• 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.

• 한국지능시스템학회논문지
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• 제13권3호
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• pp.281-286
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• 2003

웨이블릿 변환(Wavelet Transform)은 공간-주파수 영역에서 신호의 국소특성을 효율적으로 구현할 수 있다 하지만, 웨이블릿 변환을 패턴 인식을 위한 특징 추출에 적용할 경우, 입력 신호의 위치 이동에 따라 추출된 특징 값이 변화하게 되어 인식률이 낮아지는 결함이 있다. 본 논문에서는 웨이블릿 변환을 패턴 인식에 적용할 경우 발생하는 입력 신호의 위치 이동에 따른 문제점을 보완하여 노이즈에 강인한 홍채인식 알고리즘을 제안한다. 실험을 통하여 제안한 알고리즘의 우수성을 보여 준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권1호
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• pp.131-146
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• 2021

Multimodal biometric-based recognition has been an active topic because of its higher convenience in recent years. Due to high user convenience of finger, finger-based personal identification has been widely used in practice. Hence, taking Finger-Print (FP), Finger-Vein (FV) and Finger-Knuckle-Print (FKP) as the ingredients of characteristic, their feature representation were helpful for improving the universality and reliability in identification. To usefully fuse the multimodal finger-features together, a new robust representation algorithm was proposed based on hierarchical model. Firstly, to obtain more robust features, the feature maps were obtained by Gabor magnitude feature coding and then described by Local Binary Pattern (LBP). Secondly, the LGBP-based feature maps were processed hierarchically in bottom-up mode by variable rectangle and circle granules, respectively. Finally, the intension of each granule was represented by Local-invariant Gray Features (LGFs) and called Hierarchical Local-Gabor-based Gray Invariant Features (HLGGIFs). Experiment results revealed that the proposed algorithm is capable of improving rotation variation of finger-pose, and achieving lower Equal Error Rate (EER) in our homemade database.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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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.

• 대한전자공학회논문지SP
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• 제46권6호
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• pp.58-67
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• 2009

대응점 정합은 컴퓨터 비전에서 중요한 작업 중에 하나지만 스케일, 조명, 시점이 변한 환경에서 대응점을 찾는 과정은 매우 어렵다. 대응점 정합 알고리즘인 SURF(Speeded Up Robust Features) 기법은 SIFT(Scale Invariant Feature Transform) 기법에 비해 정합 속도가 매우 빠르고 비슷한 정합 성능을 보여 널리 사용되고 있다. 하지만 SURF 기법은 흑백 영상과 지역 공간정보를 사용하기 때문에 유사한 패턴이 존재하는 영상에서 대응점의 정합 성능이 매우 떨어진다. 이런 문제점을 해결하기 위해 본 논문에서는 강인한 컬러 특징 정보와 광역적 특징 정보를 이용하는 확장 SURF 알고리즘을 제안한다. 제안하는 알고리즘은 비슷한 패턴이 존재하더라도 색상정보과 광역 공간 정보를 추가로 사용되기 때문에 대응점 매칭 성능을 크게 향상시킨다. 본 논문에서는 제안하는 방법의 우수성을 조명과 시점이 변화하고 유사한 패턴들을 갖는 영상들에 적용하여 기존의 방법들과 비교 실험함으로서 입증하였다.

• 한국HCI학회논문지
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• 제1권2호
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• pp.27-33
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• 2006

본 논문은 회전 및 천이 불변 이미지 텍스처 검색의 새로운 방식을 소개한다. 주파수 영역의 극 좌표계에서 동일한 공간주파수에서 각도방향으로 투영을 함으로써 각도 투영법을 만들어 냈으며, 제안된 각도 투영법을 이용하여 주파수 영역에서 푸리에 계수의 합과 표준 편차를 특징벡터로 이용하였다. 각도 투영법을 쉽게 구현하기 위하여 극 좌표계에서 라돈변환이 수행된다. 실험 시 MPEG-7 데이터를 이용하였으며 그 결과는 여러 텍스처 이미지를 검 색하는데 있어서 특징을 잘 구별해 내는 결과를 보여준다. 또한 제안된 회전 및 천이불변 특징 추출 알고리듬은 등 방성 텍스처나 국부적인 방향성을 보이는 텍스처 영상 검색에서 효율적인 검색률을 보인다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.297-301
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• 2005

In this paper, we propose a method to estimate camera motion parameter based on invariant point features. Typically, feature information of image has drawbacks, it is variable to camera viewpoint, and therefore information quantity increases after time. The LM(Levenberg-Marquardt) method using nonlinear minimum square evaluation for camera extrinsic parameter estimation also has a weak point, which has different iteration number for approaching the minimal point according to the initial values and convergence time increases if the process run into a local minimum. In order to complement these shortfalls, we, first propose constructing feature models using invariant vector of geometry. Secondly, we propose a two-stage calculation method to improve accuracy and convergence by using homography and LM method. In the experiment, we compare and analyze the proposed method with existing method to demonstrate the superiority of the proposed algorithms.

• Journal of Information Processing Systems
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• 제11권4호
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• pp.643-654
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• 2015

Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we propose using the combination of Affine Scale Invariant Feature Transform (SIFT) and Probabilistic Similarity for face recognition under a large viewpoint change. Affine SIFT is an extension of SIFT algorithm to detect affine invariant local descriptors. Affine SIFT generates a series of different viewpoints using affine transformation. In this way, it allows for a viewpoint difference between the gallery face and probe face. However, the human face is not planar as it contains significant 3D depth. Affine SIFT does not work well for significant change in pose. To complement this, we combined it with probabilistic similarity, which gets the log likelihood between the probe and gallery face based on sum of squared difference (SSD) distribution in an offline learning process. Our experiment results show that our framework achieves impressive better recognition accuracy than other algorithms compared on the FERET database.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권6호
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• pp.1253-1272
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• 2010

In this paper, we propose a new affine shape adaptation scheme for the affine invariant feature detector, in which the convergence stability is still an opening problem. This paper examines the relation between the integration scale matrix of next iteration and the current second moment matrix and finds that the convergence stability of the method can be improved by adjusting the relation between the two matrices instead of keeping them always proportional as proposed by previous methods. By estimating and updating the shape of the integration kernel and differentiation kernel in each iteration based on the anisotropy of the current second moment matrix, we propose a coarse-to-fine affine shape adaptation scheme which is able to adjust the pace of convergence and enable the process to converge smoothly. The feature matching experiments demonstrate that the proposed approach obtains an improvement in convergence ratio and repeatability compared with the current schemes with relatively fixed integration kernel.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1420-1423
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• 2002

The classification and recognition of two-dimensional trademark patterns independently of their position, orientation, size and scale by proposing two feature vectors has been discussed. The paper presents experimentation on two feature vectors showing size- invariance and scale-invariance respectively. Both feature vectors are equally invariant to rotation as well. The feature extraction is based on local as well as global statistics of the image. These feature vectors have appealing mathematical simplicity and are versatile. The results so far have shown the best performance of the developed system based on these unique sets of feature. The goal has been achieved by segmenting the image using connected-component (nearest neighbours) algorithm. Second part of this work considers the possibility of using back propagation neural networks (BPN) for the learning and matching tasks, by simply feeding the feature vectosr. The effectiveness of the proposed feature vectors is tested with various trademarks, not used in learning phase.