• Journal of the Korea Institute of Military Science and Technology
• /
• v.10 no.2
• /
• pp.180-187
• /
• 2007

In this paper, we propose an automatic method which combines two or more images acquired by camera on the air-vehicle into a larger image mosaics. The shift, scaling, rotation factors between two images can be calculated by using the correspondences between the points of the images. In order to estimate these factors, we find the relative positions of two images with respect to each other by using the SIFT descriptor and the RANSAC algorithm. After estimating the factors, the images can be merged into a single image mosaic by warping the target image. To avoid seams when mosaics are constructed from overlapped images, we apply the average gray level value of points within a overlapped zone. We have tested our proposed method on various image sets and have confirmed that our method produced good result subjectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.699-702
• /
• 2015

다중의 영상을 이용하여 하나의 파노라마 영상을 제작하는 기법은 컴퓨터 비전, 컴퓨터 그래픽스 등과 같은 여러 분야에서 널리 연구되고 있다. 파노라마 영상은 하나의 카메라에서 얻을 수 있는 영상의 한계, 즉 예를 들어 화각, 화질, 정보량 등의 한계를 극복할 수 있는 좋은 방법으로서 가상현실, 로봇비전 등과 같이 광각의 영상이 요구되는 다양한 분야에서 응용될 수 있다. 파노라마 영상은 단일 영상과 비교하여 보다 큰 몰입감을 제공한다는 점에서 큰 의미를 갖는다. 현재 다양한 파노라마 영상 제작 기법들이 존재하지만, 대부분의 기법들이 공통적으로 파노라마 영상을 구성할 때 각 영상에 존재하는 특징점 및 대응점을 검출하는 방식을 사용하고 있다. 본 논문에서 사용한 SURF(Speeded Up Robust Features) 알고리즘은 영상의 특징점을 검출할 때 영상의 흑백정보와 지역 공간 정보를 활용하는데, 영상의 크기 변화와 시점 검출에 강하며 SIFT(Scale Invariant Features Transform) 알고리즘에 비해 속도가 빠르다는 장점이 있어서 널리 사용되고 있다. 본 논문에서는 두 영상 사이 또는 하나의 영상과 여러 영상 사이에 대응되는 매칭을 계산하여 파노라마영상을 생성하는 처리 방법을 구현하고 기술하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2020.07a
• /
• pp.300-304
• /
• 2020

본 논문에서는 원격 탐사 영상 정합에서 정확도는 유지하면서 특징점 매칭 (Matching) 복잡도를 줄이기 위해 입력 영상을 전처리하는 구조물 검출 네트워크를 이용한 원격 탐사 영상 정합 방법을 제안한다. 영상 정합의 기존 방법은 입력 영상에서 특징점을 추출하고 설명자 (Descriptor)를 생성한다. 본 논문에서 제안하는 방법은 입력 영상에서 특징점 매칭에 영향을 미치는 구조물만 추출하여 새로운 영상을 만들어 특징점을 추출한다. 추출된 특징점은 필터링 (Filtering)을 거쳐 원본 영상에 매핑 (Mapping)되어 설명자를 생성하여 특징점 매칭 속도를 향상시킨다. 또한 구조물 검출 네트워크에서 학습 영상과 시험 영상의 특성의 차이로 생기는 성능 저하 문제를 개선하기 위해 히스토그램 매핑 기법을 이용한다. 아리랑 3 호가 획득한 원격 탐사 영상에 대한 실험을 통해 제안하는 방법은 정확도를 유지하면서 계산 시간을 SURF 보다 87.5%, SIFT 보다 92.6% 감소시킬 수 있다.

• Journal of KIISE:Databases
• /
• v.35 no.5
• /
• pp.379-390
• /
• 2008

Due to the increase of the amount of image data, the demand for searching similar images is continuously increasing. Therefore, many researches about the content-based image retrieval (CBIR) are conducted to search similar images effectively. In CBIR, it uses image contents such as color, shape, and texture for more effective retrieval. However, when we apply CBIR to satellite images which are complex and pose the difficulty in using color information, we can have trouble to get a good retrieval result. Since it is difficult to use color information of satellite images, we need image segmentation to use shape information by separating the shape of an object in a satellite image. However, because satellite images are complex, image segmentation is hard and poor image segmentation results in poor retrieval results. In this paper, we propose a new approach to search similar images without image segmentation for satellite images. To do a similarity search without image segmentation, we define a similarity of an image by considering SIFT keypoint descriptors which doesn't require image segmentation. Experimental results show that the proposed approach more effectively searches similar satellite images which are complex and pose the difficulty in using color information.

• Journal of Internet Computing and Services
• /
• v.16 no.3
• /
• pp.21-32
• /
• 2015

Since spatio-temporal local features for video representation have become an important issue of modeless bottom-up approaches in action recognition, various methods for feature extraction and description have been proposed in many papers. In particular, BoF(bag of features) has been promised coherent recognition results. The most important part for BoF is how to represent dynamic information of actions in videos. Most of existing BoF methods consider the video as a spatio-temporal volume and describe neighboring 3D interest points as complex volumetric patches. To simplify these complex 3D methods, this paper proposes a novel method that builds BoF representation as a way to learn 2D interest points directly from video data. The basic idea of proposed method is to gather feature points not only from 2D xy spatial planes of traditional frames, but from the 2D time axis called spatio-temporal frame as well. Such spatial-temporal features are able to capture dynamic information from the action videos and are well-suited to recognize human actions without need of 3D extensions for the feature descriptors. The spatio-temporal BoF approach using SIFT and SURF feature descriptors obtains good recognition rates on a well-known actions recognition dataset. Compared with more sophisticated scheme of 3D based HoG/HoF descriptors, proposed method is easier to compute and simpler to understand.

• Journal of Broadcast Engineering
• /
• v.19 no.3
• /
• pp.396-404
• /
• 2014

The field of searching clothing, which is very difficult due to the nature of the informal sector, has been in an effort to reduce the recognition error and computational complexity. However, there is no concrete examples of the whole progress of learning and recognizing for cloth, and the related technologies are still showing many limitations. In this paper, the whole process including identifying both the person and cloth in an image and analyzing both its color and texture pattern is specifically shown for classification. Especially, deformable search descriptor, LBPROT_35 is proposed for identifying the pattern of clothing. The proposed method is scale and rotation invariant, so we can obtain even higher detection rate even though the scale and angle of the image changes. In addition, the color classifier with the color space quantization is proposed not to loose color similarity. In simulation, we build database by training a total of 810 images from the clothing images on the internet, and test some of them. As a result, the proposed method shows a good performance as it has 94.4% matching rate while the former Dense-SIFT method has 63.9%.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.11
• /
• pp.2271-2283
• /
• 2015

In this paper, we propose a method which infers an accurate disparity map for radiometrically varying stereo images. For this end, firstly, we transform the input color images to the log-chromaticity color space from which a linear relationship can be established during constructing a joint pdf between input stereo images. Based on this linear property, we present a new stereo matching cost by combining weighted mutual information and the SIFT (Scale Invariant Feature Transform) descriptor with segment-based plane-fitting constraints to robustly find correspondences for stereo image pairs which undergo radiometric variations. Experimental results show that our method outperforms previous methods and produces accurate disparity maps even for stereo images with severe radiometric differences.

• Journal of Broadcast Engineering
• /
• v.22 no.1
• /
• pp.51-61
• /
• 2017

This paper proposes a method to generate panoramic images by combining conventional feature extraction algorithms (e.g., SIFT, SURF, MPEG-7 CDVS) with sensed data from an inertia sensor to enhance the stitching results. The challenge of image stitching increases when the images are taken from two different mobile phones with no posture calibration. Using inertia sensor data obtained by the mobile phone, images with different yaw angles, pitch angles, roll angles are preprocessed and adjusted before performing stitching process. Performance of stitching (e.g., feature extraction time, inlier point numbers, stitching accuracy) between conventional feature extraction algorithms is reported along with the stitching performance with/without using the inertia sensor data.

• The Journal of Korea Robotics Society
• /
• v.1 no.1
• /
• pp.25-35
• /
• 2006

Improving practicality of SLAM requires various sensors to be fused effectively in order to cope with uncertainty induced from both environment and sensors. In this case, combining sonar and vision sensors possesses numerous advantages of economical efficiency and complementary cooperation. Especially, it can remedy false data association and divergence problem of sonar sensors, and overcome low frequency SLAM update caused by computational burden and weakness in illumination changes of vision sensors. In this paper, we propose a SLAM method to join sonar sensors and stereo camera together. It consists of two schemes, extracting robust point and line features from sonar data and recognizing planar visual objects using multi-scale Harris corner detector and its SIFT descriptor from pre-constructed object database. And fusing sonar features and visual objects through EKF-SLAM can give correct data association via object recognition and high frequency update via sonar features. As a result, it can increase robustness and accuracy of SLAM in indoor environment. The performance of the proposed algorithm was verified by experiments in home -like environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.892-911
• /
• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.