• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.140-146
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• 2011

In this paper, we propose a fast object classification method based on texture and color information for video surveillance. We take the advantage of local patches by extracting SURF and color histogram from images. SURF gives intensity content information and color information strengthens distinctiveness by providing links to patch content. We achieve the advantages of fast computation of SURF as well as color cues of objects. We use Bag of Word models to generate global descriptors of a region of interest (ROI) or an image using the local features, and Na$\ddot{i}$ve Bayes model for classifying the global descriptor. In this paper, we also investigate discriminative descriptor named Scale Invariant Feature Transform (SIFT). Our experiment result for 4 classes of the objects shows 95.75% of classification rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5624-5638
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• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.4
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• pp.131-143
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• 2012

In this paper, we propose a drowsy driver detection system with a novel eye state detection method that is adaptive to various vehicle environment such as glasses, light and so forth using SURF(Speed Up Robust Feature) which can extract quickly local features from images. Also the performance of eye state detection is improved as individual three eye-state templates of each driver can be made using Bayesian inference. The experimental results under various environment with average 98.1% and 96.1% detection rate in the daytime and at night respectively and those in the opened ZJU database with average 97.8% detection rate show that the proposed method outperforms the current state-of-the-art.

• Journal of the Semiconductor & Display Technology
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• v.14 no.4
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• pp.56-60
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• 2015

Mobile devices like smartphones and tablets are becoming increasingly capable in terms of processing power. Although they are already used in computer vision, no comparable measurement experiments of the popular feature extraction algorithm have been made yet. That is, local feature descriptors are widely used in many computer vision applications, and recently various methods have been proposed. While there are many evaluations have focused on various aspects of local features, matching accuracy, however there are no comparisons considering on speed trade-offs of recent descriptors such as ORB, FAST and BRISK. In this paper, we try to provide a performance evaluation of feature descriptors, and compare their matching precision and speed in KD-Tree setup with efficient computation of Hamming distance. The experimental results show that the recently proposed real valued descriptors such as ORB and FAST outperform state-of-the-art descriptors such SIFT and SURF in both, speed-up efficiency and precision/recall.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.319-322
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• 2013

Image retrieval research activity has moved its focus from global descriptors to local descriptors of feature point such as SIFT. MPEG is Currently working on standardization of effective coding of location and local descriptors of feature point in the context mobile based image search driven application in the name of MPEG-7 CDVS (Compact Descriptor for Visual Search). The extracted feature points consist of two parts, location information and Descriptor. For efficient image retrieval, we proposed a novel method that is gradual block-based efficient lossy location coding to compress location information according to distribution in images. From experimental result, the number of average bits per feature point reduce 5~6% and the accuracy rate keep compared to state of the art TM 3.0.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.144-159
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• 2014

Techniques for making a single panoramic image using multiple pictures are widely studied in many areas such as computer vision, computer graphics, etc. The panorama image can be applied to various fields like virtual reality, robot vision areas which require wide-angled shots as an useful way to overcome the limitations such as picture-angle, resolutions, and internal informations of an image taken from a single camera. It is so much meaningful in a point that a panoramic image usually provides better immersion feeling than a plain image. Although there are many ways to build a panoramic image, most of them are using the way of extracting feature points and matching points of each images for making a single panoramic image. In addition, those methods use the RANSAC(RANdom SAmple Consensus) algorithm with matching points and the Homography matrix to transform the image. The SURF(Speeded Up Robust Features) algorithm which is used in this paper to extract featuring points uses an image's black and white informations and local spatial informations. The SURF is widely being used since it is very much robust at detecting image's size, view-point changes, and additionally, faster than the SIFT(Scale Invariant Features Transform) algorithm. The SURF has a shortcoming of making an error which results in decreasing the RANSAC algorithm's performance speed when extracting image's feature points. As a result, this may increase the CPU usage occupation rate. The error of detecting matching points may role as a critical reason for disqualifying panoramic image's accuracy and lucidity. In this paper, in order to minimize errors of extracting matching points, we used $3{\times}3$ region's RGB pixel values around the matching points' coordinates to perform intermediate filtering process for removing wrong matching points. We have also presented analysis and evaluation results relating to enhanced working speed for producing a panorama image, CPU usage rate, extracted matching points' decreasing rate and accuracy.

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

• 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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• 2013.06a
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• pp.66-67
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• 2013

본 논문에서는 지역적인 그래프를 이용하여 면 외 회전에 강인한 특징 기술 및 정합 방법에 대하여 제안한다. 특징 기반의 지역적인 그래프의 경우 SURF 등을 이용하여 찾아진 특징 정보에 대하여 특징 사이의 상관관계를 이용하여 지역적인 그래프를 생성함으로써 특징 기술의 적용 범위를 확장할 수 있고 이러한 지역적인 그래프의 정합을 위하여 새로운 비용 함수와 정합 방법을 제안한다. 또한 특징 정합 시 이를 활용하여 정합 후보를 결정함으로써 면 외 회전이 존재하는 영상에 대해 좀 더 강인한 특징 정합 결과를 얻을 수 있고 실험 결과 기존의 특징 정합 방법에 비하여 찾아지는 후보의 개수가 증가하고 찾아진 정합 결과의 정확도가 증가하는 것을 확인할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5692-5716
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• 2019

One of the best means to safeguard the confidentiality, security, and privacy of an image within the IoT-Cloud is through encryption. However, looking through encrypted data is a difficult process. Several techniques for searching encrypted data have been devised, but certain security solutions may not be used in IoT-Cloud because such solutions are not lightweight. We propose a lightweight scheme that can perform a content-based search of encrypted images, namely EEIRI. In this scheme, the images are represented using local features. We develop and validate a secure scheme for measuring the Euclidean distance between two descriptor sets. To improve the search efficiency, we employ the k-means clustering technique to construct a searchable tree-based index. Our index construction process ensures the privacy of the stored data and search requests. When compared with more familiar techniques of searching images over plaintexts, EEIRI is considered to be more efficient, demonstrating a higher search cost of 7% and a decrease in search accuracy of 1.7%. Numerous empirical investigations are carried out in relation to real image collections so as to evidence our work.