• 반도체디스플레이기술학회지
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• 제12권3호
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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.

• 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.

• ETRI Journal
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• 제38권3호
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• pp.502-509
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• 2016

Feature detection and description are key ingredients of common image processing and computer vision applications. Most existing algorithms focus on robust feature matching under challenging conditions, such as inplane rotations and scale changes. Consequently, they usually fail when the scene is blurred by camera shake or an object's motion. To solve this problem, we propose a new feature description algorithm that is robust to image blur and significantly improves the feature matching performance. The proposed algorithm builds a feature descriptor by considering the integral projection along four angular directions ($0^{\circ}$, $45^{\circ}$, $90^{\circ}$, and $135^{\circ}$) and by combining four projection vectors into a single highdimensional vector. Intensive experiment shows that the proposed descriptor outperforms existing descriptors for different types of blur caused by linear motion, nonlinear motion, and defocus. Furthermore, the proposed descriptor is robust to intensity changes and image rotation.

• 한국정밀공학회지
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• 제23권5호
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• pp.68-76
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• 2006

The ability to recognize human emotion is one of the hallmarks of human-robot interaction. Hence this paper describes the realization of emotion recognition. For emotion recognition from voice, we propose a new feature called frequency range of meaningful signal. With this feature, we reached average recognition rate of 76% in speaker-dependent. From the experimental results, we confirm the usefulness of the proposed feature. We also define the noise environment and conduct the noise-environment test. In contrast to other features, the proposed feature is robust in a noise-environment.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2015년도 하계학술대회
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• pp.9-12
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• 2015

본 논문에서는 FAST(Features from Accelerated Segment Test) 특징점 검출기와 SURF 특징점 표현자(descriptor)를 수정하고 조합하여 영상의 왜곡에 강인하면서 정합을 수행할 수 있는 새로운 특징점 정합 기법을 제안한다. 스케일 공간을 생성하여 스케일 변화를 고려하고 잡음에 강인하기 위해 영상에서 특징점 후보군을 결정한다. 기존의 FAST는 에지 부분에서 특징점을 많이 검출하게 되는데 이러한 단점을 주곡률(principal curvatures)을 적용하여 개선하고자 한다. 또한 영상의 회전 변화에 강인하기 위해 SURF 특징점 표현자를 사용한다. 제안하는 정합 기법은 적은 계산량으로 기존의 특징점 정합 기법보다 우수한 성능을 나타낸다. 특별히 잡음이 존재하는 영상에서의 정합에 강인함을 보여준다.

• 전자공학회논문지SC
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• 제45권6호
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• pp.67-76
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• 2008

성공적인 상업화를 위해서는 다양한 조명 환경에서 신뢰성 있는 얼굴 인식이 필요하다. 특징 벡터 기반 얼굴 인식에서 특징 벡터를 잘 선택하는 것은 중요하다. 가버 특징 벡터는 다른 특징 벡터보다도 상대적으로 방향, 자세, 조명 등의 영향을 덜 받는 것으로 잘 알려져 있어 얼굴 인식의 특징 벡터로 많이 이용된다. 그러나 조명의 영향에 대해 완전히 독립적이지 못하다. 본 논문에서는 얼굴 이미지의 가버 특징 벡터에 대한 조명 PCA 모델의 구성을 제안하고 이를 이용하여 조명에 독립적인 얼굴 고유의 특성을 나타내는 가버 특징 벡터만을 분리해내고 이를 이용한 얼굴 인식 방법을 제시한다. 가버 특징 벡터 조명 PCA 모델은 가버 특징 벡터공간을 조명 영향 부분공간과 얼굴 고유특성 부분공간의 직교 분해로 구성한다. 얼굴 고유특성 부분공간으로 투영하여 얻어진 가버 특징 벡터는 조명 영향을 분리해 내었기 때문에 이를 이용한 얼굴 인식은 조명에 보다 강인하게 된다. 실험을 통해서 가버 특징 벡터 조명 PCA 모델을 이용한 제안된 얼굴 인식 방식이 다양한 자세에서 조명에 대해 보다 신뢰성 있게 동작함을 확인하였다.

• 한국멀티미디어학회논문지
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• 제7권7호
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• pp.877-885
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• 2004

본 논문은 여러 가지 장면 검출 방식들 중 강인한 특징 변수들의 선별과 신경망을 이용하여 향상된 장면 전환점 검출 기법을 제안한다. 기존의 장면 전환점 검출 방식에서는 인접한 프레임 간에 단일 특징과 고정된 임계값을 주로 사용하였다. 하지만, 비디오 시퀀스 내의 장면 전환점에서는 인접한 프레임 간의 내용(content)인 컬러, 모양, 배경 혹은 질감 등이 동시에 변화한다. 따라서 단일 특징보다는 상호 보완 관계를 갖는 강인한 특징을 이용하여 장면 전환점을 효율적으로 검출한다. 본 논문에서 강인한 특징 변수들을 선택하기 위해, 데이터 마이닝 기법 중 대표적인 CART(classification and regression tree)를 이용하고, 다차원 변수에 따른 임계값을 선정하기 위해 역전파 신경망(backpropagation neural net)을 이용한다. 제안한 방식과 대표적인 특징 추출인 PCA(principal component analysis)기법을 비교하여 특징 변수의 추출 성능을 평가한다. 실험 결과에 따라 제안된 방식이 PCA 기법과 비교하여 우수한 성능이 나타남을 확인한다.

• Journal of International Society for Simulation Surgery
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• 제2권1호
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• pp.7-9
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• 2015

Purpose In the many face-related application such as head pose estimation, 3D face modeling, facial appearance manipulation, the robust and fast facial feature extraction is necessary. We present the facial feature extraction method based on shape regression and feature selection for real-time facial feature extraction. Materials and Methods The facial features are initialized by statistical shape model and then the shape of facial features are deformed iteratively according to the texture pattern which is selected on the feature pool. Results We obtain fast and robust facial feature extraction result with error less than 4% and processing time less than 12 ms. The alignment error is measured by average of ratio of pixel difference to inter-ocular distance. Conclusion The accuracy and processing time of the method is enough to apply facial feature based application and can be used on the face beautification or 3D face modeling.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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• pp.13-16
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• 2000

This paper presents a robust method to combine a collection of images with small fields of view to obtain an image with a large field of view. In the previous works, there are two main areas which one is a cross correlation-based method and the other is a feature-based method. The former is based on motion estimation from video sequences. so there are a problem on rotating a camera about optical axis. In the latter method, it is difficult to match correspondence feature points correctly.'re find correct correspondences, we proposed the geometrical feature model and correspondence filters and the Gaussian distribution weight function to blend the images smoothly. The experiments show that our method is robust and effective.

• 음성과학
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• 제11권1호
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• pp.7-20
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• 2004

In this paper, we propose a two-step noise compensation algorithm in feature extraction for achieving robust speech recognition. The proposed method frees us from requiring a priori information on noisy environments and is simple to implement. First, in frequency domain, the Harmonics-based Spectral Subtraction (HSS) is applied so that it reduces the additive background noise and makes the shape of harmonics in speech spectrum more pronounced. We then apply a judiciously weighted variance Feature Vector Normalization (FVN) to compensate for both the channel distortion and additive noise. The weighted variance FVN compensates for the variance mismatch in both the speech and the non-speech regions respectively. Representative performance evaluation using Aurora 2 database shows that the proposed method yields 27.18% relative improvement in accuracy under a multi-noise training task and 57.94% relative improvement under a clean training task.