods which employ IZMD and traditional ART are 0.4234 and 0.3574, respectively.

Improving Matching Performance of SURF Using Color and Relative Position (위치와 색상 정보를 사용한 SURF 정합 성능 향상 기법)

  • Lee, KyungSeung;Kim, Daehoon;Rho, Seungmin;Hwang, Eenjun
    • Journal of Advanced Navigation Technology
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    • v.16 no.2
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    • pp.394-400
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    • 2012
  • SURF is a robust local invariant feature descriptor and has been used in many applications such as object recognition. Even though this algorithm has similar matching accuracy compared to the SIFT, which is another popular feature extraction algorithm, it has advantage in matching time. However, these descriptors do not consider relative location information of extracted interesting points to guarantee rotation invariance. Also, since they use gray image of original color image, they do not use the color information of images, either. In this paper, we propose a method for improving matching performance of SURF descriptor using the color and relative location information of interest points. The location information is built from the angles between the line connecting the centers of interest points and the orientation line constructed for the center of each interest points. For the color information, color histogram is constructed for the region of each interest point. We show the performance of our scheme through experiments.

Improved Feature Selection Techniques for Image Retrieval based on Metaheuristic Optimization

  • Johari, Punit Kumar;Gupta, Rajendra Kumar
    • International Journal of Computer Science & Network Security
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    • v.21 no.1
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    • pp.40-48
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    • 2021
  • Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

A Study on Image Retrieval Method Using Texture Descriptor (질감 기술자를 이용한 영상 검색 기법에 관한 연구)

  • Cho, Jae-Hoon;Chong, Hyun-Jin;Kim, Young-Seop
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.745-746
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    • 2008
  • In the last few years rapid improvements in hardware technology have made it possible to process, store and retrieve huge amounts of data ina multimedia format. As a result, Content-Based Image Retrieval(CBIR) has been receiving widespred interest during the last decade. This paper propose the content-based retrieval system as a method for performing image retrieval throught the effective feature analysis of the object of significant meaning by using texture descriptor.

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An Edge Histogram Descriptor for MPEG-7 (MPEG-7을 위한 에지 히스토그램 서술자)

  • 박동권;전윤석;박수준;원치선
    • Journal of Broadcast Engineering
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    • v.5 no.1
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    • pp.31-40
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    • 2000
  • In this paper, we propose an edge histogram to efficiently represent the edge distribution in the image for MPEG-7. To this end, we adopt global, semi-global, and local edge histogram bins. Also, we extract the edge information from the image in terms of image blocks rather than pixels, which reduces the extraction complexity and is also applicable to the block-based compression standards such as MPEG-1, and 2. Experimental results show that the proposed method yields better retrieval accuracy and feature extraction speed comparing to other non-homogeneous texture descriptors of MPEG-7 including the wavelet-based descriptor and local edge-based descriptor.

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Hand Gesture Recognition using Optical Flow Field Segmentation and Boundary Complexity Comparison based on Hidden Markov Models

  • Park, Sang-Yun;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
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    • v.14 no.4
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    • pp.504-516
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    • 2011
  • In this paper, we will present a method to detect human hand and recognize hand gesture. For detecting the hand region, we use the feature of human skin color and hand feature (with boundary complexity) to detect the hand region from the input image; and use algorithm of optical flow to track the hand movement. Hand gesture recognition is composed of two parts: 1. Posture recognition and 2. Motion recognition, for describing the hand posture feature, we employ the Fourier descriptor method because it's rotation invariant. And we employ PCA method to extract the feature among gesture frames sequences. The HMM method will finally be used to recognize these feature to make a final decision of a hand gesture. Through the experiment, we can see that our proposed method can achieve 99% recognition rate at environment with simple background and no face region together, and reduce to 89.5% at the environment with complex background and with face region. These results can illustrate that the proposed algorithm can be applied as a production.