• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.819-833
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• 2013

Computing global features based on local features using a bio-inspired framework has shown promising performance. However, for some tough applications with large intra-class variances, a single local feature is inadequate to represent all the attributes of the images. To integrate the complementary abilities of multiple local features, in this paper we have extended the efficacy of the bio-inspired framework, HMAX, to adapt heterogeneous features for global feature extraction. Given multiple global features, we propose an approach, designated as parameterized metric learning, for high dimensional feature fusion. The fusion parameters are solved by maximizing the canonical correlation with respect to the parameters. Experimental results show that our method achieves significant improvements over the benchmark bio-inspired framework, HMAX, and other related methods on the Caltech dataset, under varying numbers of training samples and feature elements.

• 대한전자공학회논문지SP
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• 제40권4호
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• pp.266-274
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• 2003

전방 관측 적외선 영상에서 가려짐이 없는 표적과 부분적으로 가려진 표적을 식별하기 위해 국부적 표적 경계선에 대한 거리함수의 푸리에기술자와 다중의 다층 퍼셉트론을 사용한 특징정보 융합 방법을 제안한다. 표적을 배경으로부터 분리한 후에 표적 경계선의 중심을 기준으로 푸리에 기술자를 구해 전역적 특징으로 사용한다. 국부적인 형상 특징을 찾기 위해 표적 경계선을 분할하여 4개의 국부적 경계선을 만들고, 각 국부적 경계선에서 두 개의 극단점이 이루는 직선과 경계선 픽셀로부터 거리함수를 정의한다. 거리함수에 대한 푸리에 기술자를 국부적 형상특징으로 사용한다. 1개의 광역적 특징 백터와 4개의 국부적 특징 백터를 정의하고 다중의 다층 퍼셉트론을 사용하여 특징정보들을 융합함으로써 최종 표적식별 결과를 얻는다. 실험을 통해 기존의 특징벡터들에 의한 표적식별 방법과 비교하여 제안한 방법의 우수성을 입증한다.

• 대한원격탐사학회지
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• 제36권4호
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• pp.545-555
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• 2020

This paper focuses on automatic image registration between EO (Electro-Optical) and IR (InfraRed) satellite images with different spectral properties using block-based approach and simple preprocessing technique to enhance the performance of feature matching. If unpreprocessed EO and IR images from Kompsat-3A satellite were applied to local feature matching algorithms(Scale Invariant Feature Transform, Speed-Up Robust Feature, etc.), image registration algorithm generally failed because of few detected feature points or mismatched pairs despite of many detected feature points. In this paper, we proposed a new image registration method which improved the performance of feature matching with block-based registration process on 9-divided image and pre-processing technique based on adaptive histogram equalization. The proposed method showed better performance than without our proposed technique on visual inspection and I-RMSE. This study can be used for automatic image registration between various images acquired from different sensors.

• Journal of Mechanical Science and Technology
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• 제18권8호
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• pp.1358-1367
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• 2004

As a part II of theis research, new local inspection planning strategy is proposed in this paper based on the proposed inspection feature extraction method. In the local inspection planning stage, each feature is decomposed into its constituent geometric elements for more effective inspection planning. The local inspection planning for the decomposed features are performed to determine: (1) the suitable number of measuring points, (2) their locations, and (3) the optimum probing paths to minimize measuring errors and times. The fuzzy set theory, the Hammersley's algorithm and the TSP method are applied for the local inspection planning. Also, a new collision checking algorithm is proposed for the probe and/or probe holder based on the Z-map concept. Finally, the results are simulated and analyzed to verify the effectiveness of the proposed methods.

• 한국멀티미디어학회논문지
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• 제20권5호
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• pp.758-768
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• 2017

In this paper, we present a novel method to extract face representation based on multi-resolution spatial pyramid. In our method, a face is subdivided into increasingly finer sub-regions (local regions) and represented at multiple levels of histogram representations. To cope with misaligned problem, patch-based local descriptor extraction has been also developed in a novel way. To preserve multiple levels of detail in local characteristics and also encode holistic spatial configuration, histograms from all levels of spatial pyramid are integrated by using dimensionality reduction and feature combination, leading to our spatial-pyramid face feature representation. We incorporate our proposed face features into general face recognition pipeline and achieve state-of-the-art results on challenging face recognition problems.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 춘계학술발표대회
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• pp.106-109
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• 2009

Recently, image matching becomes important in Computer Aided Diagnosis (CAD) due to the huge amount of medical images. Specially, texture feature is useful in medical image matching. However, texture features such as co-occurrence matrices can't describe well the spatial distribution of gray levels of the neighborhood pixels. In this paper we propose a frequency domain-based texture feature extractor that describes the local spatial distribution for medical image retrieval. This method is based on 2D Local Discrete Fourier transform of local images. The features are extracted from local Fourier histograms that generated by four Fourier images. Experimental results using 40 classes Brodatz textures and 1 class of Emphysema CT images show that the average accuracy of retrieval is about 93%.

• 한국멀티미디어학회논문지
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• 제19권5호
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• pp.848-855
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• 2016

Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.

• 대한전자공학회논문지
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• 제24권5호
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• pp.856-862
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• 1987

An local adaptive image segmentatin algorithm for local feature detection and effective clustering of unimodal histogram shape are proposed. Local adaptive difference image and its histogram are obtained from the input image. The parameters are derived from the histogram and used for the segmentation based on relaxatin process. The results showed effective region segmentation and good noise cleaning for the ocular fundus fluorescein angiogram which has low contrast and unimodal histogram.

• 한국멀티미디어학회논문지
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• 제16권5호
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• pp.591-600
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• 2013

This paper addresses a feature-based robust watermarking scheme for digital images using a local invariant features of SURF (Speeded-Up Robust Feature) descriptor. In general, the feature invariance is exploited to achieve robustness in watermarking schemes, but the leakage of information about hidden watermarks from publicly known locations and sizes of features are not considered carefully in security perspective. We propose embedding and detection methods where the watermark is bound with circular areas and inserted into extracted circular feature regions. These methods enhance the robustness since the circular watermark is inserted into the selected non-overlapping feature regions instead of entire image contents. The evaluation results for repeatability measures of SURF descriptor and robustness measures present the proposed scheme can tolerate various attacks, including signal processing and geometric distortions.

• Journal of information and communication convergence engineering
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• 제15권1호
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• pp.53-61
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• 2017

The preferred feature selection methods for text classification are filter-based. In a common filter-based feature selection scheme, unique scores are assigned to features; then, these features are sorted according to their scores. The last step is to add the top-N features to the feature set. In this paper, we propose an improved global feature selection scheme wherein its last step is modified to obtain a more representative feature set. The proposed method aims to improve the classification performance of global feature selection methods by creating a feature set representing all classes almost equally. For this purpose, a local feature selection method is used in the proposed method to label features according to their discriminative power on classes; these labels are used while producing the feature sets. Experimental results obtained using the well-known 20 Newsgroups and Reuters-21578 datasets with the k-nearest neighbor algorithm and a support vector machine indicate that the proposed method improves the classification performance in terms of a widely known metric ($F_1$).