Scale Invariant Feature Transform을 이용한 눈동자 영역 추출

정재진,황의성,주동현,김두영 동아대학교 정보기술연구소 2006 情報技術硏究所論文誌 Vol.14 No.1

This paper is the eye region which is used with confirmation element of face image it uses the SIFT and it extracting method proposes. The eye region image where becomes model and it composes the keypoint descriptor which is a SIFT result of the input image which it will extract. It uses the keypoint descriptor which and creates it composes a each feature vector, and the judgement where affine transform should have existed on after two feature points which adjusts with each other, against the feature point field which confront above half as the case eye region where the with each other same affine transform will exist it judged. There is not a test result learning process, more quickly recognition method of existing methods than the possibility of getting the result which extracts the region it was.

Fourier Transform을 이용한 3차원 폐곡면 객체의 특징 벡터 추출

이준복,김문화,장동식 한국융합신호처리학회 2003 융합신호처리학회 논문지 (JISPS) Vol.4 No.3

본 논문은 퓨리에 변환을 이용한 3차원 폐곡면 객체의 특징 벡터 추출 기법을 제시한다. 특징 벡터는 3차원극좌표계를 이용하여 폐곡면 객체의 회전각도별 내측거리값을 퓨리에 변환을 통해 주파수 영역으로 변환하여 추출한다. 특징 벡터는 폐곡면 표면점과 중심점과의 관계를 나타내는 내측거리값을 활용하므로 위치 이동에 불변이고 내측거리값은 퓨리에 변환 전 정규화되기 때문에 크기 변화에 불변이며 퓨리에 변환 후 파워 스펙트럼을 적용하여 회전 변화 불변임을 보여주고 있다. 실험 결과 위치 이동, 크기 변화, 회전 변화에 불변임을 알 수 있고 서로 상이한 객체간에 변별력이 있어 객체 고유의 특징 벡터로써 활용이 가능함을 제시한다. A new method to realize 3-dimensional object pattern recognition system using Fourier-based feature extractor has been proposed. The procedure to obtain the invariant feature vector is as follows ; A closed surface is generated by tracing the surface of object using the 3-dimensional polar coordinate. The centroidal distances between object's geometrical center and each closed surface points are calculated. The distance vector is translation invariant. The distance vector is normalized, so the result is scale invariant. The Fourier spectrum of each normalized distance vector is calculated, and the spectrum is rotation invariant. The Fourier-based feature generating from above procedure completely eliminates the effect of variations in translation, scale, and rotation of 3-dimensional object with closed-surface. The experimental results show that the proposed method has a high accuracy.

SIFT와 SURF 알고리즘의 성능적 비교 분석

이용환,김영섭,박제호 한국반도체디스플레이기술학회 2013 반도체디스플레이기술학회지 Vol.12 No.3

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.

기동표적에 대한 ISAR Cross-Range Scaling

강병수(Byung-Soo Kang),배지훈(Ji-Hoon Bae),김경태(Kyung-Tae Kim),양은정(Eun-Jung Yang) 한국전자파학회 2014 한국전자파학회논문지 Vol.25 No.10

본 논문에서는 두 개의 순차적인 inverse synthetic aperture radar(ISAR) 영상들을 활용하여 표적의 회전 속도(Rotation Velocity: RV) 추정을 통한 수직 거리 스케일링(cross-range scaling: CRS)을 수행한다. 순차적으로 형성된 두 개의 ISAR 영상들에 각각 scale invariant feature transform(SIFT)를 적용함으로써 관측각도의 변화에 강인한 산란원(scatterer)들을 추출한다. 추출된 산란원과 각 영상 내 표적의 회전 중심(Rotation Center: RC) 사이의 거리가 같다는 점을 이용하여 비용함수(cost function)를 설정한 후, 전역 탐색 기법(exhaustive search method)과 결합된 particle swarm optimization(PSO)의 최적화를 통해 표적의 RV를 RC 정보 없이 추정한다. 시뮬레이션에서는 시나리오 기반으로 기동하는 표적에 대한 ISAR 영상형성 후, 제안된 기법을 통해 RC의 정보 없이 RV를 추정함으로써 ISAR 영상의 CRS가 성공적으로 수행됨을 보여준다. In this paper, a novel approach estimating target’s rotation velocity(RV) is proposed for inverse synthetic aperture radar(ISAR) cross-range scaling(CRS). Scale invariant feature transform(SIFT) is applied to two sequently generated ISAR images for extracting non-fluctuating scatterers. Considering the fact that the distance between target’s rotation center(RC) and SIFT features is same, we can set a criterion for estimating RV. Then, the criterion is optimized through the proposed method based on particle swarm optimization(PSO) combined with exhaustive search method. Simulation results show that the proposed algorithm can precisely estimate RV of a scenario based maneuvering target without RC information. With the use of the estimated RV, ISAR image can be correctly re-scaled along the cross-range direction.

Experimental Optimal Choice Of Initial Candidate Inliers Of The Feature Pairs With Well-Ordering Property For The Sample Consensus Method In The Stitching Of Drone-based Aerial Images

( Byeong-chun Shin ),( Jeong-kweon Seo ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.4

There are several types of image registration in the sense of stitching separated images that overlap each other. One of these is feature-based registration by a common feature descriptor. In this study, we generate a mosaic of images using feature-based registration for drone aerial images. As a feature descriptor, we apply the scale-invariant feature transform descriptor. In order to investigate the authenticity of the feature points and to have the mapping function, we employ the sample consensus method; we consider the sensed image’s inherent characteristic such as the geometric congruence between the feature points of the images to propose a novel hypothesis estimation of the mapping function of the stitching via some optimally chosen initial candidate inliers in the sample consensus method. Based on the experimental results, we show the efficiency of the proposed method compared with benchmark methodologies of random sampling consensus method (RANSAC); the well-ordering property defined in the context and the extensive stitching examples have supported the utility. Moreover, the sample consensus scheme proposed in this study is uncomplicated and robust, and some fatal miss stitching by RANSAC is remarkably reduced in the measure of the pixel difference.

인공지능 : 얼굴인식을 위한 어파인 불변 지역 서술자

고용빈 ( Yong Bin Gao ),이효종 ( Hyo Jong Lee ) 한국정보처리학회 2014 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.3 No.9

Under controlled environment, such as fixed viewpoints or consistent illumination, the performance of face recognition is usually high enough to be acceptable nowadays. Face recognition is, however, a still challenging task in real world. SIFT(Scale Invariant Feature Transformation) algorithm is scale and rotation invariant, which is powerful only in the case of small viewpoint changes. However, it often fails when viewpoint of faces changes in wide range. In this paper, we use Affine SIFT (Scale Invariant Feature Transformation; ASIFT) to detect affine invariant local descriptors for face recognition under wide viewpoint changes. The ASIFT is an extension of SIFT algorithm to solve this weakness. In our scheme, ASIFT is applied only to gallery face, while SIFT algorithm is applied to probe face. ASIFT generates a series of different viewpoints using affine transformation. Therefore, the ASIFT allows viewpoint differences between gallery face and probe face. Experiment results showed our framework achieved higher recognition accuracy than the original SIFT algorithm on FERET database.

Parameter Optimization for the Extraction of Matching Points Between High-Resolution Multisensor Images in Urban Areas

Youkyung Han,Jaewan Choi,Younggi Byun,Yongil Kim IEEE 2014 IEEE transactions on geoscience and remote sensing Vol.52 No.9

<P>The objective of this paper is to extract a suitable number of evenly distributed matched points, given the characteristics of the site and the sensors involved. The intent is to increase the accuracy of automatic image-to-image registration for high-resolution multisensor data. The initial set of matching points is extracted using a scale-invariant feature transform (SIFT)-based method, which is further used to evaluate the initial geometric relationship between the features of the reference and sensed images. The precise matching points are extracted considering location differences and local properties of features. The values of the parameters used in the precise matching are optimized using an objective function that considers both the distribution of the matching points and the reliability of the transformation model. In case studies, the proposed algorithm extracts an appropriate number of well-distributed matching points and achieves a higher correct-match rate than the SIFT method. The registration results for all sensors are acceptably accurate, with a root-mean-square error of less than 1.5 m.</P>

조영 전후의 폐 CT 영상 정합을 위한 특징 기반의 비강체 정합 기법

이현준,홍영택,심학준,권동진,윤일동,이상욱,김남국,서준범,Lee, Hyun-Joon,Hong, Young-Taek,Shim, Hack-Joon,Kwon, Dong-Jin,Yun, Il-Dong,Lee, Sang-Uk,Kim, Nam-Kug,Seo, Joon-Beom 대한의용생체공학회 2011 의공학회지 Vol.32 No.3

In this paper, a feature-based registration technique is proposed for pre-contrast and post-contrast lung CT images. It utilizes three dimensional(3-D) features with their descriptors and estimates feature correspondences by nearest neighborhood matching in the feature space. We design a transformation model between the input image pairs using a free form deformation(FFD) which is based on B-splines. Registration is achieved by minimizing an energy function incorporating the smoothness of FFD and the correspondence information through a non-linear gradient conjugate method. To deal with outliers in feature matching, our energy model integrates a robust estimator which discards outliers effectively by iteratively reducing a radius of confidence in the minimization process. Performance evaluation was carried out in terms of accuracy and efficiency using seven pairs of lung CT images of clinical practice. For a quantitative assessment, a radiologist specialized in thorax manually placed landmarks on each CT image pair. In comparative evaluation to a conventional feature-based registration method, our algorithm showed improved performances in both accuracy and efficiency.

Viewpoint Unconstrained Face Recognition Based on Affine Local Descriptors and Probabilistic Similarity

( Yong Bin Gao ),( Hyo Jong Lee ) 한국정보처리학회 2015 Journal of information processing systems Vol.11 No.4

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.

Viewpoint Unconstrained Face Recognition Based on Affine Local Descriptors and Probabilistic Similarity

Gao, Yongbin,Lee, Hyo Jong Korea Information Processing Society 2015 Journal of information processing systems Vol.11 No.4

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.