• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1690-1704
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• 2013

An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.401-404
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• 2012

기존 얼굴 인식 알고리즘은 단일 특징 기반의 전역적 방식이었다. 정확도를 향상시키기 위해 복수의 특징점을 이용하는 방법들이 제안되었으나 이는 알고리즘의 복잡도가 증가하고, 계산 속도가 느린 단점이 있다. 본 논문에서는 대표적인 특징점 추출 알고리즘인 SURF (Speeded Up Robust Features)를 이용한 얼굴 인식 방법을 제안한다. SURF 를 통해 기술어를 추출하고, Gabor 특징과 LBP 특징을 이용해 해당 특징점에서 기술어를 추출함으로써 기존 알고리즘보다 경량화할 수 있고, 수행시간을 줄일 수 있다. 잘 알려진 ORL 데이터베이스에서의 실험에서 제안한 방법이 정합시간을 포함한 수행 시간에서 약 16%의 감소를 보였고, 정확도 또한 약 34% 향상되었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.19-22
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• 2011

본 논문에서는 대표적인 특징점 추출 알고리즘인 SURF (Speeded Up Robust Features)와 얼굴인식에서 널리 쓰이는 Gabor 기술어를 이용한 얼굴 인식 방법을 소개한다. SURF 기반 영상인식 방법은 특징점을 찾고 해당 특징점에서 기술어를 추출한 후, 정합을 수행한다. 본 논문에서는 SURF 를 통해 추출한 특징점에서 Gabor 웨이블릿 변환을 사용해 기술어를 추출하는 얼굴인식 방법을 제안한다. 잘 알려진 ORL 데이터베이스에서의 실험에서 제안한 방법이 기존 SURF 기반의 얼굴 인식 방법에 비해 더 높은 얼굴 인식 성능을 보여줄 뿐 아니라 정합시간을 포함한 처리 속도면에서도 더 우수한 성능을 보였다. 이러한 실험 결과를 통하여 제안하는 방법이 SURF 보다 얼굴 인식에 적합함을 확인할 수 있었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.49-52
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• 2014

본 논문에서는 비문(鼻紋)을 이용한 개의 개체인식 시스템을 제안하고자 한다. 기존의 비문을 기반으로 한신원 확인 시스템에서는 종이에 비문을 찍어내어 일반화(generalization)된 데이터를 만드는 과정을 거치거나, 기계학습을 위해 한 개체에 대한 여러 장의 사진을 요구하는 문제점을 가지고 있다. 본 논문에서는 한 개체에 대한 두 장의 사진과 SURF(Speeded-Up Robust Features) 알고리듬을 이용한 특징점 추출(feature detection), FREAK(Fast Retina Keypoint) 특징 기술자(feature descriptor)를 사용한 개체인식 시스템을 제안한다. 비문 이미지에는 개 코의 특성상 반사로 인한 다수의 노이즈가 생기게 되는데 이를 극복하기 위한 전처리 과정이 제안 알고리듬에 포함되어 있다. 실험결과 두 장의 사진으로도 비문 기반의 개체인식을 할 수 있다는 것을 알 수 있다.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.49-55
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• 2022

Hair styling has a significant influence on human social perception. An increasing number of people are learning hair styling and obtaining hair designer licenses. However, it takes a considerable amount of money and time to learn professional hairstyle and beauty techniques for hair styling. Since COVID-19, there has been a growing need for offline and video lectures due to the decline in onsite training opportunities. This study provides a field practice environment in which real hair beauty is performed in a virtual space. Further, the hairstyle that is most similar to the user's hair taken with a webcam or mobile phone is determined through an image matching system using the speeded up robust features (SURF) method. The matching hairstyle was created into a three-dimensional (3D) hair model. The created 3D hair model uses a head-mounted display (HMD) and a controller that enables finger tracking through mapping to reproduce the haircutting scissors' motion while providing a feeling of real hair beauty.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.143-150
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• 2010

Markerless augmented reality system must have the capability to recognize and match natural objects both in indoor and outdoor environment. In this paper, a novel approach is proposed for extracting features and recognizing natural objects using visual descriptors and codebooks. Since the augmented reality applications are sensitive to speed of operation and real time performance, our work mainly focused on recognition of multi-class natural objects and reduce the computing time for classification and feature extraction. SIFT(scale invariant feature transforms) and SURF(speeded up robust feature) are used to extract features from natural objects during training and testing, and their performance is compared. Then we form visual codebook from the high dimensional feature vectors using clustering algorithm and recognize the objects using naive Bayes classifier.

• Journal of the Semiconductor & Display Technology
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• v.15 no.4
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• pp.97-102
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• 2016

Image stitching is a process of combining two or more images with overlapping area to create a panorama of input images, which is considered as an active research area in computer vision, especially in the field of augmented reality with 360 degree images. Image stitching techniques can be categorized into two general approaches: direct and feature based techniques. Direct techniques compare all the pixel intensities of the images with each other, while feature based approaches aim to determine a relationship between the images through distinct features extracted from the images. This paper proposes a novel image stitching method based on feature pixels with approximated clustering filter. When the features are extracted from input images, we calculate a meaning of the minutiae, and apply an effective feature extraction algorithm to improve the processing time. With the evaluation of the results, the proposed method is corresponding accurate and effective, compared to the previous approaches.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.291-297
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• 2018

In this paper, we present an automatic image object tracking system for Studio cameras on the stage. For object tracking, we use the OpenCV-based Python program using PC, Raspberry Pi 3 and mobile devices. There are many methods of image object tracking such as mean-shift, CAMshift (Continuously Adaptive Mean shift), background modelling using GMM(Gaussian mixture model), template based detection using SURF(Speeded up robust features), CMT(Consensus-based Matching and Tracking) and TLD methods. CAMshift algorithm is very efficient for real-time tracking because of its fast and robust performance. However, in this paper, we implement an image object tracking system for studio cameras based CMT algorithm. This is an optimal image tracking method because of combination of static and adaptive correspondences. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the stage in real time.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.614-623
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• 2011

We propose a real-time tracking algorithm for an augmented reality (AR) system for TV broadcasting. The tracking is initialized by detecting the object with the SURF algorithm. A multi-scale approach is used for the stable real-time camera tracking. Normalized cross correlation (NCC) is used to find the patch correspondences, to cope with the unknown and changing lighting condition. Since a zooming camera is used, the focal length should be estimated online. Experimental results show that the focal length of the camera is properly estimated with the proposed online calibration procedure.

• ETRI Journal
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• v.34 no.5
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• pp.734-742
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• 2012

A novel interaction method for mobile phones using their built-in cameras is presented. By estimating the path connecting the center points of frames captured by the camera phone, objects of interest can be easily extracted and recognized. To estimate the movement of the mobile phone, corners and corresponding Speeded-Up Robust Features descriptors are used to calculate the spatial transformation parameters between the previous and current frames. These parameters are then used to recalculate the locations of the center points in the previous frame into the current frame. The experiment results obtained from real image sequences show that the proposed system is efficient, flexible, and able to provide accurate and stable results.